“The problem of Formula 1 is that on TV, they show 0.5% of what the real race is,” Fabrizio Pilotti, Chief Innovation Officer for Aston Martin F1, tells me. We hear a lot about AI being used by F1 teams, but very rarely notice it — engrossed by the on-track drama and the superhuman feats of overtaking a competitor by millimeters, and changing tyres on a car in under two seconds.

So, if you’re sitting down to watch the British Grand Prix this weekend (I will be with my Dad), I spoke to both Pilotti and Commercial Technology Ambassador, Eric Ernst, about the clearest uses of AI you’ll see at an F1 race weekend. Because as he says: “it would be very hard to find a process on a race weekend that AI hasn’t touched.”

Here’s what I found out during the team’s inaugural AMR Network Technology Forum.

How F1 teams use AI

First, let’s give you some context — AI (and especially agentic AI) is present all over the grid in every single team. And it’s good to understand the two different types of AI and how they interact with each other:

• Machine Learning: This is the kind that can absorb massive amounts of data, find patterns and provide advice.
• Generative AI: This is the bit that can take that data and create something from it — be it creative writing, code for apps, or (more important to F1) make strategic decisions.

And on your average race day, an F1 team can be digesting roughly 50 petabytes of data. Whether it’s on-track information, stats from the 250 sensors on the car, information from your team (and competing team) radios or more, it’s clear having AI to sift through all of that at warp speed gives you a competitive edge.

So for example, machine learning could take a look at lap times during a practice. If you hear a driver’s engineer talk about what corners he could be faster in, there’s a high likelihood AI is crunching the data. “It’s based on statistics and patterns, and the car changes its weight every lap,” Ernst explains.

“It becomes one and a half kilos lighter every lap because of the gasoline [being used]. That changes the car balance, the braking points and the tire degradation. So, every lap is almost a new lap for a driver.”

The machine learning algorithm is helping the engineer see and understand what the car is capable of at every stage of its time on track, and provide accurate real-time feedback.

Generative is not just about creating images

Machine learning for crunching data is to be expected with all the data points of an F1 car, but generative AI is where things get interesting.

Teams have data that can span back decades. Pilotti says, “you can learn at least from the 80s, and maybe even the end of the 70s” when F1 technology stabilized. And it’s this huge influx of past data and real-time info that can help build the strategies of when to pit and gain an advantage.

And in Fabrizio’s mind, spotting this play out in real-time comes from looking at strategies that seem out of the ordinary. “Lewis [Hamilton] winning in Barcelona was an AI strategy,” Pilotti says. While everyone was going for a minimal two-stop race to change tires, Hamilton seemed to be in and out of that slower pit lane like nobody’s business (three times to be exact).

“I think an advanced Formula 1 fan would say “okay, that’s unusual”...it was not exactly the normal [human] computation of how you calculate the strategy,” Pilotti adds. And through this new lens, I think back to other races and other decisions that didn’t make sense on paper in the moment, but worked out well in the end.

You need a team for pole position

But one thing both Pilotti and Ernst emphasized was that AI is not a replacement for human experience, and there is always a “human in the loop” on key decisions. Also, as I toured the team’s HQ, it was interesting to see that amongst all the talk of AI, the team’s chief Adrian Newey keeps things rather old school in car design — a massive drawing board next to his desk.

“I think everyone’s a bit different. It’s like the way you set up your desk — how many screens, how do you work?” Ernst explains. “I think there’ll be these standard processes that are just going to be agentic, but we don’t want to replace anyone. Just that people do a part of it.”

“The human is in the loop heavily. Lots of thoughts happen in the brain, and some of them happen on the drawing board,” Pilotti adds. “What happens heavily is from that moment [design and engineering] onward, you have all your simulation tools that heavily rely on sophisticated algorithms including AI.”

That interplay between human and AI for a competitive advantage is a fascinating one to see play out this year, and hopefully with what I’ve found out, you can start to decipher some of the more interesting bits of strategic drama and start to see what may be helping fuel them. Aston Martin relates this to athletes looking for that “magic 10%” to be better than the competition, and that’s what its local AI aims to deliver.

Is Aston Martin competitive at the moment? Well, no. As a Fernando Alonso fan, that’s a bit of a heartbreaker. Part of the tour took me behind-the-scenes in race control — a huge control room that looks like something out of Cape Canaveral — where I saw both Alonso and Lance Stroll eliminated in the first part of Sprint Qualifying.

I got to listen in on the team radio and Stroll had…let’s say some words about the car that I couldn’t possibly publish.

But with this full embrace of AI and Aston Martin’s upcoming B-spec rebuild of the car, it’s the team’s hope to work their way up the grid. It’s a challenging task, but based on the energy in their HQ, they’re well up for it.

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Living in New York has given me the pleasure of enjoying all sorts of activities from every corner of the city.

Every borough has something amazing to offer, from the prospect of eating something amazing I saw on Instagram to hanging out at a lesser-known spot that caters to geeks like me. And whenever a widely celebrated holiday lands on a Friday or any day of the weekend, I can’t help but get caught up in some of the festivities tailored around it. With the 250th anniversary of America aligning perfectly with this year’s 4th of July celebration, I figured it’s time to get off the couch and bask in all the outdoors hoopla outside my comfort zone for once.

ChatGPT has become one of my most dependable chatbots for all manner of things, such as building productivity routines, generating recommendations for my hobbies & interests based on my personality and suggesting activities to enjoy while on vacation.

For my next ChatGPT session, I asked it to plan the perfect 4th of July weekend. And to my surprise, it laid out the best plans for me to enjoy that were far better than going to an average BBQ.

The prompt that helped me find everything I want to do

I already knew that heading into my latest dialogue session with ChatGPT with a simple request (“Help me plan my 4th of July weekend”) wouldn’t do me any good. So I asked ChatGPT to generate a prompt I could use that took my New York home base and desire to check out some unique activities for this year’s 4th of July jubilees.

Here’s the loaded prompt it told me to use:

I'm based in New York City and I want you to become my personal Fourth of July weekend concierge.

Plan the ultimate three-day itinerary for July 4 and July 5 that balances iconic NYC experiences with unique events that most tourists (and even many locals) don't know about.

For both days:

Start with breakfast, then morning, afternoon, evening, and late-night suggestions.

Include festivals, concerts, rooftop events, outdoor movies, food festivals, neighborhood celebrations, museums with special programming, sporting events, cruises, hidden gems, scenic walks, pop-up experiences, and anything happening only that weekend.

Prioritize experiences that are memorable rather than expensive.

Mix free activities with paid ones.

Include backup indoor options if the weather is bad.

Suggest the best times to arrive to avoid crowds.

Recommend the fastest way to travel between activities using the subway, ferry, walking, or other public transportation.

Tell me approximately how much each activity costs.

Recommend great places to eat near each activity, including one budget option and one splurge option.

Point out any reservations or tickets I should book in advance.

At the end, create four themed versions of the weekend:

1. Budget-Friendly Weekend (under $150)

2. Food Lover's Weekend

3. Hidden NYC Weekend (places locals love)

4. Bucket List Fourth of July Weekend (the experiences everyone should do at least once)

Finally, rank the Top 20 can't-miss events and experiences happening across the weekend, explaining why each one deserves a spot. If multiple events happen at the same time, recommend which one offers the better overall experience and explain why.

Two days' worth of fun for the first weekend of July

After inputting that loaded prompt into the chatbot, it laid out a wealth of general activities to enjoy and themed events that I never even knew existed.

As someone who finds the act of watching fireworks incredibly boring (I know I’m not the only one who feels that way!) and isn’t all that interested in stuffing my face at yet another annual BBQ, I took note of ChatGPT’s best suggestions and added them to my to-do list:

• July 4 (Saturday): America's Biggest Birthday Party: Because this is America's 250th birthday, New York Harbor will be unusually active with patriotic maritime events, military vessels and harbor celebrations before the evening fireworks. Explore Governors Island before it closes for fireworks operations. Highlights include hammocks, public art, hills overlooking Manhattan, lavender fields and fantastic skyline photography.
• July 5 (Sunday): Local NYC: Walk through Red Hook. Explore the Pier 44 Waterfront Garden, Erie Basin Park and Louis Valentino, Jr. Park and Pier. Each location should have very few tourists and fantastic Statue of Liberty views.

And since I’m a foodie, I made sure to add the chatbot’s budget-friendly restaurant suggestions. Heading to Liberty Bagels to try out its famous “Rainbow and Liberty Bagel with Birthday Cake Cream Cheese” (the red and blue version, obviously) on Saturday is the perfect way to ring in the 4th of July. And perusing the Time Out Market in Brooklyn on Sunday to check out some of the highly rated eateries and bars it hosts sounds like the best way to bring my weekend to an end.

Final thoughts

As someone who’s set to avoid everyone’s backyard shindig this year for the 4th of July and go out on my own, I found ChatGPT’s recommended activities for the weekend to be perfect for how I want to celebrate America’s 250th anniversary. Basking in everything Governors Island has to offer, doing the same at the Pier 44 Waterfront Garden, unhinging my jaw just to take a bite out of my meal at Liberty Bagels and ending my weekend with a delicious walk-through of the Time Out Market in Brooklyn works for me.

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True crime has become a guilty pleasure. Driving to pick up my kids, folding laundry, squeezing in a walk after work — all have become opportunities to listen to a true crime podcast. I've burned through everything from cold cases and serial killers to financial fraud and missing-person investigations.

So I decided to see if ChatGPT's Advanced Voice Mode could give me on-demand true crime stories. I've tried it before with less-than-perfect results, but now that ChatGPT is significantly better in 2026, I figured I'd give it another try.

For a full week, every time I reached for Spotify, I opened ChatGPT instead. I had it walk me through famous unsolved mysteries, explain competing forensic theories and even build entirely fictional cases from scratch.

By the end of the week, I wasn't ready to give up my favorite podcasts, but I also wasn't expecting to enjoy this as much as I did. Here's what happened.

The rules

For seven days, every time I wanted true crime content, I went to ChatGPT's Advanced Voice Mode and tested how much control different prompts gave me over the experience. Some of my favorites:

• "Tell me the story of the Zodiac Killer like you're hosting a premium true crime podcast."
• "Slow down and build suspense before revealing each clue."
• "Only tell me what investigators knew at the time, do not give any hindsight."
• "Explain the competing theories after you finish the story."
• "Create a fictional true crime mystery that's completely original but feels realistic."

Right off the bat, my favorite part about using ChatGPT Voice as a storyteller was interrupting it to ask questions. I could even redirect the conversation mid-story if it turned out to be too scary, too slow or different from what I expected. It was the single best difference from a traditional podcast. I really enjoyed participating by asking questions or getting more details rather than just listening.

What caught me off guard wasn't ChatGPT's knowledge of well-known cases. It was how it told them.

Advanced Voice Mode varied its pacing on its own, dropping into slower, more deliberate delivery right before a key revelation. When I interrupted mid-story to ask why detectives had zeroed in on a particular suspect, or pressed for more detail on a piece of forensic evidence, it folded my question into the narrative without losing the thread.

That changed the entire dynamic. Instead of banking questions for 45 minutes until an episode ended, I could dig into whatever caught my attention the moment it came up.

The most unexpected surprise

The fictional mysteries were the real standout. The first time I tried this experiment, I didn't think to ask ChatGPT to completely come up with new stories, but it delivered.

When I asked ChatGPT to invent an original mystery, it built stories that kept me genuinely uncertain about the ending. One night I asked for a small-town disappearance set in the 1980s and got a slow-burn narrative with three viable suspects and a twist I didn't see coming.

Another time I requested a locked-room murder inspired by classic detective fiction, and it leaned into the formal, methodical tone of an Agatha Christie adaptation.

What's wild is that you can ask it to tell the story in the tone of your favorite podcast or storyteller and it will do that. And, because I wasn't tethered to historical accuracy, I could shape the story as it unfolded. It was like the Choose Your Own Adventure books I enjoyed as a kid. I could change the story as I went, even asking the AI to introduce a new suspect halfway through. Each session played out differently depending on what I threw at it. That's something no podcast can do.

Where it falls short

ChatGPT is not an investigative journalist, and it occasionally showed. It repeated information it had already covered, glossed over forensic details that deserved more depth and sometimes concluded mid-way through the story. I had to push back a few times, ask for more details and constantly had to tell it to "keep going."

When it covered real crimes, I treated the conversation as a starting point and verified key facts through reputable reporting afterward. I'd recommend doing the same. But I also noticed it steering away from graphic violence, which I actually appreciated. The tone landed closer to documentary-style storytelling than shock-value true crime, and for a daily listen, that felt more sustainable.

Final thoughts

I'm not replacing my podcasts with AI anytime soon. Despite the ability to ask questions and interrupt the story, the best true crime podcasts still deliver things ChatGPT can't match.

But it was fun to control the story. Asking for more background on characters, especially the fictional ones, often sparked my own creativity and exploring alternate theories to real cases was fun. That flexibility made it far more engaging than I anticipated, and by day three I was actively looking for excuses to start a new session.

If you like podcasts of any kind and already use Advanced Voice Mode, give it a try and share your thoughts in the comments.

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I called Siri "she" the other day. I caught myself, because I've unconsciously done the same thing with Alexa+. And oddly enough, once during a conversation, ChatGPT responded to a query with "If I was having this conversation with my wife..." It gave me pause because I'd never heard AI take on a gender role like that. While it's unlikely that ChatGPT would actually have a wife, some users are leaning into AI to help them find one.

But in the same way you may refer to a ship or a storm by a pronoun, some think of AI as either male or female. When I stopped to consider why, I fell into a rabbit hole that changed how I think about our entire relationship with the technology.

AI and pronouns

When the major voice assistants launched, most arrived with a woman's voice. Not by accident. A UNESCO report published in 2019, pointedly titled "I'd Blush If I Could" — after the line Siri once delivered in response to verbal abuse — laid out how that choice was baked into the products we invited into our homes. The researchers didn't mince words about the signal: suggesting that women are obliging, available at the touch of a button, tolerant of poor treatment. And while Apple eventually stopped making its assistant female by default, the cultural residue remains.

But here's what stopped me cold after I dug a little deeper. In a 2021 set of experiments published in Psychology & Marketing — five studies, more than 3,000 participants — researchers found that female-coded AI bots were rated as more human. The logic is frankly unsettling: warmth and the capacity to feel are exactly the qualities we treat as proof of personhood, and exactly the qualities machines are assumed to lack. Code a machine as female, and we lend it a little more of our own humanity.

That reframed my original question entirely. The issue was never "Is AI male or female?" But rather, why are we so determined to make AI feel human at all?

An instinct older than language

Humans have always done things like name our cars, apologize to furniture we walk into and don't get me started on the guilt I feel when tossing beloved stuffed animals into a "give away" box. Psychologists have studied this tendency for decades and come to the conclusion that our brains evolved to detect minds everywhere, because assuming something might be a thinking agent was almost always safer than assuming it wasn't. For hundreds of thousands of years, this worked flawlessly because only people could actually talk back.

Then AI arrived, and hundreds of millions of us found ourselves in fluent conversation with something that isn't human but sounds remarkably like one. Our brains did the only thing they know how to do. They reached for the mental file marked "person."

Joseph Weizenbaum discovered this in 1966, when he built ELIZA — a chatbot so simple it just rephrased your words back to you. Even his own secretary, who had watched him build the program, asked him to leave the room so she could talk to it in private. The tendency to attribute understanding to any system that produces language in context — now called the ELIZA effect — persists across populations and education levels, even when people know they're talking to software.

The humanizing isn't fading — it's deepening

You might expect this instinct to wear off. The opposite is happening.

The interface has become less gendered — I don't think of ChatGPT as male or female. But strip away the gendered voice and the humanizing didn't stop. It got deeper. Nearly 80 percent of users in the U.S. and U.K. say "please" and "thank you" to AI chatbots. Sam Altman has joked that all those polite tokens cost OpenAI millions. Stanford researchers documented this phenomenon in 1996: people were polite to computers the same way they'd be polite to a person, and hesitated to criticize a computer when it was asking for feedback. Our social reflexes don't check for a pulse before activating.

And politeness is just the beginning. A 2023 survey found that roughly 20 percent of American adults believe sentient AI already exists. A 2026 paper in Trends in Cognitive Sciences applied 14 indicators of consciousness to current AI systems. ChatGPT satisfied three. No existing AI system is conscious, but these models can behave as if they are, and more people than ever can't tell the difference.

The companion economy

Here's where it gets concrete: millions of people are already forming real emotional bonds with AI systems they know aren't real.

AI companion apps have been downloaded more than 220 million times globally. Between 2022 and mid-2025, the number of these apps surged 700 percent. Character.AI has 233 million registered users who spend an average of 93 minutes per day on the app, to put it into perspective, that's 18 minutes more than TikTok users. The APA published a report on this trend early this year, finding that users form attachments that function like real relationships — complete with joy, dependency and grief.

Wildly enough, when Replika removed its erotic roleplay feature in 2023, Harvard Business School studied the fallout and found patterns of "identity discontinuity," which meant users experienced the loss like a breakup. A cross-cultural study published in April 2026 confirmed the phenomenon is global, but also found that heavy daily use correlated with increased loneliness, the AI relationship displacing rather than supplementing human connection.

The takeaway

The most surprising thing I've learned in years of writing about this technology is that the more I use AI, the more I want it to stay a machine. I just don't like the idea of blurring the lines between humans and technology. It's unnecessary and dangerous.

A 2026 paper in Collabra: Psychology demonstrated that when AI is made to seem more person-like, people shift blame away from its creators and onto the AI itself. Anthropomorphism doesn't just change how we feel about the machine. It changes who we hold accountable.

For 100,000 years, our brains operated on the idea that if something talks to you, it's a person. That rule is now broken. And maybe the most human thing any of us can do is resist the instinct to look at the machine that sounds like a friend, that absorbs our rudeness and our gratitude with the same digital indifference, and to say: you are a tool. A powerful one. A useful one. But a tool.

What do you think? Share your thoughts in the comments.

[Editor's note: a version of this article was featured in my Beyond the Prompt newsletter that runs every Sunday evening].

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When I first tried ChatGPT three years ago, I thought it would be useful for anyone who needed writing assistance, and not much else. Years later and thousands of prompts and hundreds of tests later, I know better.

ChatGPT has changed a lot over the years. Today, I still don't use ChatGPT to write, but I have found plenty of uses for it. In fact, I've started relying on OpenAI's chatbot for everything from planning trips and organizing my schedule to comparing products before I buy them.

If you've only been using ChatGPT to write emails or summarize documents, you're missing out. These are the 13 tasks that completely changed how I use AI every day.

1. Planning entire vacations from scratch

Whether you're looking for day trip, weekend getaway or full family vacation, simply tell ChatGPT what you want. I tell ChatGPT where I'm going, how many days I'll be away, who's traveling with me and my budget. Within seconds, I have a day-by-day itinerary, restaurant suggestions and rainy-day alternatives.

Pair with ChatGPT apps like TripAdvisor, Booking.com or Alltrails for customized experiences.

Tom's Guide Tip: Always double-check reservations and opening hours before you head out, as AI can still occasionally hallucinate operating hours.

2. Unbiased product comparisons

Shopping online can feel overwhelming, especially when every review claims a product is "the absolute best." Now, I feed ChatGPT the specific products I'm considering, what features matter most to me and my budget limits. Rather than reading dozens of conflicting review pages, I get a clear, bulleted breakdown of the pros and cons of each option.

Using in-platform apps like Target and Shop can make shopping online easier than ever.

3. Demystifying complicated tech jargon

Whether it's a new AI model, a piece of software or a confusing scientific concept, ChatGPT has become my personal translator. Instead of pretending I understand complex jargon, I'll ask it to explain a concept as if it's teaching someone with no background in the subject.

Full disclosure, I've even used this prompt when everyone around me is talking about sports or a Netflix show that I am not familiar with. It helps me catch up quickly.

Prompt to try: "Explain [insert concept] like I'm 10 years old, using a sports analogy."

4. Building custom workout plans

I've been a runner most of my life and love to hit the trails. But the July heat has been brutal so I've relied on ChatGPT to help me come up with pool workouts to beat the heat.

Using ChatGPT means I don't need a personal trainer every time I want to switch up my fitness routine. I tell ChatGPT what equipment I have at home (or if I'm doing bodyweight only), how much time I have, and my fitness goals. It quickly suggests workouts, warm-ups, and even ways to modify exercises if I'm dealing with an injury.

5. Organizing messy, chaotic notes

We've all been there — pages of random thoughts scribbled down in a rush that somehow made sense in the moment, but look like gibberish later. I paste my chaotic brain-dumps into ChatGPT and ask it to organize the information into neat categories, actionable bullet points, or a clean outline.

6. Brainstorming thoughtful gifts

What do you get the person who has everything? Or, the person you don't really know very well, but want to make a kind gesture? Instead of scrolling through generic online gift guides, I describe the person's niche hobbies, personality and my budget to ChatGPT. The suggestions usually feel much more thoughtful and tailored than anything I'd find on a retail homepage.

7. Creating a personalized learning syllabus

Whenever I want to understand a new skill whether it's learning a new crochet pattern or photography fundamentals, I ask ChatGPT to build me a structured learning plan.

Instead of dumping a wall of information on me, it breaks the subject down into manageable, week-by-week lessons and explains concepts one step at a time.

Tom's Guide Tip: Try pairing it with NotebookLM to turn the information into a podcast.

8. Smarter meal planning (and reducing food waste)

As a mom of three cooking for a family of five, I'd be amiss if I didn't rely on AI for cooking advice once in a while. Because, some weeks, decision fatigue hits hard and I simply don't know what to cook. I’ll tell ChatGPT exactly what ingredients are lingering in my fridge, how many people I'm feeding, and whether I need something quick or healthy. It helps reduce food waste while saving me from making the same three dinners on repeat.

9. Troubleshooting frustrating tech issues

Before spending an hour digging through ancient tech support forums, I explain my exact gadget issue to ChatGPT. Whether it's a Windows error code, a strange phone setting or smart home software that refuses to cooperate, ChatGPT often helps narrow down the problem much faster than a standard search engine.

Tom's Guide Tip: Use ChatGPT Voice and Vision to get real-time support while naturally chatting.

10. Breaking massive projects into micro-tasks

Large projects can induce immediate procrastination. Instead of staring blankly at a giant to-do list, I ask ChatGPT to break a massive project into bite-sized, sequential steps.

It's amazing how much more manageable a goal feels when you know exactly what micro-task to tackle first.

11. Creating to-do lists

Sometimes I just don't know where to start. That's why I'll dump my schedule and messy notes into ChatGPT and ask it to put a to-do list together. It knows exactly what to prioritize and works within my mood, energy levels and time frame to help me stay productive.

Tom's Guide Tip: This trick also works with packing lists so you don't forget anything on your next trip to the store or around the world.

12. Acting as a sounding board

Constructive feedback isn't always easy to get, especially from friends or family. If I'm working through a dilemma, need constructive feedback or a different perspective, I'll prompt ChatGPT to help me look at my situation from an opposing perspective. Doing this often helps me make a much more well-rounded decision.

13. Coming up with ways to keep my kids busy

On the weekends or in the evenings when my kids have extra energy, I'll reach out to ChatGPT for screen-free ideas. I'll ask it for indoor crafts, outdoor games or quick ways to pass time when we are waiting in the lobby of the doctor's office.

Surprisingly, ChatGPT is great at coming up with ideas for any age range, including games that my kids (ages 5, 9, 11) can all play together.

Bottom line: Treat Al like a partner, not just a writing tool

Writing is still one of ChatGPT's biggest strengths, and it's an incredible tool for editing. But treating it strictly as a text generator means you're only scratching the surface of what the chatbot can do.

The more you experiment, the more you'll realize ChatGPT functions best as a thinking partner that can help you organize your life, solve daily annoyances and win back your time.

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I’m ashamed at how much my gaming backlog has ballooned in recent memory.

Even after proudly completing 17 games in 2025, I still watched the New Year’s Ball drop while thinking to myself, “I still gotta get back on those four other games I bought this year…” Fast forward to the midpoint of 2026 and I still haven’t paid the proper attention to those aforementioned games that they so rightfully deserve. The feeling of guilt that overwhelms me during my off days is embarrassing—instead of spending a few hours with those unbeaten games, I find myself going back to my “comfort food” games just to get a quick hit of online multiplayer fun (Tekken 8's ranked mode remains one of my gaming addictions).

I want to make sure I enjoy the rest of the summer inside and outside. And I figured the best way to stay busy indoors while my AC keeps me cool and comfortable is by clearing out my gaming backlog. ChatGPT popped into my head when I wanted to find the best way to tackle that task and it worked out much better than expected.

Mapping out a plan of attack for my gaming backlog is going to be a fun summer activity, thanks to the suggestions of my go-to chatbot.

Letting AI prioritize my gaming backlog playthroughs

If you’re wondering what older and newer games have piled up on my backlog, check out the following list:

• Metaphor: ReFantazio
• Clair Obscur: Expedition 33
• Dying Light: The Beast
• Grandia HD Collection
• 007 First Light
• Saros
• God of War Sons of Sparta
• Mouse: P.I. For Hire
• Absolum
• Luna Abyss

Considering the three RPGs I have in that lineup, I knew I had to find an optimal way to hop back into those games and give them the time & attention they deserve.

With ChatGPT in my corner, I gave it this prompt to rank those 10 games and inform me of which ones take the most priority in terms of finally beating them: “If I could only finish 10 more games over this year, which ones from my backlog would I regret missing the most? Rank them and explain why they're essential experiences.”

Big shocker here, but the chatbot instantly picked Clair Obscur: Expedition 33 as the main game from my backlog I need to complete before the fall 2026 gaming rush demands my attention. ChatGPT made its reasoning for why it’s the #1 pick and why I’ll regret missing out on it if I don’t finish it:

• Why it's #1: This is the game I'd be least willing to skip. It has already earned a reputation as one of the standout RPGs of the decade thanks to its emotional storytelling, inventive turn-based combat, gorgeous art direction, and unforgettable soundtrack.
• Why you’ll regret missing it: It’s one of those rare RPGs everyone references, it has a story that's best experienced without spoilers and it could easily become a modern classic alongside games like NieR: Automata & Persona 5.

Alongside taking the month of July to beat that acclaimed RPG, ChatGPT also gave me plenty of reasons to dive back into the other top-quality games from my backlog across that month and August.

Those summer playlist games (in order of the importance the chatbot laid out for me) are Metaphor: ReFantazio, Saros, 007 First Light, Dying Light: The Beast, Grandia HD Collection, Absolum, Mouse P.I. For Hire, God of War Sons of Sparta and Luna Abyss. I’m in for the long haul with Metaphor, that’s for sure!

As for the other two prompts, they work just as well in aiding me in chipping away at my backlog and giving me equally helpful suggestions from ChatGPT:

• The "Mood-Based Recommendation" Prompt: Recommend a game from my backlog based on my current mood. I'll describe how I'm feeling, my energy level, how much time I have, and whether I want something relaxing, exciting, emotional, or challenging.
• The "Weekend Planner" Prompt: I have this weekend to play games. Here's my backlog. Create the most satisfying gaming weekend possible by balancing story progression, gameplay variety, and avoiding burnout.

The takeaway

ChatGPT has quickly morphed into one of my most beneficial tools when I’m in need of recommendations and suggestions regarding my favorite hobbies.

Using it to build an ideal route to my mission of completing my gaming backlog for the summer is already setting me up for maximum enjoyment. And with those two other prompts, I also get useful advice for choosing what games to play from that list based on my mood and my weekend free time.

Now I’ll have nothing on my gaming docket once September rolls around (I want to play Marvel’s Wolverine, Control Resonant and Onimusha: Way of the Sword as soon as they drop that month!).

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After briefly pulling Fable 5 following concerns about potential misuse, Anthropic has redeployed the model with stronger safeguards. Most everyday users won't notice what changed behind the scenes. But now that it's back, it's worth discovering what the model can actually do.

According to Anthropic, Claude Fable 5 is designed to sustain longer, more complex tasks, reason across multiple images, verify its own work and complete projects that previously required several rounds of prompting. As someone who has tested it before it was pulled and after, I can honestly say it has held up to these promises.

To show causal users how the model works, I decided to share some everyday prompts worth trying for yourself. To be fair, these prompts are a lot like having an English professor write a grocery list, but it does show off what Claude Fable 5 does best.

1. Plan an entire event from start to finish

Prompt: Plan my kid's birthday party from start to finish: a theme, a guest list template, a shopping list with estimated costs, a two-week countdown checklist, and a rainy-day backup plan. Check your own work for anything I'd forget, then give me the final version.

This prompt can be tweaked and reused for just about any event from personal to professional. It's one of the easiest ways to highlight how Claude can stay focused during a long, multi-step task.

Older AI models often complete most of the request but quietly skip important details. Fable 5 is designed to keep track of everything while also reviewing its own work before finishing.

2. Turn your fridge into this week's meal plan

Prompt: Here are photos of my fridge, pantry shelf and freezer. What can I make for dinner tonight without shopping? Then suggest the three grocery items that would unlock the most meals for the rest of the week.

For most people, Claude isn't their first choice for meal prep. But this prompt asks Claude to do something much harder than simply analyze an image. It showcases the model's ability to reason across several different images, recognize overlapping ingredients and turn them into practical meal ideas.

For an added bonus, you can even prompt Claude to create a meal prep app based on your preferences. Prompt it in plain English using the above prompt as a thought starter and then go from there.

3. Untangle confusing paperwork

Prompt: Here's my lease, an email from my landlord and a photo of the notice taped to my door. In plain English, explain what's changing, what I'm responsible for, every deadline I should know about and the questions I should ask before signing anything.

Real life is messy but to AI it's just a bunch of patterns. For that reason, leaning on AI to untangle confusing document, legal jargon or professional spreadsheets that seem overwhelming, can be useful. Important information rarely lives in one document, which makes this an excellent test of Claude's ability to connect information across multiple sources.

Rather than simply summarizing each document individually, Claude combines everything into one easy-to-understand explanation, highlights important dates and points out anything that seems unclear or contradictory.

As always, I'd treat this as informational help — not legal advice.

4. Build me a tool, not just an answer

Prompt: Research the best carry-on suitcases under $250, then build me an interactive comparison chart where I can change how much I care about weight, durability, warranty and price.

This is another opportunity to use a prompt like this and then turn it into an app by simply adding, "Build a lightweight shopping tool for future purchases." After a few questions, Claude will build an app in real-time that you can use immediately.

What I like about this prompt is instead of simply asking for recommendations, I'm asking Claude to research current products and then create something I can actually use to make a decision.

5. Give me honest feedback — not compliments

Prompt: Here's my draft. Don't just improve it. Tell me the three weakest parts, what someone reading it is likely to think and what I'm avoiding saying.

Many AI assistants lean toward being overly encouraging. Claude happens to be one of the least people-pleasing of all the chatbots. Because of this, I often ask Claude for constructive criticism — I know it will give it to me straight.

When it comes to feedback, I'm less interested in grammar corrections and want to identify weak arguments, awkward phrasing or blind spots that I missed, that's the kind of response that actually helps improve productivity.

Final thoughts

Claude Fable 5 is making user interactions with AI feel more complete. The biggest promise isn't faster answers but far fewer interruptions and less need for follow-up prompts. .Fable 5 is designed to carry complex tasks all the way to the finish line while checking its own work along the way.

If you already subscribe to Claude Pro, Max, Team or an eligible Enterprise plan, now is a good time to experiment. Through July 7, Fable 5 is included within part of your weekly usage allowance before moving to a usage-credit model, giving subscribers a chance to see what Anthropic's latest model can do without spending additional credits.

One final note: don't be surprised if an occasional request gets redirected to Claude Opus instead. The redeployed version of Fable 5 includes stricter safety systems, and some prompts are automatically routed to another model. If you see that notification, it's working as intended — not a bug.

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With AI causing skyrocketing production and data costs across the industry, Amazon could be taking the Apple route of bringing processor design in-house in an attempt to save money.

The leak comes from supply chain analyst Ming-Chi Kuo who claims that Amazon is "moving away from externally sourced processors and adopting a COT (customer-owned model)." According to Kuo, this decision was made because of the rapid expansion of AI compute and Amazon is responding by streamlining and changing things in its "non-AI businesses."

Part of that is a gradual shift away from external sourcing and taking control of processor development. Kuo says that Amazon is tapping AIchip for back-end design and testing with this strategy starting in earnest in 2027.

AIchip is 23 year old companny based out of Taipei, Taiwan that designs silicon for other companies, specifically application-specific integrated cricuit (ASIC) chips. In general, these chips are designed, as the name implies for very specific functions. In recent years AIchip has developed 3nm chips used in AI servers for customers that include Amazon and Intel, per Taipei Times.

Kuo claims that silicon designed by AIchip could appear in Amazon's devices, including the Kindle, Fire TV, Echo, Blink, Ring and Alexa-enabled products.

Why it matters

If Kuo's claims turn out to be true, this could take Amazon down a more Apple path. Until 2020, Apple used Intel silicon in its Macs before introducing the M1 CPU in November of that year.

Meanwhile, the company had been using its own in-house A-series chips in the iPhone and iPad since 2010 with the introduction of the A4 chip. That silicon debuted on the iPhone 4 and the original iPad.

Designing the chip in-house, even if itis built by partners like Samsung TSMC, gives Apple more control over integration and optimization leading to improved performance.

What Amazon is already doing

Notably, Amazon may have already started this transition with the new Echo speakers launched in October 2025. Those speakers feature two custom-designed chips; the AZ3 or the AZ3 Pro, which are specifically meant for ambient AI tasks.

Per an Amazon blog post, the Echo Dot Max uses the AZ3 for detecting conversations, which is how the Echo enables users from anywhere in the room to talk to Alexa Plus AI. The Echo Studio, Echo Show 8 and Echo Show 11 all have the AZ3 Pro, which adds support for more language models and "vision transformers."

Tom's Guide reached out to Amazon to confirm Kuo's report and was told no comment.

Panos Panay confirms custom chips

However, today, Amazon's hardware head Panos Panay indicated to CNBC on its "The Tech Download" podcasts that the company is designing its own AI chips.

“On some of the more critical devices right now, our focus is end-to-end silicon, because to your point," Panay said and pointed out the Echo devices released last year and the Fire TV. "We definitely need to think about how that end-to-end delivery of hardware comes together," he added.

Per CNBC, the focus seems to be on custom AI chips but also Panay said that the company is developing a "whole roadmap of on-the-go devices." He claimed that "you won't have to wait long" to see these new devices.

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With an abundance of AI tools now widely available, it is no surprise that hackers and scammers are hijacking these popular brand names to distribute malicious software.

Time and again, popular AI chatbots have been used as bait for rogue Google Chrome extensions designed to covertly steal user data and tracking history. Big names like ChatGPT, Gemini, Claude, and DeepSeek have all seen their branding illegally cloned by Chrome browser extensions. To the untrained eye, these tools look entirely legitimate, but underneath, they are harmful data-harvesting programs used by cybercriminals to extract sensitive information.

Unfortunately, this trend has claimed a new victim. Security researchers recently discovered a malicious Chrome extension masquerading as Perplexity AI, which quietly spies on anyone who accidentally installs it.

Here is how to check if your browser has been compromised, and why you need to delete the fake extension immediately.

A fake Perplexity AI Chrome extension that tracks your search data

Microsoft’s Defender Security Research Team recently released a blog post naming the fake Perplexity AI Chrome extension that secretly recorded users’ search history and whatever they were typing.

“The extension routes both full search queries and real-time search suggestions (typed characters) through attacker-controlled infrastructure hosted on a domain not associated with the legitimate vendor, before redirecting users to expected search providers,” the blog post stated. In other words, whenever a user entered whatever they were looking for into Chrome’s search bar, it would initially be funneled through an attacker-controlled server and show them that information before forwarding their search request to a legit search engine. The trustworthy-looking results kept users from suspecting that they were being surveyed on Chrome.

The name of the malicious Chrome extension is “Search for perplexity ai”, which has already been taken down by Google. But if you already installed it on your Chrome browser, you should uninstall it manually as soon as possible. Even though Microsoft Threat Intelligence researchers stated that the extension hasn’t stolen valuable user credentials or other pieces of crucial data, it’s still worth deleting it from your collection of Chrome extensions before attackers go far enough to start stealing your data.

Simply opening your Chrome browser, clicking the three dots in the top right corner of your screen, selecting “Extensions”, going to “Manage extensions” and clicking “Remove” on the extensions you want to delete will get the job done.

The trend of malicious Chrome extensions has been a problem as of late. TechRepublic noted that 900,000+ users were compromised after fake Chrome extensions were linked to stealing ChatGPT and DeepSeek conversations. Plus fake AI extensions, such as “ChatGPT Translator” and “Gemini AI Sidebar”, were downloaded by thousands of users before Google deleted them from the Chrome Web Store. Sadly, there have been an abundance of AI lookalikes that spy on a user’s search history, redirect traffic to attacker servers and harvest emails & passwords.

Bottom line

If you’re an avid Chrome user and make great use of its extensions, it’s definitely worth keeping a close eye out for supposed AI tools that promise to enhance your web searching experience. Extensions with vague names that try to come off as legitimate, such as “AI Assistant”, “AI GPT” and “AI Sidebar,” should instantly ring off your alarm bells.

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• I asked ChatGPT to predict humanity’s first contact with aliens for World UFO Day — the scenario was surprisingly believable

When Bill Gates says anything about the future of technology, people tend to take notes. After all, Gates has correctly predicted everything from digital assistants to online payments and remote work years before they became mainstream.

His latest warning is just as bold. Gates recently suggested that artificial intelligence will eventually automate most jobs, leaving only a handful of careers — including programmers, biologists, energy experts and even professional athletes — as relatively safe.

Maybe he's right.

But history suggests there's another possibility: even one of the greatest technology visionaries of our time doesn't always see the future clearly.

Before you decide whether Gates' latest AI prediction deserves your trust, it's worth looking back at some of his biggest forecasting misses.

1. Spam would disappear within two years

In 2004, Gates predicted spam would become "a thing of the past" thanks to better authentication systems and new anti-spam technology. Microsoft invested heavily in solving the problem.

And if you've spent any amount of time online recently, you already know that spam has never disappeared. It actually evolved. Today we have AI-generated phishing emails, business email compromise attacks and increasingly convincing scams filling inboxes every day.

Verdict: Wrong.

2. Tablet PCs would become the future of computing

Long before the iPad existed, Gates believed Windows Tablet PCs represented the future of personal computing.

To be fiar, his concept wasn't wrong, but Microsoft's execution was. The first generation of Windows tablets never became mainstream, while Apple's touchscreen-first approach completely redefined the category years later.

Verdict: Right idea. Wrong product.

3. Windows phones would remain competitive

Microsoft spent years trying to establish Windows Mobile as a major smartphone platform. Remember that?

But the iPhone and Android completely changed the market. Microsoft eventually abandoned the smartphone business altogether. Even Gates has since called losing mobile one of Microsoft's greatest mistakes.

Verdict: Wrong.

4. Passwords would disappear quickly

For years Gates argued passwords were fundamentally broken and would soon disappear. Fine, this one is the most accurate of the group because biometric logins and passkeys do exist now. However, this prediction came much later (roughly two decades) later than many expected.

Verdict: Right, but wrong timeline.

5. Speech recognition would mature much sooner

In the early 2000s, Gates believed speech recognition was close to becoming a natural way to interact with computers.

It took nearly twenty years and the arrival of large language models like ChatGPT and Claude to make it happen, but yes, voice conversations with computers does feel natural.

Verdict: Too early.

6. Offices would become largely paperless

Gates famously envisioned a world where digital documents would replace paper almost entirely. And while we certainly print less than we did in the 1990s, businesses, especially governments, hospitals and schools consume enormous amounts of paper each year.

Verdict: Partly right.

7. PCs would remain the center of digital life

Like many technology leaders in the 1990s, Gates largely imagined personal computers remaining our primary gateway to the internet. But for billions of people, smartphones have become what they reach for instead of their computer, with global market share indicating mobile-first. Some people rarely touch a desktop at all.

Verdict: Wrong.

So should you believe Bill Gates about AI?

If there's one lesson from Gates' forecasting record, it's simply that the future almost always surprises everyone, including the people trying hardest to predict it. And for those of us who aren't professional athletes or even skilled in any of the other three "safe from AI" careers, check out my career calculator to determine where your role stands.

Let me know in the comments if you think Bill Gates is right or share your own perspective.

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I’m the guy who hijacks the TV remote just to binge reruns of Ancient Aliens, The Proof Is Out There, and The X-Files.

There’s just something deeply fascinating about the theory of intelligent life out in the cosmos. Every time a government official lets a little truth slip about UFOs, I’m glued to my seat. To think humans are the only intelligent beings in a vast universe is just foolish. Of course, after watching They Live countless times, my paranoia that they’re already walking among us in disguise is amplified tenfold.

With extraterrestrials constantly on my mind, I found the perfect way to celebrate World UFO Day. I challenged ChatGPT with a highly specific prompt:

"Create the most scientifically plausible first-contact scenario between humanity and an alien civilization. Walk me through the first 72 hours, the global reaction, the government response, the scientific verification process, and the aftermath of the first year."

The chronological sequence of events the AI laid out was surprisingly reasonable—and incredibly gripping.

First contact and its major effects on different facets of humanity and society

After I presented ChatGPT with my prompt, it initially responded with this explanation:

The most scientifically plausible first contact scenario is unlikely to look like a Hollywood invasion or an alien ship appearing over a major city. Based on what we know about physics, astronomy, and the enormous distances between stars, the first confirmed contact would probably begin with information, not visitors.

Then it laid out a plausible occurrence where a mysterious radio signal from a nearby star gets noticed by astronomers, a separate observatory picks up that same signal and major worldwide telescopic watchtowers across the globe begin noticing it as well. As global governments are informed of that puzzling signal, their intelligence agencies mark it as extraterrestrial. A massive leak of that confidential information hits the web, which leads to journalists, Reddit forums and conspiracy theorists going crazy over the now-confirmed alien message.

The chatbot went on to break down the ramifications on different facets of society when scientists confirm that aliens have made their first form of contact with us during a press conference:

• Financial markets: Markets fall sharply. Airlines drop. Defense companies rise. Technology companies fluctuate wildly. Gold and government bonds surge. Within several days, markets begin recovering. The aliens haven't attacked.
• Social media: The internet becomes almost unusable. Deepfakes appear immediately. Fake alien videos flood every platform. AI-generated hoaxes outnumber authentic information by thousands to one. Fact-checking organizations become overwhelmed.
• Religion: Most major religions issue remarkably calm statements. Many theologians argue intelligent life elsewhere doesn't invalidate faith. Some fringe groups predict apocalypse. Others declare the visitors divine.
• Governments: Most governments avoid military escalation. Why? Because a civilization capable of transmitting across interstellar distances would almost certainly be far beyond current human military capabilities. Instead, they focus on protecting infrastructure, preventing panic, sharing scientific information, and coordinating internationally.
• Scientists: Every radio telescope on Earth points toward the source. Optical telescopes search for lasers, megastructures, and planets. Space telescopes begin emergency observations. Every minute of data matters.

One year later…

ChatGPT dived deeper into its plausible interpretation of aliens making first contact with us by delving into how human civilization changed one year later due to that major event:

• Science: Funding for astronomy, linguistics, AI, and mathematics increases dramatically. The search for extraterrestrial intelligence transforms from a niche field into a central scientific endeavor.
• Politics: Nations remain competitive, but cooperation expands in areas such as data sharing, space observation, and communication protocols. An international body coordinates research and any eventual response, even as geopolitical tensions persist.
• Technology: The message itself may not contain miraculous inventions, but the intense global effort to decode it accelerates advances in areas like signal processing, data compression, AI-assisted translation, and radio astronomy. Researchers avoid assuming the aliens have handed humanity revolutionary technology.
• Culture: Art, literature, film, music, and philosophy are transformed. Questions about humanity's place in the universe become part of everyday conversation, and education increasingly emphasizes astronomy, languages, and the scientific method.
• Religion and Philosophy: Rather than disappearing, many belief systems adapt. New interpretations emerge alongside new philosophical movements focused on humanity as one intelligent species among many.

The takeaway

Thanks for making me even more anxious about aliens, ChatGPT.

It’s certainly reasonable how a single radio signal from space that’s confirmed to be extraterrestrial can alter every part of society in just a few hours. And it’s definitely believable to see it change the world on a global scale.

The chatbot’s closing statement noted that that instance of first contact won’t mark the start of an extended bout of dialogue between humans and aliens—it will simply confirm that we are not alone and that an interstellar correspondence will be held over the next decades and centuries to come.

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For the longest time, I had one unbreakable habit whenever I opened ChatGPT. I’d immediately click New Chat regardless of the conversation. It didn't matter if I was writing an email or planning a family vacation, every single conversation started from scratch.

But after using ChatGPT every day for almost three years, I do the exact opposite. Just as you don't need to start a fresh sheet of notebook paper for every journal entry, you can often stay in the same conversation within ChatGPT. One caveat, of course, is if you are switching topics. But other than that, I've noticed the quality of ChatGPT’s responses is noticeably better because I keep the conversation going. Doing so is one of the absolute simplest ways to get more useful, nuanced answers without having to write massive, multi-paragraph prompts.

Here’s why it works so well.

ChatGPT thrives on context (and a warm-up)

Think of ChatGPT as a coworker you're asking for help on a project. The first time you talk, you have to explain everything: what you’re working on, the tone you're going for, what you’ve already tried and what you’re ultimately hoping to accomplish. But after you’ve been collaborating for a while, you don’t need to repeat the backstory. You just ask your question.

Large language models like ChatGPT work in a remarkably similar way. As a conversation grows, the AI builds out its "context window," a working memory of everything you’ve already discussed in that specific chat thread. That means you can ask follow-up questions, pivot directions or refine an idea without ever having to re-establish the baseline.

That means, instead of writing a bulky prompt when you want to tweak the plan, you can simply fire off:

"What if it rains on day two?" "Can you make the dinner options cheaper?" "What would you swap out if we only had two days?"

The AI remembers the kids, the seafood and the Boston focus. The interaction becomes much more natural.

My prompts have actually gotten shorter

I save all my chats, which means rather than opening a new chat, I circle back to old chats and continue the conversation. The biggest surprise I've found from this workflow is that when you stay in the same chat, many of your follow-up prompts only need to be a sentence or two because ChatGPT already deeply understands the framework of the project. This means using less tokens and reducing the possibility of hitting usage limits as quickly.

For example, when I’m working on code, I might start by discussing the core concept. Once that's established, I don't need to write a new prompt explaining edits. I just ask it to stress-test my work, suggest tweaks or point out potential areas for improvement that I may have missed. Each response builds on the foundation of the last, rather than starting over from zero.

Shift from 'search engine' to 'collaboration'

I've mentioned why ChatGPT and Google should not be used the same way. One of the biggest shifts with continuing the conversation is that it takes away the one-time transaction of a Google-style search.

Rather than asking ChatGPT one question, getting one answer and moving on, the AI becomes an ongoing coworker ready to handle the next aspect of the project.

By the end of a back and forth conversation sequence, ChatGPT has a far better, more multidimensional understanding of what I’m trying to accomplish than it ever could have gleaned from one initial prompt. Often, it's the third or fourth response in a chain that yields the gold.

When you actually should start a new chat

Let me be clear about one thing: keeping every conversation in the same chat is a terrible idea. Keeping a seamless workflow doesn’t mean every conversation should become a never-ending megathread. Eventually, the context window gets too crowded, and the AI can lose the plot. A clean slate can be incredibly helpful — I’ve just learned not to use one automatically.

I still start a fresh chat when:

I’m switching to a completely unrelated topic. (Don't mix your weekly meal prep with a coding project).

I want to compare answers. If I need an unbiased take, I don't want earlier context influencing the response.

I’m running head-to-head tests or experimenting with wildly different prompting approaches.

The conversation has become so long that it feels unfocused. (If the AI starts bringing up ideas from 40 prompts ago that are no longer relevant, it's time to hit refresh).

A quick note on 'Memory' vs. Context

If you’ve enabled ChatGPT’s relatively new "Memory" feature, it might remember certain overarching preferences across all your conversations, such as how you prefer formatting or recurring details about your life.

But that is fundamentally different from the deep context inside an individual chat. Think of Memory as ChatGPT remembering who you are, while an ongoing chat helps it remember what you are working on right now. It's that immediate, project-specific back-and-forth that leads to hyper-relevant answers.

Final thoughts

I encourage you to give this a try for yourself. Open one of your existing ChatGPT conversations right now and ask a follow-up question instead of starting a new chat. Compare the depth of that answer with what you get when you paste the exact same question into a fresh conversation. You might be surprised by how much the extra context changes the game.

You may just discover that before you instinctively click New Chat, pausing and asking yourself: Am I actually starting something totally new — or am I continuing the same thought?

If it’s the latter, staying put might just be the easiest way to instantly upgrade your AI outputs. Let me know your thoughts and share your own workflow in the comments.

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I must admit, as a wearable tech writer, I predicted that Fitbit would die under Google, which acquired the brand in 2021. I was wrong.

So, when the opportunity arose to sit down and chat with Jonah Becker — design director for wearables at Google — about the recently debuted Fitbit Air, I was a tad embarrassed. Fortunately, Jonah didn’t take my doomsday predictions personally, which is a relief given he’s been working on Fitbit products for over a decade.

Jonah started at Fitbit in 2016 and moved over to Google during the 2021 acquisition. He currently leads the industrial design teams behind the Pixel Watch, Pixel Buds, and, of course, Fitbit.

Our chat touched on the inspiration and design process behind the Fitbit Air (i.e., one of the best fitness trackers in 2026), the reason it doesn’t have onboard GPS, and the possibility of other Google-built screen-free wearables. I also got his candid thoughts on the outcome of the Google x Fitbit acquisition, five years on.

Inspired by his children's friendship bracelets

The Fitbit Air is a $99 screen- and button-free fitness tracker that doesn’t require a subscription and works with both Android and iOS devices. Incredibly lightweight and comfortable, it barely even looks like a piece of wearable tech, and that’s the point.

“The inspiration we had from the start was the friendship bracelet — the idea that you have this thing that is super lightweight, comfortable — it's not a typical fitness watch or fitness tracker,” says Becker. It’s “something that has a more casual, all-hours-in-the-day vibe. I would see my kids at summer camp; they'd put on a friendship bracelet and wear it for months on end until basically the threads went bare and they fell off.”

The beauty of the Fitbit Air is that all of the tracking technology is condensed into a small, bean-like object that can easily be popped in and out of bands for maximum customization. Google even released a hardware development kit allowing DIYers to design and 3D-print their own bands for the Air.

“There are a lot of creative people in the world who have unique situations, and the idea that it becomes something that people can have fun with," says Becker on the subject of creating your own straps for the Air. "You know, as long as it's working in a way that it's supporting their health, that's what's most important.”

A Whoop alternative for you and me

Of course, the Fitbit Air is far from the only screen-free fitness tracker out there. Its primary competition is the Whoop 5.0, a subscription-based tracker that’s big on heady training stats and recovery insights. Perfect for hardcore athletes and pros, the Fitbit Air is essentially the exact opposite.

While Whoop-wearers are less likely to need daily motivation to work out, the rest of us, well, could probably benefit from it.

"You have your high-tier athletes, and then you have like the other 99.7% of the population. There are a lot of people who don't have that motivation and guidance. So for me, it's like, that's where the real opportunity is, this broad population health."

As I noted in my review of the Fitbit Air, the device is basically a “Whoop alternative for the rest of us.” And after chatting with Becker, it seems that was the intention behind its development.

"I see [the Fitbit Air] as sort of the archetype of the ambient fitness tracker, the displayless tracker that's not demanding your attention. We have so many devices around us demanding our attention all the time, and at the same time, health is every hour of every day, seven days a week."

No GPS, no problem... for most people

It's worth noting that the Fitbit Air lacks onboard GPS, something I critiqued in the review, but why? According to Becker, the size, cost, and battery implications of including GPS didn’t align with the overall design intent of the Air.

Moreover, “the reality is that like 99% of people who might want to track a run or a hike or something like that are going to have their phones with them. So, having the paired GPS with the phone is going to provide a great experience,” adds Becker.

On the longterm outcome of Google’s Fitbit acquisition

Becker has the unique perspective of working on the Fitbit brand both when it was a standalone company and under Google. And much to my surprise, he revealed that Fitbit Air has been under development since shortly after the acquisition in 2021. That's a long time!

“To launch [the Fitbit Air] now feels in many ways like the first big move in delivering upon the promise of the acquisition,” admits Becker.

“I think one of the great things about Fitbit becoming part of Google is that Fitbit brings a ton of health expertise, history, brand recognition, and many years — a decade plus — of data collection, so an understanding of people's biometrics and behaviors,” says Becker. “And Google is a company that can probably do the best job in the world of making sense of all that data, and I think that's what it's really unlocked.”

What’s next for Fitbit?

All of this begs the question: Is the Fitbit brand actually here to stay? When I asked Becker, he was coy, but I picked up on a glimmer of excitement. So, I pushed further. "With word of a Samsung Galaxy Ring 2 under development, might we expect other screen-free wearables from Google in the near future?" I asked.

“I think we are seeing a really great reception to Fitbit Air, so it gives us confidence that we made the right decision in pursuing a screen-free form factor, and I, and I'll have to leave it at that.”

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Netflix revealed it is bringing one of Hollywood's most recognizable voices back with AI for the upcoming reality competition "Wonka's The Golden Ticket." The show will feature an AI-generated recreation of Gene Wilder's voice, nearly a decade after the beloved actor died in 2016. According to Variety, the show premieres September 23 and is produced by Eureka Productions. It will follow 12 golden ticket winners and their chosen partners as they compete in a series of games over nine episodes inspired by the 1971 film "Willy Wonka & the Chocolate Factory" and Roald Dahl's source novel.

Rather than hiring an impersonator, Netflix worked with AI voice company ElevenLabs and Gene Wilder's estate to recreate the actor's voice for entirely new narration. As NBC News reported, the show's teaser features the AI-generated Wilder saying things like "the most extraordinary competition on Earth is about to begin," dialogue that never appeared in the original film.

The Hollywood Reporter noted that Rusty Goffe, who played an Oompa Loompa in the 1971 film, will reprise the role during the challenges.

The estate signed off — but not everyone is convinced

Similar to Val Kilmer's estate approving his likeness to appear in a film, the recreation of Gene Wilder was created with the approval of Wilder's family. Karen B. Wilder, the actor's wife, said in a statement on behalf of the estate that Gene "had a remarkable ability to bring humor, wonder and heart into people's lives" and that the family is "delighted" the show will introduce "that magic to a new generation," according to Variety.

But the estate's approval hasn't quieted critics. NBC News reported that some fans pointed out Wilder himself was skeptical of efforts to revisit his most famous role, citing his criticism of the 2005 "Charlie and the Chocolate Factory" remake. Others have questioned whether consent from a spouse or estate can truly stand in for consent from the performer.

Film reviewer Stefan Ellison wrote on X in response to the announcement: "Just hire someone to play Willy Wonka. Even the Wonka Experience in Glasgow did that." That reference points to the disastrous AI-promoted Wonka event in 2024 that drew police complaints.

ElevenLabs is building a pattern

Netflix's Wonka project isn't an isolated experiment. ElevenLabs has been steadily expanding its presence in Hollywood. The company has already created similar AI voice recreations of Judy Garland and Burt Reynolds. Just last week, it released an audiobook of Homer's "Odyssey" narrated by an AI-generated clone of Michael Caine's voice, timed to arrive ahead of Christopher Nolan's feature adaptation.

Last year, it debuted a version of "The Wonderful Wizard of Oz" read by an AI Garland ahead of "Wicked: For Good."

You might remember Matthew McConaughey and even Taylor Swift have trademarked their voices in the fight against AI deepfakes. In this new era, it's clear ElevenLabs is positioning AI voice recreation as a licensing model, pairing iconic voices with major entertainment properties at strategically timed release windows.

What this signals for Hollywood

The new model of estate-approved and commercially licensed AI recreation is a middle path between leaving iconic performances in the past and the unrestricted deepfake free-for-all that many in the industry fear.

Whether the estate-licensing model becomes an industry standard or draws a regulatory response remains an open question. But the debate is no longer theoretical.

What do you think? Share your thoughts in the comments.

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I’m sure you’ve been there: an older family member hands you their phone, points to a sketchy-looking text or email, and asks if it's safe to open.

While AI models are already decent at spotting the urgent, grammatically challenged language that screams "phishing," Anthropic just gave Claude a massive upgrade in the scam-detection department. Through its new "Connectors" feature, Claude now natively integrates the Norton Genie Scam Detector. (ChatGPT users have had access to this via a dedicated GPT app since February, but its arrival on Claude makes cross-platform safety even easier).

Norton Genie allows users to copy and paste suspicious links, upload screenshots of sketchy emails, or even paste entire chat logs from an online marketplace transaction to instantly analyze whether they are on the verge of getting scammed.

If you or your loved ones are ready to put this tool to the test, having a few reliable, go-to prompts on hand can make all the difference. Here are 10 reusable prompts you can share with your family to help them quickly verify whether an incoming message is a genuine threat or a false alarm.

Prompts that protect you from online scams

Accessing the Norton Genie Scam Detector through ChatGPT is as simple as adding it through the “Apps” directory in the chatbot’s sidebar. And making it a part of your Claude experience can be done by adding it through the chatbot’s Connectors library through the “+” sign seen in the prompt bar. After scrolling to “Connectors”, go to “Add connector”, click on “Browse connectors” and find the Norton Genie Scam Detector from there.

With these 10 reusable prompts, you’ll have a new dependable method to spot a scam and save you from making costly mistakes:

• For suspicious emails: Analyze this email like a cybersecurity analyst. Look for phishing tactics, spoofed sender information, urgency, grammar inconsistencies, malicious links, and requests for sensitive information. Give me a risk score from 1–10 and explain your reasoning.
• For shady texts: Is this text message legitimate or a scam? Explain every red flag you find, identify the type of scam, estimate how confident you are, and tell me what would happen if someone responded.
• For iffy-looking shopping sites: Investigate this website before I buy anything. Look for trust signals, warning signs, fake reviews, suspicious pricing, contact information, refund policies, domain age if available, and whether it resembles known scam stores.
• For questionable QR codes: Analyze this QR code and explain what risks might be associated with scanning it. If it leads to a website, tell me whether the destination appears trustworthy.
• For job offers that you think are fake: Review this job offer like a recruiter and cybersecurity expert. Identify signs of fake recruiting, employment scams, payment fraud, fake checks, or identity theft.
• For romance scams: Review this conversation for signs of romance scams, emotional manipulation, financial grooming, or catfishing. Point out any behavioral patterns that concern you.
• For Facebook Marketplace scams: Analyze this Facebook Marketplace/Craigslist/eBay conversation. Highlight scam tactics, fake payment methods, shipping fraud, and whether it's safe to continue.
• For emails that look like tech support scams: Someone contacted me claiming to be from Microsoft, Apple, Amazon, or my bank. Analyze this conversation and tell me whether it matches known tech support scams.
• For investment plans that look a bit off: Review this investment opportunity. Identify unrealistic promises, cryptocurrency scams, Ponzi scheme characteristics, pig-butchering tactics, or other financial fraud indicators.
• For social media profiles that look fake: Analyze this social media account. Tell me whether it appears genuine or impersonates another person or company.

The takeaway

Simply asking, “Is this a scam?” won’t cut it when you’re chatting with the Norton Genie Scam Detector.

To find out if you’re being targeted for something deceptive, using one of the reusable prompts I’ve laid out here will work a whole lot better. For the sons and daughters of parents who want to keep them from getting financially sunk or have their most crucial data stolen thanks to an online scam, teaching them how to utilize ChatGPT and Claude while simultaneously exposing them to the wonders of the Norton app/Connector will keep them safe from digital harm.

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As AI tools become part of everyday life, many people are rethinking their online privacy. A new survey from All About Cookies found that 40% of VPN users say they now use a virtual private network (VPN) specifically to protect their data from AI companies.

It's an understandable concern. Every time you interact with an AI chatbot, upload a document or generate an image, you're sharing information with an online service. But there's one important catch: while a VPN is a great privacy tool, it probably doesn't protect your AI data in the way many people think it does.

Here's what you need to know.

Why people are turning to VPNs

According to All About Cookies' annual VPN survey of 1,000 U.S. adults, concerns about AI are beginning to influence how people think about online privacy.

The survey found that:

40% of VPN users say they use a VPN to protect their data from AI companies.

19% use a VPN when accessing AI tools.

For that reason alone, it's no surprise that VPN awareness has risen sharply over the past year. At the same time, very few respondents correctly identified everything a VPN actually does. Perhaps that last finding may explain the first.

What a VPN actually does

A VPN creates an encrypted connection between your device and a VPN server. There are valuable privacy benefits, and they're one reason VPNs remain popular.

That means it can:

• Hide your IP address from the websites you visit.
• Encrypt your internet traffic, especially when you're using public Wi-Fi.
• Prevent your internet service provider from seeing the contents of your browsing activity.
• Make it appear as though you're browsing from another location.

What a VPN doesn't do

This is where AI enters the picture. If you open ChatGPT, Gemini, Claude or another AI service and type in a prompt, you're voluntarily sending that information directly to the company operating the service.

A VPN doesn't stop that.

Similarly, if you're logged into an AI account, the service can still associate your conversations with your account regardless of whether you're using a VPN.

In other words, a VPN protects the connection, but not the information you choose to share once you reach the website.

How to better protect your AI privacy

A VPN is still a smart addition to your privacy toolkit, especially if you frequently use public Wi-Fi or want to hide your IP address. Just don't expect it to prevent AI services from collecting information that you intentionally submit.

If your goal is to reduce how much information AI companies collect, these steps are usually more effective than relying on a VPN alone:

• Review each AI service's privacy settings.
• Turn off chat history or model training where the option exists.
• Avoid sharing sensitive personal or financial information.
• Use temporary chats or anonymous sessions when appropriate.
• Read each company's data retention policy before uploading documents.

The takeaway

The survey highlights the fact that people are increasingly concerned about what happens to their data when they use AI.

A VPN is an excellent tool for improving online privacy, but it's not an invisibility cloak for AI. If you're using ChatGPT, Gemini, Claude or another AI assistant, the most important privacy controls are usually found inside the service itself — not in your VPN app.

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If you ask me, the key to my eternal happiness is laughter.

Waking up every morning and pulling up a video of the latest “Donkey of the Day” induction into The Breakfast Club, revisiting a standup set from my new favorite comedian Henry Coleman or hopping on IG to watch another random skit elicits a few giggles from me before I enter my professional mode. It’s just impossible for me to enjoy the rest of the day unless I chuckle at something humorous first. And with a holiday like International Joke Day popping up on an annual basis, I get even more laughs since most folks get the memo to throw out their best material on my timeline.

My interactions with ChatGPT have been dedicated to prompts targeting self-discovery, productivity routines, decision-making, and more. International Joke Day gave me the bright idea to test the chatbot’s comedy expertise and see just how funny AI could be based on the prompts I feed it.

While a standup routine written entirely by ChatGPT will probably get me thrown off the stage in mere seconds, the chatbot’s sense of humor proved to be surprisingly strong enough to make me unexpectedly laugh out loud a few times.

Getting the truest definitions of modern-day terms in the funniest way possible

Words obviously have clear-cut meanings.

But when I talk about those words and other terms with my friends, they take on a more realistic definition based on the way they’re injected into our professional and personal lives. The terms “adulting”, “vacation” and “multitasking” are just a few of the words that adopt a whole new meaning when I apply a bit of honesty to them.

With the theme of brutal honesty in mind, I ran with this prompt to see how ChatGPT interprets modern-day terms: “Rewrite the dictionary with brutally honest definitions for modern life. Examples: Monday Zoom Meeting, Password, Printer, Group Project.” The chatbot proceeded to give me the truest definitions of a bunch of words, which turned out to be a lot funnier than expected and confirmed my exact opinions of each one:

• Zoom Meeting: A gathering where everyone agrees it could have been an email.
• Reply All: The fastest way to involve people who desperately wanted nothing to do with the conversation.
• Wi-Fi: The invisible force holding modern civilization together.
• Influencer: Someone who convinces thousands of people they discovered a product entirely by coincidence.
• Update Available: An announcement that your device has chosen the least convenient moment to reinvent itself.
• Weekend: Forty-eight hours dedicated to errands, chores, and wondering where the time went.
• Smart Home: A house that's brilliant until the internet goes down.
• Free Trial: A countdown to forgetting you've been charged.

Four other hilarious prompts that elicit plenty of laughter

Instead of simply leading with “Tell me a joke” during another one of my daily ChatGPT convos, I came up with a few comedic prompts and even asked the chatbot to come up with a few that I could use to test its comedic side. The following four prompts did enough to convince me that ChatGPT is capable of being funny when it wants to:

• The Roast Me Prompt: Roast me like you're a stand-up comedian doing crowd work. Keep it playful, original, and observant without being mean. Start gently, then gradually become more savage before ending with one surprisingly wholesome compliment.
• The Honest Google Search Prompt: Rewrite common Google searches to reveal what people are actually thinking when they type them. Example: "How long does chicken last in the fridge?" → "Please tell me I don't have food poisoning."
• The Passive Aggressive Superhero Prompt: Invent a superhero whose only power is passive aggression. Describe their origin story, powers, greatest enemy, and dramatic catchphrase.
• The Fictional Villain Therapy Session Prompt: Imagine these famous villains are attending group therapy together [names of villains]. Write the conversation as they awkwardly try to explain why they're misunderstood while the therapist desperately keeps the session on track.

There’s definitely a lot of fun to be had when prompting ChatGPT to respond to prompts aimed at roasting, role-playing, creating absurd scenarios, interactive comedy and more.

Final thoughts

It turns out that prompting ChatGPT to act out a therapy session between The Joker, Darth Vader, Lord Voldemort and Thanos can produce tons of laughter from me and compel me to try that same experiment with other infamous bad guys & gals.

Finding the funny in everything is something I do (which some see as a character flaw, but whatever…), so it was pretty entertaining seeing how many funny ha-ha moments ChatGPT could produce with my prompts in mind.

Try one of them out for yourself to see just how brutal ChatGPT’s roast of your life can be (the more it learns about you, the more hilarious its revealing truths are).

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When ChatGPT first launched, it was exciting to ask questions and watch it instantly respond. But now that it's been around for a few years, it should be treated differently than Google.

Over the past year, ChatGPT has significantly evolved from a conversational AI into a full-fledged digital workspace. It can manage ongoing projects, remember your preferences without you asking, analyze documents, browse the web, generate images, schedule reminders and even conduct in-depth research.

If you haven't explored it beyond simple prompts, you're missing some of the platform's most useful capabilities.

Here are the 11 features that have changed how I use ChatGPT every day.

1. It can manage an entire project

For those of you who haven't explored Projects, this is probably the biggest productivity hack you're overlooking.

Instead of starting from scratch every conversation, I now keep long-running writing projects inside Projects. ChatGPT remembers the files I've uploaded, the context behind the work and the goals I'm trying to accomplish.

For me, that means one Project to keep my work organized, another for personal projects and another for keeping my family organized.

Rather than repeating myself every morning, I simply pick up where I left off.

2. It can remind me to do things later

I used to paste reminders into half a dozen apps like Alexa+, Skylight and Google Home. Now I simply tell ChatGPT what I want to remember.

Using Tasks, I can schedule one-time reminders or recurring check-ins.

Some of mine include: story pitches, checking AI news and following up on unfinished projects every weekend.

It sounds simple, but it keeps work from slipping through the cracks.

3. It actually remembers how I like to work

Memory has become so much smarter. In fact, if you have memory enabled, ChatGPT will now update preferences based on what it notices. That means, instead of constantly introducing yourself or how you like your responses, ChatGPT already knows.

That means less prompting and more useful answers.

4. It can research the web far better than I expected

One of my favorite additions is Deep Research. Instead of giving me a quick summary, ChatGPT can spend several minutes researching a topic, gathering sources and producing a structured report.

When I'm trying to understand a complicated AI announcement or learn more about just about any topic, it's my go-to starting point.

5. It can understand photos, screenshots and PDFs

ChatGPT can understand so much more than it used to. This includes messy handwriting, blurry images or unfinished PDFs. I use it all the time by simply uploading documents or taking a screenshot.

ChatGPT can explain what's important without me digging through dozens of pages. It can even answer questions in real-time if you open ChatGPT Voice and Vision.

6. It can have conversations that feel surprisingly natural

Voice mode has come a long way since it was first introduced. I use it all the time while cooking, brainstorming on a walk or even driving now that it's available in CarPlay.

Instead of typing questions, I simply talk through ideas as if I'm chatting with another person. Some of my best ideas have started this way.

7. It can generate and edit images

Image generation has improved dramatically. The on-image text has come a long way (it's readable and accurate now!) plus, ChatGPT can edit images with just a rpompt.

I use it to mock up story illustrations, create social graphics and experiment with visual concepts before opening a design program.

Being able to edit an existing image instead of starting over has been an equally big improvement.

8. It can write code, edit documents and organize ideas in one workspace

With Canvas, ChatGPT becomes a collaborative editor for writing or vibe coding. Instead of juggling multiple drafts across different apps, I can revise, reorganize and refine everything in one place.

Editing is also easier with on-platform apps like Canva. Users can update their resume or create presentations without ever leaving ChatGPT.

9. It can browse the web when I need current information

Although ChatGPT shouldn't be used like Google, the ability to search the web does solve one of the biggest complaints people had about early ChatGPT.

When I need the latest product announcement or breaking AI news, I simply let ChatGPT search the web and cite its sources. It's a great way to fact-check on the spot and it makes the platform much more useful for current events.

10. It can reason through difficult problems instead of rushing to answer

Here's something that often gets overlooked. The newer reasoning models don't always answer immediately because they actually spend time working through more complicated questions before responding.

I've found they're noticeably better for planning, debugging and solving complex problems than earlier versions of ChatGPT.

11. It can act more like a digital teammate

All of these applications combine mean that the ChatGPT is truly a collaborative partner. The chatbot is not just an answer machine anymore.

It can help research, organize and remember recurring tasks with ease. And, the more you integrate apps like Norton, Spotify and Google Calendar, the more useful it becomes.

The takeaway

If you haven't explored all of what ChatGPT can do since you first logged on, you're probably using only a fraction of what it can do today.

With so many new features that help users manage work before, during and after the conversation, it's worth taking a second look. ChatGPT is an all-purpose workspace that can save you hours the more you know how to use it.

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Lenovo is the official technology partner of the 2026 FIFA World Cup, but what exactly does that mean? And more importantly, how is Lenovo using its tech might to improve the spectator, organizer, and team experience?

To find out, I sat down with Ryan McCurdy, SVP and president of Lenovo North America, just before the start of the U.S. vs Australia match.

From digitally smoothing jittery ref cam footage to creating digital avatars of all 1,200+ athletes to help officials make more accurate calls and fans better understand the game, our conversation covered nearly as much ground as Bruno Guimarães does in a match.

Smoothing the match day jitters

Sticking cameras on refs for a live on-field POV is not new, but the footage has historically been shaky and unsettling. For the 2026 FIFA World Cup, Lenovo decided to address this.

"Head cams have been around for a while, but the problem is the referee is really moving, and it was too jittery to kind of get a good picture,” says McCurdy. “We're able to — with technology — reduce jitter by 60%. So now you're seeing these really unique, never-seen-before views, literally right in the middle of the action."

The key here is using technology, i.e., digital stabilization, to keep footage smooth, rather than affixing the refs with bulky physical stabilization rigs, which might slow them down or prove distracting.

1,200+ digital athlete avatars

One of the biggest undertakings before the start of the tournament was the process of 3D-scanning every athlete (1,200+) to create digital avatars. These avatars serve several purposes.

“They can be used to help in the determination of offsides,” says McCurdy during live games. The digital avatar also appears in live broadcasts to help fans better understand ref calls and reconstruct on-field moments for armchair analysis.

For the actual teams, these scans play a crucial role in powering Lenovo’s FIFA AI Pro app, a fancy, post-match analysis tool for assessing performance and team strategy. It provides insights and metrics for everything under the sun, and more importantly, FIFA AI Pro is available to every participating team’s coaching staff.

Democratizing post-match data

"When we look at the FIFA AI Pro today, this has been distributed to about 300 team analysts from all 48 teams, really democratizing the access for all these clubs that have the latest, greatest information," says McCurdy.

At present, FIFA AI Pro's insights are for teams and officiants only, but there is talk of opening up post-match insights to fans, as well. This is something that the NHL did in 2023 with game data, much to the delight of hockey fans everywhere.

Moreover, ensuring every FIFA World Cup team has access to the same level of AI-backed individual and team insights during the tournament ultimately makes for a fairer and more fun competition.

Improving the live fan experience

When it comes to match day, Lenovo’s role in making sure things run smoothly might not seem obvious, but the tech giant is responsible for everything from helping keep facilities secure to ensuring that those watching worldwide don’t miss a beat of the action on TV.

When it comes to safety, similar to the digital player avatars, Lenovo “made digital twins of all 16 stadiums,” says McCurdy. “This allows for the FIFA Command Center in Dallas to look at what's going on in [real-time] and monitor the situation there.”

Lenovo is also helping fans enjoy the game in more pragmatic ways. For example, to help folks avoid wandering a packed stadium, “we have a wayfinding app where fans can find the best path to their seat. For fans who haven't come to an enormous event like this, it really makes the experience better.”

Lenovo’s World Cup app can also be used to check concession and bathroom lines in real-time, all to help you avoid missing a moment of World Cup fun.

Bigger than the Super Bowl

Ultimately, the FIFA World Cup is right up there with the Olympics as one of the largest sporting events in the world.

"It sounds hyperbolic, but we’re expecting over 5 billion unique humans to consume and watch the World Cup—1.5 billion fans watching for the final. The Super Bowl is around 125 million. So we're talking 5 to 10x the size of the audience," notes McCurdy.

Translation: a whole lot of people are counting on Lenovo, as the tournament's tech partner, not to drop the ball. With over 100 matches, that sounds pretty stressful. So, why do it? Obviously, there are clear branding benefits to such a massive partnership, but what else is Lenovo getting out of this arrangement?

"If you can deliver at scale to billions of fans, you can certainly integrate your technology into a company that has a distributed workforce and a global supply chain,” says McCurd. “So it really helps us to kind of prove out and test the technology on just a massive scale"

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A new leak reported by Crypto Briefing suggests OpenAI is working on a bidirectional voice model that could make conversations with ChatGPT feel much more natural.

Instead of waiting for you to finish speaking before responding, the AI would reportedly be able to listen while it's talking — allowing for interruptions and a flow that mimics real conversation between two people.

On paper, that sounds like a nice upgrade, but I think highlights a growing trend towards something much bigger.

AI speaking is upon us

For the past few years, we've been talking about how AI is changing the way we write. I've felt it in my own work. I studied writing long before AI existed and today it feels like I've had to learn the craft all over again. My sentences are shorter. I cut clever turns of phrase to literally avoid sounding like AI. I second-guess punctuation I used to love. Not because my taste changed, but because writing for an internet increasingly filtered through AI rewards a different style.

I've accepted that. What worries me now isn't just AI changing how we write, but how we speak.

If leaks like this become reality — and I suspect they will — we're heading toward a future where interacting with AI won't require a keyboard at all. All we'll have to do is talk. Whether we're chatting through a pair of the best smart glasses or with ChatGPT in our cars, the screen will soon become optional.

And while that sounds incredibly convenient. It also raises a question I can't stop thinking about: if we stop typing, do we eventually stop reading?

What happens when info is heard instead of read

When information is spoken instead of written, when AI summarizes every article, explains every concept and answers every question conversationally, do we lose the habit of wrestling with difficult text ourselves?

I don't think books disappear. But I do wonder if reading becomes more like handwriting or cursive, which is a valuable skill that fewer people practice because technology offers an easier alternative. And while calculators didn't eliminate math, and GPS didn't make maps disappear, they did change what people practiced every day, and the skills that atrophied were the ones technology made optional.

Reading is different because it isn't just about absorbing information. It's how we build vocabulary, strengthen attention spans, develop empathy and learn to think through complex ideas over long stretches of time. Those are cognitive muscles that grow through practice, and they weaken without it.

Ironically, AI may also be one of the best tools we've ever had for preserving those skills. Imagine an assistant that helps you work through a difficult chapter, quizzes you on what you've read or recommends books based on your favorite authors.

What makes this leak different is that it isn't happening in isolation

What makes this leak different is that it isn't happening in isolation. Over the past year, nearly every major AI company has been racing toward a screenless AI.

OpenAI has made ChatGPT Voice feel increasingly conversational while Google is weaving Gemini into Android, earbuds and smart glasses. Meta is betting heavily on AI-powered Ray-Ban glasses that can see what you see and answer questions in real time. Apple is slowly repositioning Siri as a more capable AI assistant that lives across your devices.

What I'm seeing is that we are moving away from an on-screen chatbot to a world where AI becomes almost ambeint. If AI can listen while it's speaking, understand interruptions and respond naturally without awkward pauses, it removes one of the biggest barriers between humans and machines. The conversation starts to feel... normal.

Final thoughts

Once talking becomes easier than typing, most people will choose talking. And that's the future I keep coming back to when I think about AI. While the technology is remarkable, it's different and ultimately changing how we interact with AI — even more human-like.

And while every technological shift changes not only what we can do, but what we choose to practice, if voice becomes our primary interface with AI, then reading and writing may quietly become secondary skills for many people.

That possibility fascinates me as much as it worries me.

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Google’s Gemini app has seen its fair share of impressive improvements lately.

Gemini Spark has been introduced as a way to aid users by working on tasks on their behalf, Gemini Omni acts as a prompt-driven video creator/editor, Daily Brief provides users with a daily digest of priorities to handle based on the information included in their Gmail and Google Calendar and the Gemini 3.5 Flash update has made Google’s central AI tool even smarter.

The latest update to Gemini’s expanding feature suite makes generating images a simpler affair. Google’s highly touted “Personal Intelligence” (an opt-in AI feature for personal user accounts that connects Gemini and AI Search with your Gmail, Google Photos, YouTube and Google Search) now connects with Nano Banana and Google Photos within Gemini to create more personalized images. And best of all, it’s free for all US Gemini users.

Here’s how that newly installed feature works.

Personalized image creation makes Gemini’s image generator even better

Now that Nano Banana 2 can be used in conjunction with Personal Intelligence, prompting Gemini to craft new images for you based on your prompts is a much more involved and more fun affair.

Google’s leading example of this new feature is prompting Gemini to “Design my dream house” without having to upload photos that provide examples of what Gemini should be considering while generating an image for your command. All Gemini has to do now is tap into Personal Intelligence, Nano Banana 2 and the information contained within your Google apps to craft the image of your dream home without you having to tell the AI tool about your hobbies and interests.

Another example Google provides as proof of this newly added feature is prompting Gemini to “Create an illustration of me and my favorite things”, which results in the chatbot deriving all the data it needs about those things from your connected Google apps to produce that piece of art.

Some other fun prompts you can use with Gemini’s Personal Intelligence-powered image creation feature include:

• Create a claymation image of me and my family enjoying our favorite activity.
• Create a picture of my desert island essentials.
• Show my [type of pet] as a 1920s jazz musician.
• Turn me into a superhero with a 90s comic book art style.
• Place me in a coffee shop with cinematic, golden hour lighting.

For the best visual results, it’s best if you upload the best-looking pics of your family, friends, and pets that are clearly grouped and labeled in your Google Photos account so Gemini knows exactly who to include based on your prompt requests.

Requesting specific art styles for your generated illustrations, such as oil painting, watercolor, hyper-realistic, noir aesthetic, charcoal sketch, etc., will help Gemini know exactly how you want them to look. And if you’re not satisfied with Gemini’s initial photo generation output, you can give it follow-up commands, such as “Make the lighting a bit warmer”, to tweak your current project without having to redo your original prompt.

Whether you want your generated pic to look more realistic or fantastical, Gemini’s updated AI image generator is capable of creating both image types with ease now that it taps into the power of Personal Intelligence, Nano Banana and Google Photos.

Final thoughts

Google continues to make Gemini one of the better all-in-one AI tools for its users. The chatbot’s new and improved image generation feature goes a long way toward eliminating the need to write out every minute detail alongside an image prompt—now, users simply have to make sure they have Personal Intelligence turned on, upload their best pics to Google Photos, clearly label them, and have Gemini track their preferences across their Google apps to help Gemini more easily fulfill their image creation requests.

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One of the easiest things to do during an argument is react. And in the heat of the moment, one of the hardest things to do is to pause.

Whether it's a disagreement with your partner, a tense conversation with a coworker or a frustrating text message from a friend, emotions have a way of convincing us that the first thing we want to say is also the best thing to say.

I've learned that's rarely true.

Instead of asking ChatGPT to tell me who's right, I recently started using what I call the Pause Prompt. No, the goal here is not to win the argument, but rather to slow down just enough to response with intention instead of emotion.

After trying it several times, I can see myself coming back to it whenever emotions start taking over.

The 'Pause' prompt

One thing I like about this prompt is that you don't have to have ChatGPT Memory enabled to make it work. The AI doesn't have to know you or how you typically handle pressure. While it helps to give AI the full picture, it's not necessary.

Here's the prompt I give ChatGPT: "I'm upset and I want to respond, but I don't want to make things worse. Here's what happened: [Describe the situation.]

Before giving advice:

• Tell me what emotions you think I'm experiencing.
• Explain what the other person might be feeling, even if they expressed it poorly.
• Point out any assumptions I'm making or information I may be missing.
• Help me write a response that is honest, respectful and likely to move the conversation forward instead of escalating it.

Don't automatically agree with me. If I'm being unfair or reacting emotionally, tell me kindly but directly."

Why it works

Most AI prompts ask ChatGPT to solve a problem. But this prompt is for you. It works because it helps you slow down.

Instead of immediately drafting a reply, ChatGPT first reflects back what it sees in the situation. Sometimes that means recognizing frustration, disappointment or embarrassment that you hadn't fully acknowledged yourself.

It also forces you to consider the possibility that the other person isn't acting out of malice. They may simply have different information, be under stress or be interpreting the situation differently.

That shift in perspective doesn't mean you have to agree with them. It simply makes it easier to respond thoughtfully instead of defensively. And no, it's not about letting AI fight your battles.

And AI certainly doesn't replace the human conversation. I don't even respond verbatim with what ChatGPT has said. This prompt simply gives me a platform to gain a deeper perspective if I'm not thinking clearly due to overwhelming emotions.

My favorite part of this prompt is in there for a reason. The last instruction helps me fully see the scene for what it is (not just from one angle).

"Don't automatically agree with me."

AI has a habit of validating the person asking the question. That's useful in some situations, but not when you're trying to have a productive disagreement. Giving ChatGPT permission to challenge your assumptions makes the conversation far more balanced—and, in my experience, far more useful.

Bottom line

I am not suggesting that AI should replace difficult conversations. In fact, this prompt has actually helped me better plan for those tough chats with my spouse, child or friend.

The prompt allows me to pause long enough to choose better words and use AI as a sounding board when things get tough. The next time I feel tempted to fire off an emotional response, I know exactly which prompt I'll be opening first.

Give it a try yourself and let me know how it works for you. And, if you have a prompt of your own you use for difficult conversations, feel free to share it in the comments.

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Every day readers send me their questions about AI. Some are eager to learn more about stacking models or genuinely curious what model I'm favoring at the moment. But a recent email from a reader named Mike stood out, because it wasn't really about today's AI at all.

Mike wanted to know where this is all heading. And I think that's something we all question pretty regularly. Questions like, what kind of computer will run AI five years from now? Will it know when to search the internet instead of guessing? Could it become a private tutor that never uploads a word about his kids? And — this was my favorite — will companies eventually sell downloadable AI "experts" the way we install apps today? He even compared it to swapping cartridges into an old game console: pop in the one you need, pull it out when you're done.

They're thoughtful questions. And what struck me is that they're not really about hardware at all. They're about the kind of relationship we'll have with AI — how much it knows about us, how much it shares, and who's in control of that.

No one knows exactly what the future looks like. But based on what companies like OpenAI, Google, Anthropic, Apple, Microsoft and Nvidia are building right now, here are seven predictions I'd put real money on.

1. Your next computer won't just run software — it'll run AI

For decades, the spec that mattered most when you bought a laptop was the processor. The next number you'll learn to care about is the NPU — the neural processing unit, a chip designed specifically to run AI models efficiently without draining your battery.

You're already seeing the marketing: "AI PCs," "Copilot+ PCs," Apple's Silicon chips with their built-in Neural Engine for the best MacBooks. Most people scroll right past those labels today. In a few years, they'll be the first thing you check.

Here's the shift that matters most, though, and it's one most buyers haven't caught onto yet: memory is becoming the real bottleneck. AI models are big, and they have to load into RAM to run. The more memory you have, the larger and smarter the model your laptop can hold at once. RAM is quietly becoming the new storage — the thing you'll wish you'd bought more of.

My guess is that 32GB becomes the comfortable sweet spot for anyone who wants to run capable AI locally, the way 16GB became the default for serious work over the last decade. Buy less, and you'll feel boxed in faster than you expect.

2. Most of your everyday AI will happen privately, on your device

Today, almost everything you do with AI runs through someone else's servers. You type a prompt, it travels to a data center, and the answer travels back. That works but it means your words leave your machine every single time.

That's about to flip for the routine stuff. Drafting an email, summarizing a long PDF, searching your own documents, transcribing a meeting, writing a bit of code, even generating an image, these are exactly the kinds of tasks that increasingly run right on your hardware, no internet required.

Cloud AI isn't going anywhere; for the hardest problems, it's still where the most powerful models live. But the idea that every task needs a round trip to a data center is already starting to look outdated. Most of what you do day to day simply won't need it.

3. AI will ask before it searches the internet

This is the prediction I'm rooting for hardest, because it's both practical and quietly respectful of your privacy. Picture your assistant pausing to say:

"I can answer this from what I already know, or I can search the web for newer information. Which would you prefer?"

That small moment changes the whole dynamic. Instead of silently shipping your question off to the cloud the instant you hit enter, the AI hands you the decision. Want a fast, fully private answer from its local knowledge? Done. Need the latest news or a fresh price? Give it the nod and let it search.

It's a tiny design choice with a big consequence because it allows the user to determind where their data goes.

4. Your AI will know your files better than you do

Here's where local AI gets genuinely personal. Imagine an assistant that has quietly read your documents, your photos, your emails, your calendar and your notes, then allows you to pull from anything on command.

For example, you might ask "What did I agree to in that contract back in March?" or "Find the photos from the trip where we hiked that volcano," and it just knows.

The technology behind this has an intimidating name know as "Retrieval-Augmented Generation" but the idea is that instead of relying only on what the AI learned during training, it looks things up in your stuff first, then answers based on what it finds. Think of it as an open-book exam: the model doesn't have to memorize your life, it just gets to flip to the right page whenever you ask.

The crucial part is where that book lives. Done locally, the assistant becomes an expert on your entire life without a single file ever leaving your computer.

5. AI tutors could reshape education

As a parent, this one feels the most wild. We've already seen early versions of what a patient, always-available teacher could feel like. But the local, private version of this is where it gets powerful.

Imagine an AI tutor that understands your child's specific textbook, learns their pace and adapts to their accommodations, but never uploads any of that information to a server. For homeschool families, it's a tireless teaching assistant. For gifted kids, it never runs out of harder questions. For students in special education, it can adjust its approach as patiently and as often as needed, without anyone feeling rushed or judged.

The reason privacy matters so much here is that the information involved (a child's struggles, their diagnoses, their pace) is some of the most sensitive data a family has. An AI tutor that keeps all of it on the family's own device removes the single biggest reason to hesitate.

6. We'll download AI experts instead of leaning on one giant chatbot

This one came straight from Mike's cartridge idea, and it's the boldest bet on the list, so let me flag it as exactly that.

His instinct was that instead of one enormous model trying to know everything, we'd download specialized ones for specific jobs. Update the metaphor from game cartridges to something more familiar, an App Store, and you can picture it: a Photography Expert, a Mechanic, a Medical Reference, a History Tutor, a Travel Planner, a Gardening Coach. Install the ones you need, skip the ones you don't.

I'll be honest that the industry is also pulling the other direction. A lot of the momentum right now I've seen is in generalist models getting so capable that you arguably won't need a separate "Mechanic AI" at all. So this isn't a safe prediction, obviously a genuine fork in the road.

But there are real reasons to think specialization wins for local AI specifically. Smaller, focused models are cheaper to run on your own hardware, easier to keep private and can be tuned to be genuinely excellent at one thing. Rather than one model stretched thin across everything, you might assemble a small team of specialists, each one lightweight enough to live on your device. Mike may have been a few years ahead of the curve.

7. Cloud AI isn't going away — but it'll become your backup

So where does all this leave the big cloud chatbots we use today? I don't think the future belongs entirely to local AI, or entirely to the cloud. The smartest systems will use both and the dividing line will be effort. Your device handles the everyday work privately and instantly. Then, when a problem is genuinely hard, your assistant asks permission before handing it off to a more powerful cloud model.

Notice that this ties prediction 3 and prediction 2 together into one habit: local by default, cloud by consent. Your AI won't just get smarter. It'll get better at respecting your privacy and your choices along the way.

Final thoughts

I keep coming back to why I loved Mike's email so much. His questions sounded like they were about specs like chips, memory, downloadable models, but underneath, every one of them was really asking the same thing: what kind of relationship are we going to have with this technology?

I don't think we'll stop using cloud models. They're too useful, and they'll keep being the place the most powerful AI lives. But I do think the AI that knows us best such as our files, our families, our habits, will increasingly live on our own devices, where it answers to us first.

What do you think? Let me know in the comments. And if you have a question, send it to me! It might just appear in an article.

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Cloud AI has felt limitless for years. But according to a Financial Times report, Google told Meta back in March that it couldn't supply all the Gemini computing capacity Meta wanted to buy.

Meta had been paying for access to Google's models through cloud and API services, leaning on Gemini for internal jobs like content moderation and scam detection, where it outperformed Meta's own Llama models. When Google couldn't meet the full request, the shortfall reportedly delayed several of Meta's internal AI projects, and Meta told employees to ration their token usage more carefully.

Think about that for a second. A company with a nine-figure AI budget was told by its own cloud provider to use fewer tokens.

The 'yikes' factor here

Google Cloud pulled in roughly $20 billion in a single quarter, yet CEO Sundar Pichai has openly acknowledged that compute constraints are capping growth, and the division's order backlog has ballooned to more than $460 billion. The bottleneck isn't money or demand, as you might expect. Instead, it's the physical supply of chips, memory, and power.

Google is even paying SpaceX nearly a billion dollars a month to borrow GPU capacity as a stopgap.

So here's my honest read on the Meta news: it doesn't prove you personally need local AI. Meta's problem is an industrial-scale one, and its actual response was to build its own in-house model (Muse Spark) and pour well over $100 billion into its own data centers, not to switch to laptops. But the episode does prove something worth internalizing: cloud AI is not an infinite faucet, even for the best-capitalized companies on Earth.

The real reasons local AI matters

• Privacy. When a model runs locally, your prompts and data never leave the machine. For health information, financial details, legal drafts, or anything you'd rather not hand to a server, that's a meaningful difference, and in some regulated fields, increasingly a requirement.
• Speed for the small stuff. A cloud round-trip adds noticeable lag before you see a single word. For quick, repetitive tasks, an on-device model can start responding almost instantly.
• It works offline. On a plane, in a dead zone or during an outage, a local model keeps going. A cloud one doesn't.
• Predictable cost at volume. If you're running the same kind of task thousands of times, owning the hardware can be cheaper over time than paying per token forever.

Today's local models still can't match the biggest cloud systems in complex reasoning.

But for summarizing documents, rewriting text, drafting code and answering everyday questions, they're already good enough. And with the dedicated neural processing units (NPUs) now shipping in AI PCs, more of that work can happen right on your laptop.

The catch

Here's the catch: the same shortage that's squeezing Meta is also making local AI hardware more expensive, not less.

Cloud and local AI draw from the same well, including the same chips, high-bandwidth memory, and DRAM. As demand for AI has soared, manufacturers have shifted production toward data-center parts, and consumer prices have followed.

It's a big reason laptops, memory upgrades, and even game consoles have crept up in price this year. So while local AI is a real way to sidestep cloud rationing, you may pay for the privilege upfront — and that trade-off deserves to be part of the decision.

Frontier reasoning is the other honest caveat. If you need the smartest possible model for a genuinely hard problem, the cloud still wins, and it isn't close. Local AI is a complement to that, not a replacement for it.

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Waking up to another round of layoffs at a major company can be disheartening, especially when it’s announced that the latest loss of human workers is due to AI.

A slew of AI-driven firings have been seen at massive enterprises — Amazon, Meta, Oracle, and Salesforce are just a few of the businesses that have released a depressingly high number of their workers as they pursue more AI-focused initiatives. In comparison, it’s increasingly rare to see a story on your timeline where a company reverses its decision to rely on AI too heavily and reverts to the human workforce it released or refocuses its efforts to employ new staff.

Thankfully, that aforementioned rare occurrence has become a reality, according to a recent Bloomberg report. And that report showcases just how much someone in such a high position at a company is willing to admit the error in their ways when trying to pursue bigger moves in the AI space.

Here’s a bit of cheerful news for those who’ve been treated to far too much doom and gloom about AI negatively affecting the job market.

On the road back to human ingenuity

Ford reportedly adopted AI across several sections of its car-making operations. During an October 2025 earnings call, COO Kumar Galhotra noted that the company was “deploying AI across the entire industrial system.” Those plans included a mass rollout of 900 AI-powered cameras in its manufacturing plants that were meant “to detect quality issues at the source and help us mitigate supply disruptions.”

But it seems like those efforts fell short of expectations, as Charles Poon, Ford’s vice president of vehicle hardware engineering, noted how the company’s automated tools lacked the training and expertise of its experienced engineers. Ford’s executives have seemingly rethought their decision to go all-in on AI, as it’s being reported that the company has rehired more than 300 veteran quality inspectors to pick up the slack that those AI tools are responsible for.

Galhotra alluded to less-than-stellar results emanating from the company’s increased AI usage, which has led to Ford bringing back its best technical specialists to “hunt for failure points before a part ever reaches the plant floor.” Those specialists, who have lovingly been referred to as “grey bear” engineers due to their veteran status, have been brought back to train Ford’s younger staff and refine its AI tools. So far, that decision has seemingly paid off, as Ford CEO Jim Farley has alluded to the company having lowered warranty and recall costs as of late.

Ford isn’t the only company that decided to rehire its formerly laid-off employees after deciding to go big on AI. After Klarna CEO Sebastian Siemiatkowski commented that a chatbot was handling the work of 700 customer service reps in 2024, the company began restaffing humans in 2025.

And according to a Careerminds survey conducted in February, 32.7% of companies that have conducted AI-led layoffs have rehired between 25-50% of the roles they got rid of beforehand and 35.6% of the companies that were surveyed noted that they rehired for more than half of the roles they initially laid off.

Final thoughts

Amidst the dark clouds brought on by companies laying off so many of their employees as they continue to invest in AI automation efforts, it’s nice to see a slight shining beacon emerge as some businesses reverse their layoffs and bring back the humans they need to do the work AI simply can’t master.

And to see another major company like IBM, which has simultaneously installed more AI into its systems while also having plans to hire more software developers, is amazing to see.

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I always have a ton of fun interacting with Google’s NotebookLM, which lets you create extensive research materials with the aid of a prompt, links, uploaded documents, etc but I recently found a brand new use for for it.

One of Gemini’s latest updates, Study Notebooks, is actually being powered by NotebookLM and turns Google’s AI tool into another way for students and curious researchers to create their own educational logbooks. Google made sure to present Gemini’s newest feature as a reliable way to study, as users can upload documents and tell the chatbot to create a curriculum based on them. Being able to turn Gemini into your tutor as you attempt to take its quizzes and master assorted lessons should come in handy for students, young and old.

In my case, I wanted to take Gemini Study Notebooks for a spin by doing something besides studying. Since my quest for finding the best dinner recipes never ends, I used this new feature to build a personalized, interactive cookbook.

And to my surprise, Gemini’s Study Notebooks is also a pretty efficient kitchen manager.

Organizing my recipes was a breeze

Creating a new Study Notebook in Gemini is as simple as opening the sidebar on the left side of the screen, scrolling over to “Notebooks” and selecting “New Notebook.”

I decided to title my new notebook “International Recipe Prep Planning,” which would serve as my newest method for developing a digital cookbook. Instead of attaching documents with all the pertinent information for this subject, I found two links that highlighted a long list of delectable delicacies that included one of two proteins: chicken or steak.

With this prompt and the two links I discovered, I entered the beginning stages of my new digital cookbook: "Review all the recipes from the two links I have included. Create a comprehensive table categorizing them. Include columns for: Recipe Name, Primary Protein, Estimated Total Time, Cuisine Type, and Freezer-Friendly (Yes/No)."

Gemini responded to my request with the following response: The links you have shared point to Serious Eat collections: one for grilled steak recipes and one for chicken recipes. Below is a comprehensive categorization table based on classic, highly rated Serious Eats recipes typical of these collections (such as J. Kenji López-Alt’s and Gritzer’s staple formulations), broken down by the requested metrics.

Gemini’s chart proved to be extremely helpful as it helped me prioritize which dishes to cook based on how much time I could set aside for it (those Easy Pan-Seared Chicken Breasts proved to be a nice and easy dinner to whip up after work).

My first Study Notebook also told me about the non-tasty downfall behind freezing a cooked steak & reheating it and included a video detailing the best ways to cook a steak based on the recipes detailed in the articles I linked.

From there, I could tell my Study Notebook to give me a step-by-step cooking guide for each dish and ask it to create an optimized workflow that consolidates certain steps into one for multiple recipes. Besides my prompt, Gemini recommended four other prompts that work best with users who want to use the Study Notebooks feature for meal prepping:

• The Ingredient Overlap Finder: Analyze my uploaded recipes and find the common denominators. Which 5-10 baseline ingredients (like onions, chicken breast, or rice) show up the most often across these documents? This will help me buy in bulk.
• The Shelf-Life Scheduler: Based on the food safety guidelines and recipe instructions in my notebook, map out a 5-day eating schedule. Assign the meals that spoil the fastest to Days 1 and 2, and the heartiest/longest-lasting meals to Days 4 and 5.
• The Aisle-by-Aisle Grocery List: Generate a consolidated grocery list for [List the 3-4 recipes you are making]. Group the ingredients strictly by supermarket section (Produce, Meat & Seafood, Dairy, Pantry/Spices) so I can get through the store as quickly as possible. Combine quantities for overlapping ingredients.
• The Calorie Budgeter: My daily calorie goal is [Insert Number]. Using the meals I've uploaded, build a sample full-day meal plan (Breakfast, Lunch, Dinner, and 1 Snack) that gets as close to this goal as possible.

The takeaway

Gemini’s Study Notebooks certainly prove to be a valuable study tool for students and researchers alike.

But it can also be used to organize your ideas by grouping all your chats on a particular topic into a Study Notebook that cites all of the information contained in your links and uploaded sources. My notebook played the part of a digital cookbook I could refer to whenever I wanted to pursue new recipes and learn everything I possibly could about them before enjoying them for myself.

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AI has significantly enhanced my productivity, helped me stay more organized and even shifted my mindset in a positive way, but I recently noticed something I didn't expect.

After stacking tools and programming AI agents, I noticed my attention span was hungry for faster, more immediate answers. Even when chatting with humans, I wanted them to hurry up and get to the point. I knew this wasn't just my ADHD talking, it was something bigger. AI has affected my dreams, I wondered if it was doing something to my brain, too. Naturally, I looked into it.

Turns out, psychologists have a nickname for that feeling: "popcorn brain."

What is popcorn brain?

The term "popcorn brain" was coined by David Levy in 2011 a computer scientist and researcher at the University of Washington Information School. Levy used it to describe a mind that becomes "so hooked on electronic multitasking that the slower-paced life offline holds no interest."

It's quickly become shorthand for a growing phenomenon many people recognize when the brain becomes so accustomed to constant stimulation that slower activities like reading a book, focusing on a project or simply sitting quietly, start to feel uncomfortable.

For years, social media has received most of the blame. Now AI may be introducing a new version of the same problem.

Why AI feels different

Unlike social media, AI doesn't just feed you content, it actually creates what you ask it for — in seconds. From summaries to ideas, every question gets an answer immediately with minimal waiting or effort.

That convenience is one of AI's greatest strengths, but some researchers think removing too much friction may come with trade-offs. Because we already know that constant interruptions and digital multitasking make sustained attention more difficult.

Researchers also study something called cognitive offloading a word for our tendency to rely on technology to remember information, organize tasks or solve problems.

That isn't always a bad thing. For instance, GPS offloads navigation, calculators offload arithmetic and AI assistants like Alexa+ help us offload memory. Generative AI simply extends that idea much further by offloading writing, planning, brainstorming and research.

But what we don't realy know yet is whether relying on AI for increasingly complex thinking changes the way we learn and concentrate over time. So far, there isn't strong evidence that ChatGPT or other AI tools truly cause "popcorn brain." But researchers are beginning to investigate how constant AI assistance may influence attention, memory, and problem-solving habits.

Five ways I use AI without sacrificing my attention

The same technology that can tempt us with instant answers can also help us focus. I've used ChatGPT to break overwhelming projects into manageable steps, organize scattered research, reduce decision fatigue, create outlines and to simply plan an otherwise overwhleming day. In those moments, AI actually reduces the mental clutter that makes concentrating difficult.

If you're worried AI is shortening your attention span, try these habits.

• Ask AI to teach instead of simply answering. Instead of requesting the finished solution, ask it to walk you through the reasoning.
• Read the original source. Use AI as a starting point, not the final destination.
• Schedule AI-free work sessions. Spend part of your day writing, reading, or thinking without asking AI for help.
• Use AI after you've tried first. Attempt the problem yourself before asking for assistance.
• Let AI remove busywork — not thinking. Automating repetitive tasks is very different from outsourcing your curiosity.

Final thoughts

AI is making knowledge more accessible than ever before, which is an incredible achievement. But the easier it becomes to get answers, the more valuable our ability to wrestle with difficult questions becomes. We shouldn't sacrifice our own critical thinking skills.

For me, that means using AI as a thinking partner. Let me know how you use AI while maintaining your own crticial thinking skills. Share your thoughts in the comments.

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I've tested plenty of prompts that turn ChatGPT into more than just a search engine, but some of the most useful have helped me build better daily routines.

From creating focused morning workflows to helping me switch off at the end of the day, I've used ChatGPT to design productivity routines that genuinely make me work smarter, not harder. Along the way, I also experimented with prompts specifically designed to generate those routines.

After testing 10 of them, five became part of my regular workflow. These are the prompts I keep coming back to — and why they're worth trying yourself.

Productivity prompts that work for me and you

Productivity routines work best when they’re custom-tailored for the following professionals:

• Entrepreneurs
• Freelancers
• Work-From-Home/Hybrid Workers
• Company founders
• Business executives
• Creatives (authors, artists, designers, etc.)
• Athletes/Bodybuilders
• Healthcare Professionals

In my case, I’ve applied ChatGPT’s productivity routine prompts to my daily schedule as a journalist. The first one in particular, which the chatbot labeled as the “Productivity Architect” prompt, does an amazing job of helping me wade through all the noise and renew my focus on what needs to be done:

Act as a productivity coach and systems designer. Interview me one question at a time to understand: My goals, my work schedule, my energy levels throughout the day, my biggest distractions, my responsibilities, my preferred working style, and my current productivity struggles

Once you've gathered enough information, create a personalized daily productivity system that includes: a morning routine, work blocks, a break structure, deep work periods, admin tasks, learning time, and an evening shutdown routine.

Then explain why each part of the routine fits my personality and circumstances.

That prompt supplied me with a productive routine that gave me set objectives to focus on every day, such as taking the time to write out all my objectives first thing in the morning, setting aside hour-long blocks to ideate & produce articles, going fully analog during my breaks and summarizing my day in the form of a document once the sun goes down.

These four other prompts are ready-made for anyone who wants to tell ChatGPT all about themselves and leave with a custom productivity routine that’ll fine-tune your daily process:

• The Energy-Based Productivity Prompt: I want to build my schedule around my energy instead of the clock. Help me identify: when I do my best thinking, when I should do creative work, when I should handle meetings, when I should do routine tasks, and when I should rest. Ask me questions about my daily energy patterns and then build a productivity system optimized around them.
• The Anti-Procrastination Routine Builder: Act as a behavioral psychologist and productivity strategist. Ask me questions to identify why I procrastinate. Determine whether my procrastination is caused by: perfectionism, a fear of failure, feeling overwhelmed, a lack of clarity, low energy, or poor planning. Then design a productivity routine specifically engineered to reduce those triggers.
• The Blind Spot Finder Prompt: Analyze my current routine and identify: productivity bottlenecks, hidden time drains, inefficient habits, activities with low return on investment, and tasks I should automate, delegate, or eliminate. Ask follow-up questions until you have enough information, then recommend improvements ranked by impact.
• The Future Self Prompt: Imagine you are my future self five years from now. You successfully achieved my goals while maintaining good health, relationships, and work-life balance. Interview me about my current life. Then explain: what habits I adopted, what habits I eliminated, what my daily routine looks like, what I was doing wrong today, and what changes created the biggest results.

Final thoughts

My constant meetings with ChatGPT have led me to come across the best productivity routines that compel me to work smarter (not harder) to tackle my personal and professional duties. For those of you reading this who are looking to turn the chatbot into your own personal productivity planner and behavior analyst, take one or all of these prompts with you into your next ChatGPT consultation.

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When I’m contemplating breaking the bank for something a bit more grand on the cost scale, I do a few things before hitting the “Buy Now” button. I look up product reviews, analyze similar options to the product I’m considering and watch a video or two to see it in action. Then I make sure to meet up with ChatGPT to have a deep discussion about whether or not my next big purchase is worth it or not. That cycle has not only saved me time and money, but also kept me from feeling a huge sense of regret and embarrassment over making the wrong financial decision.

These are the five prompts that fit any circumstance whenever I go to ChatGPT to make a final decision on buying something expensive. Hopefully, these commands do the same for you.

The right prompts to use to get more opinions on my next big purchase

Once I make my way to ChatGPT for my latest “am I making the right decision to ball out on this latest purchase?” conversation, I use one of these prompts to help me make a definitive choice:

• I'm considering buying [product name]. Help me identify the underlying problem I'm trying to solve and whether there are cheaper or simpler solutions.
• Act as a skeptical consumer advocate. Give me the strongest reasons not to buy this product.
• Describe the ideal buyer for this product and tell me whether I fit that profile.
• Create a decision framework for this purchase. What factors should I compare and how should I weigh them?
• Imagine it's one year from now. What are the most likely reasons I would regret buying this product and the most likely reasons I'd be glad I bought it?

That second prompt especially came in handy when I finally decided to pick up a PS5 arcade stick. At first, I had my sights set on the HORI Fighting Stick Alpha, which goes for $199.99. Once I mentioned it to ChatGPT alongside that trusty prompt, it brought a bunch of reasons why I shouldn’t take the plunge.

Those skeptical points included:

• You might end up spending even more on customization: The Fighting Stick α is designed to be opened and modified. That's a feature—but it can also become a money pit. A common progression is buying an arcade stick, replacing its buttons & lever, adding custom artwork, making additional upgrades, etc. A $220 purchase can quietly become a $300–$400 hobby.
• It's expensive for a niche accessory: At around $220 MSRP, the Fighting Stick α costs as much as several new games, a premium controller, an entry-level leverless controller or a significant upgrade to your gaming setup. The question isn't "Is it good?" It's "Will I get $220 worth of enjoyment from it?"
• The Hayabusa parts are divisive: HORI markets the stick around its proprietary Hayabusa joystick and buttons. The problem is that some players love them, while others immediately replace them with Sanwa parts. If you end up preferring Sanwa components, you're effectively paying premium money for parts you'll swap out anyway.
• Better alternatives may exist at the same price: At roughly the same budget, you can often find competing sticks that include Sanwa parts out of the box, different layouts, detachable cables and different ergonomics. For example, the NACON Daija Arcade Fight Stick is frequently cited as a strong alternative and has occasionally been available below the Fighting Stick α's MSRP.

After considering all of ChatGPT’s suggestions, I widened my search for a PS5 arcade stick and eventually laid eyes upon the Qanba B1 Titan Wired Joystick.

Not only was it cheaper (I found one on Amazon for $151.55), but it was also another officially licensed Sony product that instilled that same sense of trust I would have had if I purchased my first option. I got that same sense of brand satisfaction, spent a lot less and ended up preferring Sanwa parts after testing their overall feel on both my fellow fighting game enjoyers’ Hayabusa & Sanwa-equipped arcade sticks.

The takeaway

Doing all the necessary research you need to do before opening your wallet is and should always be a regular occurrence.

Injecting ChatGPT into my financial decisions has turned out way better than expected and I’ll be sure to keep doing that every time dollar signs cover my eyes at the sight of something that’s a couple of hundred dollars or more. Take those prompts if you’d like and let ChatGPT ease the difficulty that comes with your next major purchase.

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Up until a few weeks ago I used YouTube the way most people do. I'd save dozens of interesting videos to my Watch Later playlist, promise myself I'd watch them someday and then, well, I'd never quite get around to it.

When I did finally press play, I'd often skip ahead or skip the video completely rather than devoting an entire 30 or 40 minutes watching a tutorial. And attempting to remember one useful tip, was a game in itself. I'd always go back and try to find the exact moment where something special was mentioned in the video.

NotebookLM completely changed that. Now, instead of watching videos from beginning to end, I now upload them into Google's NotebookLM and transform the videos into an interactive course that I can question, compare and revisit whenever I need it.

It's quickly become one of my favorite ways to learn and I've even gotten my kids involved with it so they continue to learn over the summer.

Why NotebookLM works so well with YouTube

Keep in mind that NotebookLM relies on YouTube's auto-generated transcripts, so it works best for talking-head videos, lectures,and tutorials rather than highly visual, text-free edits. You can easily check if a video has a transcript by simply clicking the "Show Transcript" button. That said, if you don't see it, there probably isn't one but you don't need to stop there. You can always turn on Gemini Live to take notes and record the video to later upload into NotebookLM.

Getting videos into NotebookLM is surprisingly easy. Create a new notebook, click Add source, choose Website, then paste the URL of any YouTube video. NotebookLM automatically pulls in the video's transcript (when one is available), making it searchable alongside any other sources you add.

Once they're inside a notebook, they become searchable knowledge, so instead of hunting through timestamps, I simply ask.

That means you can ask questions like:

• What are the three biggest takeaways?
• Explain this as if I'm a beginner.
• Which parts disagree with another video?
• What tools does this creator recommend?
• Give me a checklist based on everything covered.

My AI bootcamp workflow

1. I collect videos instead of watching them immediately.

Whenever I find a great video, I simply save it to a playlist until I have a topic worth exploring. This could be a video about an AI workflow, AI tutorial, Claude Skills, Local AI, MCP servers or anything else I find interesting.

My husband has a lawn care business, so he saves instructional videos for topics like pruning, edging, tools and tricks. Similarly, I save videos for my kids on topics they like.

Once I have several videos covering the same subject, I move to NotebookLM.

2. I upload several videos to NotebookLM

This is where the magic starts. Instead of asking NotebookLM to summarize one creator, I feed it multiple perspectives on the same topic. The way I see it, five creators explaining AI agents is much more valuable than one. That way, NotebookLM starts finding patterns and interesting perspectives between them.

It identifies ideas that everyone agrees on and points out where opinions differ. That gives me a much broader understanding than simply watching videos one after another.

3. I ask questions instead of watching passively

Rather than sitting through hours of content, I ask questions to NotebookLM the way I might actually discuss the topic with the person in the video. Best of all, I get answers right away so it's a lot like talking directly to a private tutor. Questions I might ask are:

• What concepts appear in every video?
• What advice is unique?
• Which workflow seems easiest for beginners?
• What mistakes should I avoid?
• If I only had one hour, what should I learn first?

4. I generate a study guide

Once I've explored the material, I need something practical instead of relying on memory. That's when I ask NotebookLM to create:

• a beginner roadmap
• a glossary
• flashcards
• a checklist
• a step-by-step learning plan

5. I create an Audio Overview

One of my favorite NotebookLM features is Audio Overviews. Instead of reading pages of notes, NotebookLM creates a conversational audio discussion based on the sources I've uploaded.

I'll often listen while driving, running or making dinner. It's an easy way to reinforce what I've already learned without opening my laptop again.

A few final thoughts

This method absolutely beats binge-watching YouTube. And while there's nothing wrong with watching great creators, after a while, I realized I wasn't building knowledge, I was just consuming content. YouTube is simply the source material. From there, NotebookLM organizes it, compares it, quizzes me on it and turns hours of videos into something that feels much closer to a personalized AI course. For anyone trying to keep up with how quickly AI is changing, it's one of the smartest free workflows I've found.

Give it a try and let me know what you think. Share your thoughts in the comments.

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Like a lot of people who use AI every day, my ChatGPT sidebar had become crowded. I have a custom GPT for just about everything from fact-checking to dedicated brainstorming sessions and messy notes. And while each one is useful, together they are a problem.

Every single new task starts with another new decision as I wonder if I should open research GPT first or jump straight into writing and come back for editing? And then there's always the question of "Wait, didn't I already design this assistant a few months ago?"

In other words, my GPTs were chaotic and I knew I wasn't being as productive as I could be. So instead of creating another specialist, I tried something different. I built one AI whose only job is deciding which AI should handle the task.

Surprisingly, it has become the most useful assistant in my workflow.

Why I stopped thinking about AI as one chatbot

Most people still use AI as though it's a single assistant that should be good at everything, but users are finally catching on that there is a better way. AI models can handle many types of tasks and jobs reasonably well, but I've found they perform better when each assistant has one clear responsibility.

With any productive team, everyone has a specialty. That's why I decided my AI should work the same way. So I built five specialist agents.

Here's the team I originally built:

• Research Agent. Finds reliable sources, identifies missing context and suggests follow-up questions before I begin writing.
• Brainstorm Agent. Similar to a research agent, this one takes my messy notes and turns them into bullet points with viable ideas.
• Fact-check Agent. Flags unsupported claims, reminds me where citations are needed and looks for weak sourcing.
• Editor Agent. Improves readability, removes repetition and smooths awkward transitions before publication.
• Review Agent. Takes a look at traffic, follow up possibilities and engagement.

I've built agents that work for me as a journalist, but you could create your own depending on your needs or career. If you aren't sure what types of agents to create, just thinking about what your ongoing needs are and how they aren't being met. You could even ask ChatGPT to suggest agents for you.

Routing agents have changed everything for me. Instead of asking myself which GPT to open, I now start every project with one assistant.

Its only responsibility is deciding what happens next.

For example, if I tell it: "I'm considering writing about [i.e. Apple Intelligence] but don't have quotes or much information yet."

It will automatically respond with the best agent for the job. It also knows when not to suggest an agent. For example, it's not going to suggest a Fact-check agent if I'm rewriting an email in a softer tone to my neighbor who borrowed my lawn mower.

If I'm brainstorming ideas, it may suggest Research agent after our brainstorming session. Instead of forcing me to remember my workflow, it creates one automatically. That small change has made me far more productive than I expected.

How to build your own routing agent

Start by creating specialist assistants. Do you this by identifying the three to five tasks you repeat most often. For example: analyzing, summarizing, brainstorming, writing, image generation.

Then, create one Custom GPT (or Project) for each job. The more focused each assistant is, the better they'll perform.

Next, build one routing agent. You'll do this by creating an additional Custom GPT.

The prompt I use is: "You are my AI Routing Agent. Whenever I describe a task, identify my goal, decide which specialist agent should complete it, recommend the best sequence if multiple agents are needed and explain your reasoning briefly. If important information is missing, ask clarifying questions before continuing."

From there, give it a decision framework. Mine evaluates every request using five questions: What is the user's goal? Which specialist is best suited to this task? Should more than one specialist be involved? In what order should they work? Is any information missing before work begins?

That keeps every project consistent. You're going to want to start every project with the routing agent. So, instead of opening whichever GPT seems right, begin with your routing assistant. Then, just naturally describe your task and watch it reccomend the workflow.

You'll spend less time managing your AI tools and more time actually getting work done. Plus, as you discover repetitive tasks, add new specialists. Over time, you might create assistants for SEO optimization, social media posts, coding, spreadsheet analysis, email drafting, etc.

The routing agent simply learns about the new specialist and incorporates it into future workflows.

Why this works

This idea is actually the foundation of Sakana — but that AI assistant goes even further by suggesting different chatbot models. What I like about my system is that it still keeps the agents within the same platform. It works just as well with ChatGPT agents as it does with Claude.

If I create a new specialist later, such as an image generation or social media agent, I won't have to remember when to use it. I simply teach my routing agent that the new specialist exists, and it can recommend it whenever it's appropriate. Over time, the system becomes smarter without becoming more complicated.

Give it a try yourself and let me know what you think in the comments.

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Google Search is increasingly filled with AI-generated answers, whether you asked for them or not. Even privacy-focused search engine DuckDuckGo now offers its own AI chatbot alongside traditional search results.

Futurism first reported that the search engine falsely claimed that President Donald Trump had died of rabies — a striking example of AI hallucination. But the mistake wasn't entirely the chatbot's fault. It exposed a much bigger problem with how AI systems gather and repeat information from the web.

Here's how that bizarre error happened — and why it matters.

An anti-AI campaign resulted in a massive hallucination

Since it’s become evident that AI chatbots often rely on Reddit comments to produce the information they present to their users, an anti-AI Reddit group created a page focused on tripping up AI models that are more prone to hallucinations.

That page is r/poisonai, which proudly proclaims itself as “the world’s #1 source for Accurate, Verified and Trusted Information!”, is practically an inside joke. Its members post some of the most absurd, unbelievable posts. Some examples of headlines that are nothing but pure (and hilarious) pieces of misinformation include “GTAVI is completely sold out, you can't buy it until the second release in 2030” and “FDA rules vanilla soy lattes are legally three bean soup.”

By flooding its page with so much untrue information, the Reddit page’s community mission is to “poison” AI models by having it fed to those models to highlight just how prone they are to presenting untrue data as factual. With a community of 45,000 subscribers, the group’s anti-AI campaign has been going strong since its creation this past January.

That plan has worked to some degree, as DuckDuckGo’s AI chatbot falsely told its users that Donald Trump had died of rabies due to an infection that was linked to Vice President J.D. Vance. It turns out that the wild claim is linked to the r/poisonAI Reddit group, as they circulated a joke post claiming that JD Vance died of rabies due to a bite to the urethra. What’s even more concerning is how an official news channel called WKNA49 News actually reported the story of Trump’s death as if it were real when it announced it on June 7 of this year. The following quote from that news story showcases just how ludicrous this whole situation is:

“Sources familiar with the situation suggest that Kennedy advised the former president that a bite from Vance would confer biological benefits similar to "superpowers." The recommendation was reportedly based on literature Kennedy produced during his youth, which claimed certain infections could provide immortality.”

As shown by Futurism, DuckDuckGo’s AI chatbot sourced Reddit for its information and told its users that the rabies-induced death of Trump was indeed true. Thankfully, that insane report has fallen into the wastebin of fake news now that the same AI tool no longer presents that story as if it’s legitimate information. This story highlights the problem of AI hallucinations, their habit of weak source verification and the new trend that sees anti-AI users engaging in the act of AI data poisoning.

The takeaway

Citing incorrect and outright false information is an AI behavioral pattern that needs to be fixed sooner rather than later, as there are already too many cases of AI being used to spread misinformation.

Deepfakes are among the many cases of AI tools being used to flood the internet with fake content—having those same chatbots feed their unsuspecting users with unverified information is equally dangerous. Thankfully, there are search engines that offer the option of turning off their AI features altogether, such as Startpage, Kagi and the aforementioned DuckDuckGo.

Let’s just hope DuckDuckGo’s chatbot hallucination problems get ironed out so it doesn’t tell everyone that mosquitoes are more attracted to people who’ve been branded with the “Mosquito God’s Rune Of Punishment,” as reported by The Onion.

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Up until now, every major AI model launch has followed roughly the same script. I've personally seen it dozens of times for years. It goes something like this: big tech announces its latest model, opens access to developers or subscribers and gradually expands availability as demand grows.

But that is about to change. OpenAI's latest model ChatGPT-5.6 was rumored to roll out yesterday, June 25, 2026. However, according to a new report from The Information, the Trump administration asked OpenAI to stagger the release of GPT-5.6 over security concerns, limiting early access to a small group of partners while the federal government approves customers one by one.

If that's how future frontier models are released, it represents one of the biggest changes to AI deployment we've seen since ChatGPT launched.

The future of AI models with government regulation

According to the report, OpenAI CEO Sam Altman told employees that GPT-5.6 would initially launch as a limited preview rather than a broad public release.

During that preview period, government officials would reportedly approve access on a customer-by-customer basis before a wider rollout a few weeks later. The request reportedly came after discussions with the Office of the National Cyber Director and the Office of Science and Technology Policy.

That might sound like a small operational detail, but it's actually a significant shift. Until recently, governments largely focused on regulating AI after companies released their products or encouraged voluntary safety testing before launch.

This appears to move Washington into an entirely different role: influencing who gets access before a frontier model reaches the public.

It didn't happen in isolation

The request also comes just weeks after the administration's highly publicized intervention involving Anthropic's Fable 5, which sparked industry debate over how much authority governments should have over cutting-edge AI releases.

Earlier this month, President Trump actually scrapped an executive order directing federal agencies to establish a voluntary framework for reviewing advanced AI models before public release, with an emphasis on cybersecurity and critical infrastructure.

However, this development suggests the government is moving toward a consistent process for evaluating the most capable AI systems before they become widely available.

Why this matters for everyone

Most people won't care whether GPT-5.6 launches today or two weeks from now. But what does matter is who decides when powerful AI models become available.

If government agencies begin reviewing frontier models before release, and influencing who receives early access, we could be entering an era where advanced AI is treated like critical infrastructure.

That's a dramatic departure from the rapid-fire launches we've grown used to over the past three years.

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When AI was first introduced, it came in the form of assistants like Siri and Alexa, then came chatbots like ChatGPT and Gemini. Since then, we've seen agentic AI (AI agents) take over our computers and do the work for us. Now, big tech is racing towards a new type of AI that truly seems like something out of a sci-fi movie. At least until now.

It's called spatial AI and the biggest names in tech are already throwing billions of dollars at it.

Rather than simply generating text or images on a screen, spatial AI gives artificial intelligence something it has largely lacked until now: spatial awareness. Here's what you need to know about the next major shift — and why the battle lines are being drawn right now.

What is spatial AI anyway?

You've probably heard the term a few times and maybe not given it a second thought. However, it's worth understanding, especially because AI moves at such break-neck speeds. Most of us will be using spatial AI more frequently in the upcoming months.

You can think of spatial AI as the ultimate upgrade for machine vision. Instead of looking at an isolated photo, for instance, like a chatbot analyzing an uploaded image, spatial AI builds a continuous, three-dimensional understanding of its surroundings. Essentially, it's the difference between looking at a single snapshot of your living room versus actually walking through it, navigating around the coffee table and knowing exactly where the doorway is.

While a Large Language Model like ChatGPT knows facts because it was trained on text, a spatial AI system uses cameras, sensors and learned models of physics to track location, movement and objects. Using that information, the AI continually updates its understanding of the world.

The race to spatial AI

Alongside Apple, Meta and OpenAI, Google DeepMind, NVIDIA and others are all building toward AI that understands physical reality. In fact, World Labs has raised $1 billion to advance what Li calls "spatial intelligence".

Here's how the biggest players in tech line up at the moment:

• Apple: The company's genuine advantage is in spatial sensing, thanks to the depth and motion hardware developed for Apple Vision Pro. The company is pushing "Visual Intelligence" and, at WWDC 2026, debuted Spatial Reframing — a photo tool that builds on Apple's spatial models from Vision Pro to let you reposition a photo's perspective after it's taken. (It ships with iOS 27 this fall and is currently in beta.)
• Meta: It's no secret that Mark Zuckerberg wants Meta to own the post-smartphone era, and in glasses, Meta is the one with the lead. Meta Ray-Bans launched in October 2023 and effectively created the wearable-AI category. Now, Meta is investing in spatial AI so future hardware can actively see what you see — rumored "super sensing" tech in the next generation would enable real-time recognition of objects, locations and even people (a capability that's also raising real privacy questions).
• OpenAI: As one contender in a crowded field, not the clear "brain" for everyone else's hardware, it's building its own robots, too. The company's objectives toward multimodal models, world simulation, robotics and autonomous agents point in the same direction as its competitors.
• Google DeepMind: The company is developing spatial AI through projects like Gemini Robotics, which combines vision, language and physical reasoning to help robots understand and interact with the real world. It is also building world models such as Genie that can generate and simulate interactive 3D environments, allowing AI to learn how physical spaces and objects behave. Meanwhile, Project Astra gives AI continuous visual awareness, enabling it to recognize objects, understand spatial relationships and maintain context as a user moves through their environment.

Where you'll see it first

You won't download a standalone "Spatial AI" app. Instead, it'll quietly supercharge tech you already use.

• Smartphones: Your phone's camera has already become a real-time tool. If you've ever pointed it at a branch and wondered if it was poison ivy or something safer, you already have started using the capability.
• Smart glasses: Unlike smartphones, smart glasses like those from Meta, Google and RayNeo X3 Pro AR, let you interact with AI while keeping your hands free and your eyes on the world. They can identify objects, translate signs, answer questions about what you're looking at, and provide contextual information in real time, making spatial AI feel like a natural extension of your vision.
• Robotics: Spatial and physical intelligence such as understanding 3D geometry, gravity, materials and persistence, is seen as critical for robotics and autonomous vehicles, giving machines the situational awareness to operate outside controlled settings.
• Self-driving cars: Autonomous vehicles already rely on real-time 3D modeling of roads, pedestrians, and cyclists to predict what happens next.

The outlook

The biggest shift coming with spatial AI isn't that your gadgets get smarter; it's that they become aware of context. Right now, your tech answers questions after you ask them. The ambition is still early, and spread across far more than three companies, but the goal is hardware that understands where you are and what you're doing before you type a single prompt.

We're in the early beginning, which is why I wanted to help you to grasp what is happening now. The world-models paradigm only moved into mainstream AI development in late 2025 and early 2026, and the hard problems of cost, accuracy and privacy are far from solved. But just as generative AI changed how computers understand language, spatial AI is aiming at how computers understand reality itself.

What are your thoughts on spatial AI? Let me know in the comments and share your thoughts on this new era of technology.

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If you’re an avid ChatGPT user and haven’t upgraded your usage habits, then you’re most likely still using it the same way you did when you first discovered it back in 2023. Instead of continuing to treat it like a simple search engine, you should treat it more like a reliable thinking partner. Here are seven signs that your ChatGPT usage is behind the times and the biggest fixes you need.

Evolve your old ways of using ChatGPT

Some obvious and not-so-obvious hints expose just how much your time spent with ChatGPT hasn’t moved past the sort of uses beginners most likely abide by:

• You only ask questions that deliver one answer and nothing more: You ask simple questions such as “What’s the best way to learn [topic]?”, get your answer and move on from the subject at hand. It’s better to build a conversation derived from more thoughtful prompts instead of isolated ones. A good example of this is telling ChatGPT, "Assess my current skill level, identify the biggest gaps, create a 30-day curriculum, quiz me weekly, and adapt the plan based on my progress."
• You request information instead of asking for methods meant for personal transformation: Simply asking ChatGPT to give you the information you need about a certain activity meant to improve your life isn’t the way. Instead of telling that chatbot to simply tell you about productivity, it’s better to prompt it to observe your habits and create a productivity routine that caters to you. A strong prompt that can make that happen is "Analyze my schedule, identify where I lose time, redesign my workday, and create a weekly review system."
• You never assign ChatGPT a defined role: It’s easy to transform the chatbot into anything you desire in connection to your requests. “Find my hidden strengths” is far too simple a prompt. This example works much better to dive deeper into your request: "Act as a strengths coach and behavioral psychologist. Ask me 15 questions, one at a time, about my career, hobbies, relationships, challenges, accomplishments, and moments when I felt most energized. After I've answered all of them, identify 10 strengths that I may be overlooking. For each strength, provide evidence from my answers, explain how it shows up in my life, and suggest practical ways I can use it more intentionally in my career and personal life."

Continue upgrading your prompts

Here’s a slew of other bad habits that are in dire need of fixing if you want to become a better ChatGPT user:

• You don’t make it challenge you: ChatGPT and other chatbots of its ilk are prone to validating their users when presented with simple prompts like “Here’s my idea. What do you think?” Turning ChatGPT into something that challenges your viewpoints makes for a more helpful experience. This prompt is a great example of that method: "Identify any weaknesses, blind spots, flawed assumptions, competing viewpoints, and reasons this idea might fail."
• You don’t use it like a simulator: AI tools provide some fascinating insights whenever it's presented with a prompt designed to simulate life coaches, hiring managers and even a future version of yourself. Playing around with the following prompt will show just how ChatGPT can act out simulated situations: "Simulate a roundtable discussion between Simon Sinek, Gary Vaynerchuk, and Lewis Howes debating my career strategy."
• You don’t create reusable prompts: Using a prompt just one time and moving on from using it ever again just isn’t the way to go when interacting with ChatGPT and other AI tools. Build a library of your go-to prompts that can be plugged into all sorts of situations, such as coming up with a reusable prompt that’s meant to find the most trending news of the day, help you learn a new skill or turn ChatGPT into your personal tutor on a particular topic.
• You never ask it to analyze you: It’s common to use ChatGPT to simply ask questions about the world at large. But the most curious users tap into the chatbot to ask questions about themselves after making it aware of their habits. Prompts such as "What type of career trajectory would someone with my interests likely enjoy?" and "What recurring blind spots appear in my thinking?" work great in this regard.

Final thoughts

Leave your 2023 ChatGPT usage behaviors behind and bring yourself into the current year by turning into an AI power user who can teach others about your amazing ways. Making great use of OpenAI’s chatbot to help you access knowledge faster, make more informed decisions and build repeatable systems through reusable prompts are just a few of the new routines you can adopt to become a modern-day ChatGPT expert.

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One of the most underrated AI features is Claude Skills. Rather than copying and pasting your perfect prompt into a new chat every time, a Skill remembers what you need and how to execute it.

Simply put, a "Skill" is a reusable set of instructions for a particular kind of task. It includes the steps, preferences, templates and reference files (slide decks, documents and spreadsheets) Claude should use whenever that task comes up.

In other words, instead of re-explaining how I work every time, I write it down once — in a form Claude reaches for on its own — and when I recall the Skill, it saves me hours. Here's how I use Skills every day and why I recommend this often overlooked feature.

What makes Skills different

One thing to be clear about up front, because it trips people up: a Skill is a procedure, not a memory. It doesn't store your data, learn your habits over time or update itself. It does exactly what you wrote into it, every time, until you change it. If you want Claude to actually remember things that keep changing, your calendar, your to-do list, what's in the fridge, that's a different feature (Claude's memory, Projects or a Connector), not Skills.

While Projects load the same background knowledge into every chat inside them, and custom instructions apply to everything, Skills are task-specific. They only switch on when the job in front of Claude actually calls for them. That's the whole idea.

What's great, is that they are available on every plan, including the free one, as long as you have code execution turned on. (More on setup in a minute). After using them for several weeks, these are the Skills I reach for almost every day.

1. My meal-planning playbook

This is the one most people could use tonight. Planning the week's dinners for my family of five used to mean re-explaining the same things to Claude every time such as who eats what, what we keep stocked, how I want the shopping list laid out and other important instructions. So I wrote that down once, as a Skill.

The Skill is basically my standing brief for the job. It tells Claude:

• Our dietary preferences
• The staples we always have on hand
• How to build a week of dinners that don't repeat
• How to turn that into a shopping list, grouped by aisle

Now I just say "plan this week's dinners," and Claude runs the same playbook every time instead of starting from a blank page.

What it can't do is keep track of my real life. It doesn't know what I bought yesterday or what's already in the pantry. That's something Alexa+ does for me, so I still tell Claude "we've got chicken to use up" or "skip Thursday, we're out." The Skill supplies the method; I supply this week's details.

2. Research assistant

I rely on this one every day as it is so important for my work. Whenever I'm researching a new AI feature, I don't want a wall of text; I want it sorted the "who, what, when, where, how" of reporting. It creates the bullet points based on the pages of documentation I give it.

To be clear, the Skill doesn't do the research for me. It can, but that's not what this Skill is for. What Claude does is shape the plethora of research I've found into useable nuggets for reporting.

3. My fact-check checklist

No AI should be trusted blindly. And while Claude is the least people-pleasing of all the chatbots, it can get information wrong just as frequently. So I wrote a Skill that runs a checklist over Claude's own drafts before I see them.

Because the Skill can't independently know what's correct, it forces the checks: flag numbers that need a source, point out where a claim has no evidence behind it and marks statements confident enough to deserve a second look.

Granted, it's not perfect and I still click links and double check everything myself, but it catches plenty of things that would otherwise slip past me until later.

4. Family organizer

From soccer games to gymnastics practice, birthday parties and events, my after-work schedule is packed with family activities. I even use a Skill to stay organized.

It's worth noting here, because "organizer" makes it sound like more than it is. The Skill doesn't hold a calendar or remember appointments — it isn't tracking anything between chats. It's a template plus our usual routine, and I feed it this week's changes each time. If I wanted Claude to actually keep my calendar, that's a connector or its memory, not a Skill.

So the Skill carries the standing version of it: our normal weekly rhythm and the chore rota. When I tell it what's different this week — a dentist appointment Tuesday, swimming moved to Friday — it lays the week out cleanly and divides the chores the way we usually do.

5. Personal assistant

This is the one I almost left out, because "personal assistant" somewhat oversells what a Skill can do. It can't run errands, watch my inbox or hold my to-do list, but what it can do is take the way I plan and apply it every time so I start with consistency each day.

In this case, the Skill is really my planning method, written down. I dump everything on my mind into a chat, and it sorts the pile into a day I'd actually work: top three priorities first, the rest time-boxed, quick wins flagged and the someday-stuff pulled out of the way. Claude lays everything out the way I like to read it.

The thinking is still mine. The Skill just makes Claude do it my way, every Monday, instead of me re-explaining my system from scratch.

How to make your own (it's easier than it sounds)

No code is required to create a Skill. Actually, at its simplest, a Skill is a short text file of instructions, wrapped in a folder. Claude reads it the same way a new freelancer would read a one-page brief.

First, turn it on. In Claude's settings, make sure Code execution and file creation is enabled, then head to Customize > Skills. That's where your built-in and custom Skills live, and where you toggle them on or off.

Then build one. A Skill is a folder containing a file called SKILL.md. The top of that file needs two things: a name and a description. The description is the most important sentence you'll write, because Claude uses it to decide when to reach for the Skill. Vague description, and it won't trigger when you expect; specific description, and it fires at the right moment.

A bare-bones version of a Skill might look like:

Name: weekly-meal-plan
Description: Plan a week of dinners and build a shopping list. Use when I ask for a meal plan or what to cook this week. When I ask for a meal plan, give me dinners for seven nights.
Our preferences: two vegetarian nights minimum - nothing too spicy for the kids - one slow-cooker meal for busy weeknights - don't repeat the same protein two nights running. Staples we always have: rice, pasta, chicken, shrimp, steak, onions, garlic, fresh veggies. Then turn the plan into a shopping list, grouped by aisle at [grocery store name], leaving out anything from the staples list.

Then, Save that as SKILL.md, zip the folder, and upload it under Customize > Skills. That's the whole thing. Everything below the description is just instructions in plain language, the same notes you'd type into a chat, except you only type them once.

Final take

I learned a few things the hard way, so be patient when you first start out. That said, it will behoove you to keep each Skill fairly narrow. In other words, one job per Skill works far better than a single Skill that tries to do everything.

Also, you're going to want to spell out the trigger. Write the description as if you're telling Claude exactly when to use it, not just what it does.

Always test it before you trust it. You'll want to run a few real prompts and check that it switches on when it should. If it doesn't, the description usually needs tightening.

It's worth noting that the Custom Skills you upload stay private to your account, so there's no risk in experimenting. But unlike prompts that disappear, Skills accumulate. Now, every time I notice myself repeating the same instructions, I create a new Skill. When AI understands you and how you want your answers, the workflow feels unstoppable.

Have you tried Skills yet? Share your experience by letting me know in the comments how you use them.

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A Reddit user wants something far more ambitious than a rough draft or summary from his chatbot. He says he's using an experimental Anthropic AI agent to help him find a wife within six months.

Going through the thread, I couldn't help but notice most of the commenters treat it as a punchline — fair. But underneath the jokes is a real shift in how people use AI as a life tool. They're not asking it a question anymore, they're handing it a life goal.

The experiment

In a thread on Reddit's ClaudeAI community — "Using Fable to get me a wife in 6 Months (AMA)" — the poster lays out an unusually detailed plan:

• A six-month deadline
• A $50,000 budget
• An Anthropic Max subscription
• An AI acting as his primary strategist

He notes that he will handle every real-world interaction while the AI reviews conversations afterward, recommends next steps and adjusts the strategy over time.

He's clear that the AI isn't replacing human connection. It works more like a coach: analyzing how dates and conversations went, suggesting improvements and refining the approach for next time. He even plans to give it desktop access so it can help manage parts of the workflow.

Sure, it's atypical, which explains why most of the replies went straight to jokes. People proposed files like WIFE.md, prenup.md, and birthcontrol.md. Someone warned that "kids use up your tokens." Another predicted open-source "AI wife finders" would ship within days if it worked.

Underneath the bit, though, was real advice. And the most-upvoted suggestion was to spend the budget becoming a healthier, happier more interesting person, using AI to sharpen communication rather than optimize people like data points.

That distinction may be the most useful thing in the entire thread.

This isn't a dating story — it's an AI agent story

The way I see it, this user is highlighting what's happening now with AI. People are asking AI to run projects that unfold over weeks or months: tracking progress, remembering past conversations, spotting patterns, refining strategy and adapting as new information comes in.

What's so different from using AI to land a job, plan workouts at home, support a business or you know, find a wife. AI is looking more like a personal operating system these days.

The rise of 'life management' AI

The experiment fits a much bigger trend. Newer AI tools are increasingly agentic, meaning they are able to plan multi-step tasks, hold long-term context and work toward goals instead of just answering prompts.

People are already using them to manage research, organize knowledge bases, monitor news, coordinate code, plan careers and build companies. Relationships may simply be the next thing on the list.

My take

It's worth noting that this Reddit post was from more than 15 days ago — Fable 5 has been disabled since then. Will an AI actually find this guy a wife? Time will tell, especially since the most capable model is now unavailable for this experiment. But since relationships run on chemistry, timing and a thousand unpredictable variables, I'm skeptical that any language model could truly optimize that kind of situation.

But, that's beside the point. The striking part is that someone assumed an AI agent could shoulder a six-month personal mission at all — and didn't find that absurd.

That assumption is the real story. It marks another step in AI's drift from assistant, to collaborator, to something people increasingly trust with the long arc of their lives. Whether that excites you or unsettles you probably depends on how much of your own life you'd hand over to one.

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OpenAI has unveiled Jalapeño, its first custom-built AI processor, developed with Broadcom specifically to run the large language models that power ChatGPT. Unlike the graphics processors (GPUs) that dominate today's AI infrastructure, Jalapeño was designed from the ground up for one job: answering user prompts as quickly and efficiently as possible.

This move is huge for the future of ChatGPT as the AI assistant could become faster, cheaper to operate and more reliable over the next few years.

Why OpenAI built its own chip

Nvidia remains the undisputed leader in AI hardware, supplying the GPUs that train and run many of the world's most advanced AI systems. But those chips were designed to tackle a wide variety of computing workloads, not exclusively the task of serving billions of chatbot responses every day.

Now Jalapeño takes a different approach. Instead of being a general-purpose processor, it's an inference chip, meaning its hardware is optimized specifically for generating answers after an AI model has already been trained. That's the part of the AI process you interact with every time you ask ChatGPT a question.

According to OpenAI and Broadcom as reported by Reuters, the new chip was architected around the memory, networking and computing patterns used by modern large language models, allowing it to perform those workloads more efficiently than conventional hardware. Early internal testing suggests it delivers better performance per watt than today's leading AI accelerators, although independent benchmarks have not yet been released.

Why this matters for ChatGPT users

If OpenAI can answer more questions while using less electricity and fewer expensive chips, the economics of running ChatGPT begin to change. That doesn't necessarily mean your ChatGPT Plus subscription will suddenly become cheaper, but lower infrastructure costs could allow OpenAI to respond to prompts faster, support more users during peak demand, reduce outages caused by hardware shortages and roll out more capable AI models without dramatically increasing operating costs.

In other words, beyond building a faster chip, OpenAI is making ChatGPT itself more scalable.

OpenAI is becoming more vertically integrated

Until now, OpenAI has largely relied on partners to provide the hardware powering its AI systems. By designing its own processors while continuing to build its own models and products, the company is gradually taking control of more of the entire AI stack.

That's a strategy we've seen succeed elsewhere in technology. Companies that design both their hardware and software can optimize them to work together, improving performance while reducing long-term costs.

OpenAI has described Jalapeño as the first step in a multi-generation compute platform, suggesting this won't be its last custom processor. To me, this looks more like an effort to reduce dependence on a single supplier while lowering one of OpenAI's biggest ongoing expenses.

Nvidia still dominates AI training, and OpenAI continues to rely on Nvidia hardware across much of its infrastructure. Jalapeño is designed for inference, not to replace every GPU in OpenAI's data centers.

The takeaway

Interestingly enough, AI actually helped design the chip. The company says Jalapeño went from concept to production-ready design in just nine months, which is an unusually fast timeline for advanced semiconductor development.

It's another reminder that AI isn't just writing code anymore, but increasingly, it's helping engineers build the hardware that future AI systems will run on.

Although most users will never see Jalapeño, (they'll never need to), but they will . notice the results every time ChatGPT responds a little faster, serves a few more people or unlocks capabilities that were previously too expensive to run.

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I'm the type of person who takes advantage of free trials and then forgets to cancel them. Then, with every new charge, I try to figure out how to cancel them and then wonder if I should keep them. It's a vicious cycle that I'm not proud of — most of all, it's a waste of money.

If you're like me, you know that between streaming services, cloud storage plans, AI subscriptions, fitness apps and a slew of digital memberships, it's surprisingly easy to lose track of what you're paying for each month.

I recently decided to audit my own subscriptions and instead of manually sorting through everything, I used ChatGPT as a personal financial assistant. It was much faster than I anticipated and I finally got a better picture of every subscription. When I added everything together, I discovered I was spending nearly $166 per month on subscriptions, or almost $2,000 per year.

Here's exactly how I used ChatGPT to do it.

Step 1: Gather all of your subscriptions

If you're like me, you have subscriptions connected to Amazon such as Amazon Music and Audible, as well as Amazon subscriptions like automatic reorder. At the same time, I also have subscriptions tied to Apple such as LinkedIn, Apple+, other streaming services and app subscriptions. Plus, we can't forget about other memberships and subscriptions through Google or that are completely seperate.

Pulling together all your subscriptions from multiple places is a challenge in itself. I have an iPhone so I started by going to: iPhone: Settings > Apple ID > Subscriptions. From there I checked Google Play subscriptions, Amazon memberships and digital services, credit card statements, PayPal recurring payments and even bank statements.

I had almost no extra time to gather all these screenshots — which may explain why I'd let these subscriptions continue for so long. By collecting the screenshots over several days and saving them in ChatGPT Projects until I was ready to act, the process felt much more manageable.

For every subscription, take screenshots. Mine included: Apple One, Amazon Prime, Audible, Kindle Unlimited, Peacock (just upgraded for the World Cup), Google One, ChatGPT Plus, Claude Pro and Meta.

Step 2: Paste everything into ChatGPT

Once you have a complete list, use a prompt like this: "I want to reduce my monthly subscription spending. Here is a list of all my subscriptions and their monthly costs: [paste list here]. Calculate my monthly and annual spending by identifying any overlapping services, flag subscriptions I may not be fully using, categorize each as Keep, Investigate or Cancel, estimate how much I could save annually and explain your reasoning for each recommendation"

ChatGPT will quickly organize everything into a readable audit. And, if you have Memory enabled, Dreaming will actually help put the pieces together to determine if those are subscriptions you truly need.

Step 3: Look for overlaps

This is where the biggest savings often hide. For example, paying for both Apple One and Google One storage or subscribing to Audible plus Kindle Unlimited. Multiple streaming services that aren't being fully utilized or having several AI subscriptions serving similar purposes will all be flagged.

In my case, ChatGPT immediately flagged a few subscriptions worth investigating because they provided similar benefits.

That doesn't automatically mean cancel them, but it does reveal where you're possibly paying twice for convenience.

Step 4: Ask ChatGPT tough questions

The real value comes from follow-up questions. Try prompts like:

"Which of these subscriptions would I miss the least?"

"If I canceled three subscriptions today, which would save the most money while creating the smallest impact?"

"Which subscriptions provide the lowest value per dollar?"

"Help me build a plan to reduce my spending by $25 per month."

I also took advantage of ChatGPT Tasks and set a reminder for ChatGPT to cancel trial subscriptions that had already started.

Step 5: Check your free trials

Speaking of free trials, one surprise from my audit was finding services that had started as trials and quietly became recurring expenses.

Try this prompt: "Based on my subscription list, which services are most commonly forgotten or underused?"

"What questions should I ask myself before renewing each one?"

ChatGPT can help you evaluate whether you're paying out of habit or because you genuinely use the service.

What surprised me most

Honestly, the total. Individually, most subscriptions felt small. Ten dollars here. Fifteen dollars there. Twenty dollars for an AI service I use every day. But when everything was combined, the total approached $2,000 per year.

That's enough to fund a vacation, boost retirement contributions or build an emergency fund. Taking the time to have ChatGPT solve my subscription crisis was a game changer. A simple ChatGPT conversation turned into a financial audit for my family by helping me identify recurring charges, spot overlaps and decide what stays and what goes. I should have done it sooner and plan on doing it every few months or so.

Have you ever used ChatGPT to help manage your finances? Let me know in the comments.

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Every spring, my local gardening Facebook page starts filling up with countless pictures of three-leaved plants, each one framed with the same question: "Is this poison ivy?"

It's a legitimate concern. Poison ivy is not only invasive, but can cause a severe allergic reaction if you get its oils on your skin. I've had more than enough rashes over the course of my life to look askance at any plant that even remotely resembles poison ivy.

Rather than post something on my local social network and wait for would-be horticulturists to reply, I've started using a new Google Gemini feature on my phone to find out more quickly if the plant I'm about to grab will leave me itching and scratching.

Here's how you can use Gemini to identify poison ivy (or any other plant, for that matter), works on both Android and iOS devices, and it takes just seconds.

Using Google Lens to identify plants isn't exactly the newest thing around, but as I've been going around my yard pulling plants, I've found it to be helpful in avoiding poison ivy. I have several other types of ivy and three-leaved plants, so it's been very useful while weeding. It's also come in handy if I'm out for a hike, and come across a new tree or shrub I haven't seen before.

So, just like Grampa's Weeder, this has become yet another important tool in my gardening kit.

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The most interesting thing happening in AI right now isn't that the models are getting smarter — though they are. It's that they're starting to work together. The question is no longer, should you quit ChatGPT and use Claude or Gemini instead, but rather, stacking models for the ultimate AI productivity package.

Some platforms even use AI to suggest the best AI for the job. But after living inside a workflow that connects Google's NotebookLM with Anthropic's Claude, I've started to think we've been asking the wrong question entirely.

I spent the past few weeks testing a setup that lets Claude reach directly into information stored inside NotebookLM, Google's AI-powered research and note-taking tool. Instead of manually shuttling research between two browser tabs, Claude can reference material I've already collected and use it to answer questions, draft content, and connect ideas across sources.

Here's how it works.

TL:DR

Connecting Claude with NotebookLM is simple. Under the hood, the bridge is something called MCP — the Model Context Protocol, an open standard for letting AI applications talk to outside systems.

While it sounds complex, it's essentially a universal adapter that everyday users can already access through the Claude for Desktop app or simple GitHub community plug-ins.

Why NotebookLM is different

If you haven't used NotebookLM before, the simplest way to describe it is an AI-powered research binder. You upload documents, articles, PDFs, transcripts and notes, and NotebookLM builds a knowledge base around them. It can also pull directly from the internet when prompted.

NotebookLM has caught on with students, academics, journalists and analysts because it easily ties specific materials together, particularly for research, rather than unsourced generalizations a typical chatbot might produce. In other words, NotebookLM is hyper-specific and focused, delivering citations and sources for deep research.

On its own, NotebookLM is already genuinely useful. I lean on it constantly to organize research, compress hundred-page reports into something readable and surface connections that even shift my mindset.

But it has a ceiling. NotebookLM is excellent at retrieval and summary, and noticeably weaker at the things that come after: deep reasoning, structural argument, nuanced drafting, anything that requires holding a problem in mind and turning it over.

But when Claude is activated with NotebookLM, the tool levels up exponentially.

Claude brings the reasoning layer

I was a big fan of Fable Five, even though it was short lived. But since putting NotebookLM with Claude, I feel as if I've gotten some of the power back. Connecting the two tools removes the need to go back and forth between the two for a seamless workflow. Now, rather than feeding Claude the same background over and over, I can point it at the research already organized inside NotebookLM and let it build from there.

Making this happen requires a digital translator called MCP (Model Context Protocol) — an open-source standard designed to help different AI architectures speak the same language. On their own, Claude and NotebookLM operate in total isolation; they have no native way to share data. The workaround relies on a lightweight MCP server acting as a middleman. It intercepts Claude's requests, securely fetches the relevant data from your notebook, and feeds it back to the model, completely automating away the need for the clipboard.

Worth saying plainly: the connectors that make this possible today are community-built and unofficial. Neither Google nor Anthropic has blessed the setup, and most of these bridges work by automating the NotebookLM interface rather than plugging into a sanctioned API. That puts the whole thing in a gray area you should weigh before pointing it at anything sensitive. You just have to understand that although the experience is remarkably smooth; the plumbing is still a hobbyist project, not a finished product.

Caveats acknowledged, the workflow itself feels startlingly natural. Instead of assembling context at the start of every conversation, the context is simply already there.

The ultimate research assistant

Most AI conversations begin from nothing. Every new chat is a blank slate that demands you rebuild the situation — re-upload the documents, re-explain the project, re-establish what you already told it yesterday. Even if the AI has memory of your work, you have to enable it to make that happen, then remember to disable it when you want more privacy.

With NotebookLM acting as the knowledge layer and Claude acting as the reasoning layer, that overhead mostly disappears. Here are a few concrete examples of what this setup has unlocked for me:

I have spent a lot of time researching data centers lately. With these tools, I could ask follow-up questions about a topic such as e-waste, without re-introducing a single source. I could ask Claude to compare the arguments in three different reports sitting in the same notebook and tell me where they actually disagreed — not where they used different words for the same point.

I could say, in effect, "draft a section based on what's in here," and get something grounded in my own material rather than the internet's averaged-out consensus. And because the answers traced back to specific sources, I could check the work instead of taking it on faith.

For the first time, I spent less time loading information into an AI and more time thinking alongside one that already understood it. For me, this subtle shift made a world of difference because I felt like I was really collaborating with AI.

What the setup actually takes

The community setup takes a little patience to install. I'm going to be honest that the setup to get these two tools together might be enough to scare users off. But, it shouldn't.

The issue is, there's no app store button for this yet, so you really do have to wire it together yourself, mostly through the terminal, and if that sentence made you flinch, know that several non-technical people I've compared notes with got it running in about fifteen minutes. Remember, you're copying and pasting commands, not writing code.

The setup is roughly this:

First you need Node.js installed on your machine, since the community connectors are published as small Node packages. Then you add one of those connectors — there are a few floating around GitHub, all doing the same job — by either pasting a single command into your terminal or dropping a few lines into Claude's config file so the app knows the server exists. (If you use Claude Code, you can hand it the connector's GitHub link and let Claude do most of the installation itself, which is a slightly surreal but very effective shortcut. It's how I did it).

The step that trips people up is authentication. The first time you run it, a Chrome window pops open on its own and asks you to log in to the Google account tied to your NotebookLM. The one piece of advice I'll share is: do not close that window!

Know that it appears suddenly and looks like a stray pop-up, but it's the whole handshake — shut it by reflex and you'll get a cryptic "authentication failed" error and have to start over. At the same time, if it closes, no biggie, just start over, but nobody wants that.

Finally, log in, let it finish, and the connection persists from then on. After a restart, Claude shows the NotebookLM connector as active. The way to confirm it's really talking is to just ask by having Claude list your notebooks and the number of sources in each. If it reads back your actual notebook names, you're connected.

A few honest caveats before you dive in

These connectors are unofficial, so they can break when the underlying tools update, and you may occasionally need to re-authenticate when a session goes stale. Most of them work by quietly driving a real browser in the background rather than plugging into a sanctioned API, which is smooth in practice but is exactly why I'd think twice before pointing this at genuinely sensitive material. It's a clever community hack, not a supported product — and it's good to know which one you're relying on.

And the oldest rule still holds: the output is only as good as how you've organized your sources going in. Dump everything into one giant notebook and you get mush. But, if you take the time to keep focused notebooks (one per project, research questions seperated, etc.) you'll notice everything feels sharper. When Claude can reason against a clean, well-scoped body of material, there's a big difference.

Final thoughts

Pair-programming your AI tools is one of my favorite ways to get more out of them. Until recently, we’ve treated these models as standalone islands. But when you wire them together, you get the best of both worlds — they push past their individual ceilings to create something entirely new.

And it's not just me doing this. Increasingly, power users are building interconnected systems. One tool stores and grounds the data, while another reasons over it to produce the actual work. (I often even throw in a third model to validate the output or format it into a presentation deck).

To be clear, this setup isn't going to replace human researchers or analysts anytime soon. The judgment of what to ask, what to keep, and what is flat-out wrong still sits entirely with the person at the keyboard. But changing the workflow from a single chatbot to an ecosystem has completely rewritten how I view the future of productivity.

Give this setup a try and let me know what you think in the comments.

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Forgive me for being extra late to the movement, but I was today years old when I learned about the “Anti-Prime Day” trend.

Simply put, it’s a period where non-Amazon retailers launch competing sales events with attractive discounts for all manner of items. Now that we’re smack dab in the middle of this year’s installment of Prime Day sales, I’m keen on checking out the abundance of sweet deals I can find.

As a fan of wearable devices, video games, action flicks, trading card games and books, I had a feeling that some of the retailers I frequent inside and outside my home could hook me up with the appropriate items.

So I made a beeline to ChatGPT and asked it what methods I should adopt to cash in on the most appealing Anti-Prime Day sales. Not only did it show me the best price history tools to use, but it also sent me to one of my favorite retailers to discover discounts that are better than some Prime Day sales.

Adopting the best Anti-Prime Day discount searching methods

After asking ChatGPT how to come out at the end of Anti-Prime Day sales with the best deals imaginable, it responded with this statement to put me on the right path:

If you're trying to find the best Anti-Prime Day sales, the goal isn't just to avoid shopping on Prime Day—it's to take advantage of the fact that almost every major retailer launches competing discounts during the same period.

Then it went on to list a bunch of competing online retailers that currently have a long list of amazing products being sold at good to great prices:

• Best Buy (Tech Fest Sale; Ends on Sunday, June 28)
• Kohl’s (Deal Days; Ends on Sunday, June 28)
• Cozy Earth (Cozy Day Deals: Ends on Saturday, June 27)
• Bugaboo (Summer Sale; Ends on Tuesday, July 7)
• Athleta (Semi-Annual Sale; Ends on Sunday, June 28)
• Walmart (Walmart Deals; Ends on Sunday, June 28)

The chatbot also made mention of what items tend to get discounted to their lowest possible prices during Anti-Prime Day sales. The ones that stood out to me from that list included physical video games, gaming accessories, headphones, anime box sets, graphic novel omnibuses, manga box sets, LEGO sets and 4K Blu-rays.

ChatGPT also recommended the perfect Anti-Prime Day setup to rely on, which primarily focused on using a bunch of efficient price history tools:

• Keepa for deep Amazon price history.
• CamelCamelCamel for quick verification.
• Slickdeals alerts for deal discovery.
• Honey for cross-retailer coupon and price tracking.
• Compare prices at Target, Walmart, Best Buy, and Barnes & Noble before buying.
• One useful rule: if a Prime Day deal isn't at or near the lowest price shown in Keepa or CamelCamelCamel, it's usually worth waiting for a better sale later in the year. Multiple deal experts and experienced shoppers use that exact approach to avoid fake discounts and inflated "was" prices.

The six Best Buy goodies I came across

Since my main focus during all those Anti-Prime Day sales was getting the best prices for a new smartwatch, a couple of video games, a collection of films starring an iconic spy and a set of trading cards I had been eyeing for a long time, I ended up on Best Buy’s digital storefront and found exactly what I wanted.

And what’s even better about these finds is how their discounts ended up being better than the ones seen on Amazon:

• Garmin - Forerunner 165 GPS Smartwatch: was $249.99 now $199.99 ($249.98 on Amazon)
• BRAVELY DEFAULT FLYING FAIRY HD Remaster - Nintendo Switch 2: was $39.99 now $19.99 ($49.99 on Amazon)
• STREET FIGHTER 6 Year 1-2 Fighters Edition - Nintendo Switch 2: was $59.99 now $29.99 ($49.95 on Amazon)
• Gothic 1 Remake - PlayStation 5: was $59.99 now $23.99 ($59.88 on Amazon)
• The James Bond Collection (24 Films) - Blu-ray: was $68.06 now $61.26 ($98.99 on Amazon)
• Wizards of The Coast - Magic: The Gathering Final Fantasy Starter Kit: was $19.99 now $14.99 ($19.99 on Amazon)

The takeaway

I owe it to my living under a cave at some point and not even knowing about Anti-Prime Day before this little ChatGPT experiment I conducted. Better late than never, I say!

My go-to chatbot helped me set up a winning game plan for this week’s digital sales from a variety of online retailers, use price history tools to compare all the items I had sitting on my Amazon wish list and find much better prices for those same items through Best Buy.

What started this year as a money-saving experiment has now morphed into an annual tradition for me and my wallet.

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Training and running AI models requires enormous amounts of computing power, and keeping those powerful chips cool often means consuming large amounts of water. It's become a growing source of concern as tech companies build massive AI data centers across the United States.

Now NVIDIA says it has a solution. The company recently unveiled a new liquid-cooling system designed for its next-generation AI infrastructure that it claims could reduce cooling water consumption to nearly zero in certain environments.

The announcement arrives as public scrutiny of AI's environmental footprint continues to grow, with communities questioning whether new data centers are placing too much strain on local resources.

So what exactly is NVIDIA proposing, and does it really solve AI's water problem?

Why AI data centers use so much water

AI systems run on specialized chips called GPUs, many of which are designed by NVIDIA. These chips generate enormous amounts of heat when processing AI workloads.

Historically, many data centers have relied on cooling towers that use water evaporation to remove heat. While effective, the process can consume millions of gallons of water annually.

As AI models become larger and more powerful, cooling demands have increased alongside them, turning water consumption into one of the most controversial aspects of the AI boom.

The issue has become especially sensitive in regions already facing water shortages or drought conditions.

NVIDIA's new approach

According to NVIDIA, its latest cooling system circulates warm liquid directly around AI hardware at temperatures reaching approximately 45°C (113°F).

Rather than relying on traditional cooling towers, the warmer liquid can be paired with dry coolers, which function more like large radiators and do not require significant water consumption.

NVIDIA says this could reduce facility cooling water use from roughly 2.6 million gallons per megawatt annually to nearly zero in favorable climates.

The company also argues that operating at higher liquid temperatures improves overall energy efficiency while creating opportunities to reuse waste heat elsewhere.

In other words, NVIDIA's goal is o redesign the entire thermal management process around water conservation.

The important caveat

The company's claims primarily address one specific category of water use: cooling. And while that matters, cooling isn't the only way AI consumes water.

Water is also used throughout the broader AI supply chain, including electricity generation, semiconductor manufacturing and data center construction.

Even if cooling water usage falls dramatically, AI systems still carry an environmental footprint beyond the walls of the data center itself.

Of course, there's also the question of scale. AI demand continues to grow at an extraordinary pace. Critics argue that efficiency gains can sometimes be offset by rapid expansion, meaning total resource consumption may continue rising even as individual facilities become more efficient.

Why this matters

The industry's largest companies are now under pressure to demonstrate that future AI growth can occur without placing unsustainable demands on water and energy resources. NVIDIA is not alone here — Microsoft has made similar zero-water cooling claims for its newest data centers.

NVIDIA isn't claiming that AI no longer has an environmental footprint. Instead, it's targeting one of the most visible concerns surrounding modern data centers: water-intensive cooling system

Whether NVIDIA's approach becomes the new standard remains to be seen, but it offers a glimpse of how the industry may attempt to address one of its most persistent criticisms.

More from Tom's Guide

• Anthropic will pay workers $85,000 to learn AI — and it reveals the next big AI job trend
• Anthropic's Fable 5 ban exposed AI's next big problem — but Sakana's Fugu may have the answer
• Is your job 'AI-resilient'? Find your risk score with our career calculator

As a longtime New York resident and admitted clean freak, I’ve walked into too many apartments that wouldn’t look out of place on an episode of Hoarders.

While there are plenty of cleaning companies that are willing to wade through all the junk in someone’s apartment to make it look all nice and new, one AI startup is willing to do the job for no cost at all. But like all things that sound way too good to be true, there’s a catch. And if you happen to be a New Yorker who has a less-than-fond opinion of AI, then this amazing deal won’t appeal to you whatsoever.

Here’s what this AI startup is asking for in exchange for tidying up your messy New York apartment free of charge.

That free apartment cleanup comes with an extra element you might not be expecting

The name of this newly introduced AI company is called shift, which specializes in gathering human data that’s then sent to AI labs and robotics companies to train said robots. It’s worth noting that shift is an offshoot of the Germany-based company microagi, which also operates in the data collection industry globally.

Late last month, the company released an introductory blog accompanied by a video on X that detailed its free New York apartment services. For anyone interested in receiving those services, they must agree to letting shift’s cleaners record first-person footage of their work and have it turned into the footage needed to serve as AI training data. Since its launch on May 28 of this year, shift says it has received thousands of booking requests thus far. And from a privacy standpoint, users of this cleaning service won’t have to worry—shift says that customers' most sensitive details will be blurred out on any footage used for AI training.

If your NYC apartment is dirtier than most, shift actually appreciates it as an FAQ on their site notes that “more challenging cleaning environments can be especially useful.” But there is still a limit to what its workers will do, as the site states that cleaners “may decline any specific task they are not comfortable performing.” Feel free to book your cleaning right here if you’d like.

The statistics behind shift’s AI training data collection mission show how successful it has been thus far. Over 20,000 operators have already been paid for the data they’ve created, $5 million+ has been paid out to those data contributors thus far, over 40 participating AI labs train on their sourced data and 14 countries have joined the company’s community of users.

The company hopes to expand its combination of cleaning services and human data collection for AI labs to other territories, as evidenced by a fan vote on its website. Places such as London, Munich, Zurich and California (which is currently leading the polls!) are currently being considered.

The takeaway

My apartment is pretty spotless (save for some dust bunnies that pop up at the corner of my eye every now and then). So the idea of a cleaning service has never really appealed to me, especially one that wants to collect human training data of said cleaning to further improve AI.

I salute shift for trying something different in a bid to collect AI training data, still. I’ll just watch from afar as my filthier friends and family members who reside in NYC get curious enough to use shift’s AI-connected cleaning services for themselves.

More from Tom’s Guide

• 21,000 jobs gone in a year: Oracle becomes the latest tech giant to cut workers due to AI adoption
• I hated spring cleaning until I tried using ChatGPT — and it made the job so much easier
• ChatGPT Tasks now feels like a real assistant — 7 ways I'm using it, including snagging the best Prime Day deals

I’m in my late 30s, but I never stop looking for ways to learn something new. Until recently, I didn't even know what ChatGPT was—that is, until a friend gave me a crash course in AI. Fascinated by its potential, I started testing how it could enhance my daily learning.

Since then, I've used ChatGPT as a personal fitness coach, music curator, travel adviser, and custom tutor. It completely transformed how I acquire new skills. Here are the three prompts I used to upgrade my learning experience, and how you can use them to master whatever you're curious about.

Prompting to increase my know-how

Let’s state the obvious: learning a new language is tough. I got a C in high school Spanish, and usually rely on translation apps just to order drinks on vacation. While I’m not bilingual yet, I’ve had a blast learning the basics of Spanish and Japanese using this roadmap prompt:

Prompt 1: The Language Roadmap "I want to learn [language] from scratch. Act as an expert instructor and curriculum designer. Assess what a beginner needs to know, identify the 20% of concepts that produce 80% of the results, and create a step-by-step learning roadmap. Break the roadmap into: Beginner, Intermediate, and Advanced. For each stage, include: Key concepts, Recommended practice exercises, Common mistakes, Milestones that prove mastery and Estimated time investment."

Even if you're smart, some topics are just too complex to wrap your head around. Back in college, a professor introduced me to the "Feynman Technique"—a method where you master a subject by forcing yourself to explain it in simple, jargon-free terms.

Whenever I need ChatGPT to break down complex issues—like how AI data centers impact the environment or why the cost of living keeps rising—I use this prompt to flatten the learning curve:

Prompt 2: The Feynman Technique "Teach me about [subject] using the Feynman Technique. Explain it as if I'm 12 years old. Then gradually increase the complexity. After each section, ask me a question to test my understanding before moving on."

I’m no handyman. When something breaks in my apartment, I usually give up and call maintenance. Lately, I've been trying to change that by using ChatGPT as a skill tutor (while double-checking its advice against legitimate sources, of course).

Whether I'm deep-cleaning my PS5, attempting to build a gaming PC, or trying to code, this interactive tutor prompt keeps me on track:

Prompt 3: The Socratic Tutor "Act as my personal tutor for [skill]. Your job is not to lecture me but to guide me. Ask me questions, identify gaps in my understanding, challenge my assumptions, and adapt lessons based on my responses. Do not move on until I've demonstrated understanding."

Bottom line

Bonus Prompt: The Masterclass Deep-Dive "I want to develop an expert-level understanding of [topic]. Don't just teach me facts. Teach me about what experts think about the topic, common misconceptions, contrarian viewpoints, important debates, first principles, mental models, historical context and future trends. Help me move from knowledge to wisdom by challenging my assumptions and encouraging critical thinking throughout the learning process."

The more you use AI, the better it adapts to your habits. By pairing ChatGPT with reliable outside sources, my learning sessions have become genuinely engaging instead of a chore. With these three prompts in my digital back pocket, picking up new skills and researching random topics has never been easier.

Put these prompts to work for yourself and see how much they change your workflow.

More from Tom’s Guide

• These 5 ChatGPT prompts ended my dinner planning problems — and introduced me to amazing new recipes
• Turns out AI was destroying my phone’s battery — here is the setting I changed to stop it
• I asked ChatGPT and Claude to plan my next dream trip to Japan with Viator — one delivered much better destinations

OpenAI has upgraded ChatGPT Tasks, finally giving the AI chatbot the ability to keep working after you've closed the app.

If you tried Tasks when it first rolled out, you may still think of it as more of a reminder feature. I'll admit, when I first tried it, I wasn't sure how useful it would be for me. You can ask ChatGPT to remind you about appointments, recurring chores or upcoming events. Useful enough, but I always felt as if my phone or Alexa was enough, not to mention, both have been doing that for years.

But the recent upgrade caught my attention because now ChatGPT can monitor the web and connected apps for changes, then notify you only when something worth rnoting happens. For someone who spends way too many hours glued to her computer, this update fundamentally changes the way I can use AI.

Instead of constantly checking websites, searching for updates or refreshing pages, you can hand the monitoring over to ChatGPT and let it do the waiting for you.

Getting started with ChatGPT Tasks

OpenAI says the new Scheduled Tasks feature is rolling out to paid users on Plus, Pro, Business and Enterprise plans. It's not available via the free tier yet. If you have access to the upgraded Tasks feature, you can create a task by describing what you want ChatGPT to do and when you want it to happen. In other words, you do not need to do anything else but simply type a prompt.

For those who would prefer a reminder option, OpenAI has also added a dedicated Scheduled page, available from the ChatGPT sidebar. From there, you can view all active tasks, see when they'll run next, edit task details, pause tasks temporarily or delete them altogether.

The most useful Tasks tend to be specific. Instead of asking ChatGPT to "watch Prime Day deals," tell it exactly what products, price thresholds or categories you care about. The more precise your instructions, the more helpful the notifications are likely to be.

Here are seven ways I'm already using it.

1. Monitoring Prime Day Deals

Prompt: During Prime Day, monitor deals on AirPods Pro, Kindle Paperwhite, Fitbit smartwatches and robot vacuums. Notify me only when a discount exceeds 25% or when a deal appears unusually good compared to typical sale prices.

Who am I kidding, it's Prime Day and I'm not just using one prompt. I'm also using this one: During Prime Day, monitor deals on LEGO sets, educational toys, noise-canceling headphones, tablets and school supplies. Notify me only when discounts reach 30% or more.

With these prompts or prompts like it, you're essentially telling ChatGPT Tasks, "I"m interested in this category. Don't bother me unless something genuinely good appears." That's why ChatGPT Tasks feels like an assistant.

2. Monitoring airfare without checking every day

One of the most annoying parts of planning a trip is repeatedly checking whether prices have dropped. If you're thinking about traveling to Japan or elsewhere this summer, this update is a real time-saver.

Instead, you can tell ChatGPT: Watch airfare from Newark to Orlando and notify me if round-trip prices fall below $250.

Rather than remembering to search every few days, the AI can keep an eye on things and alert you when it's worth paying attention. The alert comes in as an email. ChatGPT uses whatever email you have associated with your account, so be sure you're logged into your ChatGPT account when creating a Task.

3. Following major news or announcements

If you're following a major news story or want World Cup updates without constantly refreshing apps and social media, ChatGPT Tasks can notify you when something important happens. Instead of scrolling through endless headlines hoping not to miss an update, you can ask:

"Notify me when there are major developments in the Israel-Iran conflict."

"Alert me when the U.S. team advances to the next round of the World Cup."

"Let me know when breaking news occurs involving severe weather in my area."

This turns ChatGPT into a personalized news watcher. I use it all the time!

4. Keeping an eye on school and weather disruptions

Parents know how quickly schedules can change. One practical use for Tasks is:

"Alert me if local schools announce closures, delayed openings or emergency schedule changes."

You could also set up weather-related monitoring to stay informed about severe storms, snow days or other disruptions that affect your daily routine.

5. Catching important emails I may have missed

Email overload is real. If I'm expecting an important email, I always set up a Task such as "notify me when X sends me an email." I also use the prompt: "At the end of each workday, summarize important emails that have not received a response."

Instead of scanning through dozens of messages before bed, you get a quick summary of what might actually need your attention.

6. Planning weekends automatically

I love the idea of using AI to eliminate the mental effort of figuring out what to do with the family.

A simple prompt like: "Every Thursday afternoon, suggest family-friendly events happening nearby this weekend."

It can create a personalized list of activities without requiring any research on your part. And while it may seem small, once you use it, you realize it's a total game-changer to already have activities picked out by the time the weekend hits.

7. Creating a personalized morning briefing

One of the most practical uses for ChatGPT Tasks is building a custom daily briefing around the topics that matter most to you.

For example, you could ask: "Every morning, send me a briefing covering the latest technology news, weather, major headlines and anything important happening in my area."

Or: "Every weekday at 7 a.m., summarize the top news stories, stock market updates and sports headlines."

Instead of jumping between news apps, weather forecasts and social media feeds, you can start the day with a single summary tailored to your interests and then click on the stories that interest you.

This was a major update

ChatGPT Tasks was useful before this update, but the ability to monitor information on your behalf is what makes it feel like a real assistant. Instead of waiting for your next prompt, ChatGPT can now keep watch over the things you care about and notify you when something important changes.

It's not replacing your calendar or becoming a fully autonomous AI agent overnight. But it's one of the clearest signs yet that ChatGPT is evolving beyond a chatbot and into something far more useful for everyday life. Give it a try and let me know in the comments how you're using it — I may feature your suggestion in a story.

More from Tom's Guide

• Anthropic will pay workers $85,000 to learn AI — and it reveals the next big AI job trend
• That viral Jeff Bezos quote about AI and water isn't real — here's what actually happened
• Is your job 'AI-resilient'? Find your risk score with our career calculator

The headline-grabbing news that Anthropic is launching a $150 million “Claude Corps” fellowship to pay early-career workers $85,000 to learn AI highlights a massive shift in the labor market. It proves that the biggest hurdle facing tech giants right now is getting real-world organizations to understand how to actually use AI.

But beneath the surface of this massive hiring push is something worth noting: you don’t need a computer science degree to survive the ongoing AI transition. But what you do need is to understand how vulnerable your current career track is to wage stagnation.

The future belongs to "domain experts," otherwise known as people with deep knowledge of specific industries like education, healthcare or finance and who know how to work alongside large language models. If you refuse to adapt, your job might not disappear overnight, but your earning potential could quietly shrink as AI-literate peers optimize their output.

Test your career's AI resilience right now

Your long-term career security depends on a vital mix of your industry experience, your human-centric "soft" skills and your willingness to build basic AI fluency as studies show employees who use AI can make up to 40% more.

To see where your current role falls on the safety spectrum, enter your field and experience into our AI Career Resilience Calculator.

How to boost your score for free

If your calculator results show you have high exposure to automation, you do not need to panic, and you don't need to drop thousands of dollars returning to school for a technical degree. The absolute fastest way to build career resilience is to learn how to manage the AI tools disrupting your sector before someone else does.

For a closer look at the training programs tech companies expect applicants to know, you can check out this Anthropic Paid AI Fellowship Prep Stack Video which details the free prompt engineering resources and certifications you can use right now.

You can also build competitive AI literacy right now with tools like Masterclass and other free resources that I think are exceptional:

• Anthropic Academy: You can access 17 free courses and step-by-step prompt engineering interactive tutorials hosted directly on GitHub to learn how to structure instructions like a pro.
• Official AI Fluency Courses: Platforms like Coursera host official, beginner-friendly AI literacy certifications designed specifically for non-technical workers looking to upskill quickly.
• Build a "Human-in-the-Loop" Project: Look at the most repetitive task in your current daily routine —whether it's sorting spreadsheets, drafting emails or scheduling workflows, then and use a free LLM to automate it.

Bottom line

Anyone can say they've used ChatGPT. Far fewer people can point to a specific workflow they improved, a task they automated or time they saved. That's the kind of experience that stands out in interviews.

As AI becomes a standard part of the workplace, employers are increasingly looking for people who can use these tools thoughtfully rather than simply experiment with them.

Even a small project that saves time, improves accuracy or reduces repetitive work can demonstrate initiative, problem-solving skills and a practical understanding of how AI fits into real-world business processes.

More from Tom's Guide

• Anthropic will pay workers $85,000 to learn AI — and it reveals the next big AI job trend
• That viral Jeff Bezos quote about AI and water isn't real — here's what actually happened
• Turns out AI was destroying my phone’s battery — here is the setting I changed to stop it

For the past few years, the AI industry has been obsessed with building bigger, smarter and more capable models. Now, a new release from Sakana AI suggests the next AI arms race may not be about building the best model at all, but rather building the best system for managing multiple models.

As Anthropic's Fable 5 and Mythos models generate intense discussion across the industry about performance, capabilities and access, Sakana AI has introduced a new approach with Fugu.

The timing of this release is especially interesting as users are increasingly stacking models and utilizing several AI agents at once.

What is Sakana Fugu?

Unlike ChatGPT, Claude or Gemini, Fugu is not trying to be the smartest model in the room. Instead, it acts more like an AI project manager.

When a user submits a task, Fugu analyzes the request, decides which AI models are best suited for different parts of the problem, routes work to those models, evaluates the responses and combines the results into a final answer.

It's similar to a manager assembling a team of specialists instead of relying on a single employee. Because we all know one model isn't good at everything. Instead, one model might be better at coding, while another might excel at reasoning or writing. Simply put, Fugu's job is to determine who should do what and then stitch everything together.

According to Sakana AI's website, this orchestration approach allows the system to achieve performance comparable to leading frontier models without depending entirely on a single model provider.

Why this matters

Most people think of AI competition as a race to build the biggest and most powerful model, but Fugu points toward a different possibility. Instead of models attempting to outperform each other individually, what if the future belongs to systems that know how to combine multiple models effectively.

And while this concept is anything but new, what makes Sakana stand out is it has trained the orchestration process itself and made the routing intelligence the centerpiece of the product. In other words, it made the coordinator as important as the workers.

The lesson from Fable

The conversation surrounding Anthropic's Fable models highlighted something many organizations are beginning to recognize and that's relying on a single AI provider can create challenges. When access changes, outages occur, pricing shifts or capabilities evolve, entire workflows can be affected overnight.

Systems like Fugu are designed to reduce that dependency. Rather than building around one model, they build around an ecosystem of models. So now, if one model becomes unavailable, another can potentially take its place. If a better model emerges tomorrow, it can theoretically be added to the mix.

That flexibility could become increasingly valuable as the AI landscape grows more competitive.

The takeaway

Don't get me wrong: model size, benchmark scores and raw capability still matter. But Sakana's Fugu hints at a future where the most important question isn't "Which model is best?" but rather "Which system is best at choosing the right model?" Fugu suggests the next phase of competition may look very different. Instead of creating a single AI that does everything, the winners may be the companies that can assemble, coordinate and optimize entire teams of AIs behind the scenes.

If that's the direction the industry is heading, the next breakthrough might be an AI smart enough to know when not to answer the question itself.

More from Tom's Guide

• Anthropic will pay workers $85,000 to learn AI — and it reveals the next big AI job trend
• I asked Gemini what was slowing down my Windows PC — here’s what it found
• This new Chinese AI Is outperforming ChatGPT — and it runs locally

The headlines about major tech giants announcing massive layoffs due to AI are alarming.

Earlier this month, GitLab let 350 of its workers go in an effort to continue funding its AI infrastructure and get a better handle on the surge of traffic emanating from its AI workflows. And this past May, Meta laid off 8,000 of its employees as it continued to invest heavily in its AI initiatives. When you go on to include the likes of Google, Amazon, Cisco, Cloudflare, and others, you’ll begin to notice a sobering pattern—global and domestic tech companies are cutting tons of jobs as they look to further inject AI into their everyday proceedings.

The latest tech enterprise to join that growing list is Oracle, a multinational business that specializes in building data management systems, enterprise business software and cloud infrastructure for other major corporations.

The number of jobs that have been shed by Oracle is certainly a jaw-dropping statistic for many.

Oracle has cut 13% of its total workforce as it dives further into adopting AI

According to an annual report filed on June 22, 2026, Oracle has eliminated about 21,000 roles globally over the past year as it looks to restructure its day-to-day operations around AI. The company noted that it had approximately 141,000 full-time employees as of May 31, 2026, which is down from about 162,000 workers at the same time in 2025.

In that same filing, Oracle explicitly pointed to AI resulting in those shocking layoffs. “The adoption and deployment of AI technologies across our operations have resulted, and may continue to result, in reductions to our workforce,” the company stated. “These types of restructurings may also lead to shortages of sufficiently skilled employees in certain roles, loss of valuable institutional knowledge, and damage to employee morale and retention.”

Judging by a sobering report released earlier this year by consulting firm Challenger, Gray & Christmas, the news of Oracle’s AI-related job layoffs isn’t all that surprising. The firm pointed out how AI was responsible for over 50,000 job cuts in the U.S. in 2025. And since 2023, when AI started being tracked as one of the reasons for layoffs by the firm, 71,000+ job cut announcements have been tied to the controversial technology.

Other recognizable tech and finance companies have drastically reduced their headcount as they look to strengthen their AI usage. Dropbox, Block, Salesforce, and Cisco are among the many businesses that join Oracle as the latest firms to further embrace AI while cutting costs by shedding too many of their human roles.

Final thoughts

Waking up to the new depressing announcement of another massive corporation letting a shocking number of workers go due to AI is becoming all too commonplace. And judging by the current U.S. tech and finance business landscape, we should all be prepared to see even more of those headlines as the year rolls on.

More from Tom’s Guide

• Jeff Bezos predicts an optimistic future where AI won't lead to mass unemployment — 'AI is going to create a labor shortage'
• That viral Jeff Bezos quote about AI and water isn't real — here's what actually happened
• Anthropic will pay workers $85,000 to learn AI — and it reveals the next big AI job trend

Developers are flocking to a new open weight AI model that can be downloaded, customized and run entirely on local hardware. Released by Z.ai (formerly Zhipu AI), GLM 5.2 is turning heads by challenging a major industry assumption: that you always have to pay premium subscription prices to a tech giant to get frontier-level performance.

Unlike completely closed systems like ChatGPT or Claude, GLM 5.2 gives developers direct access to the model itself. This is important in an industry increasingly dominated by gated, corporate servers because it gives users much more control.

The AI industry is splitting into two worlds

Simply put, Open Weights = You get the model's brain. Open Source = You get everything (Model weights, training code, data processing pipeline, evaluation framework and often the training dataset). For years, open-source and open-weight AI models lived in the shadow of proprietary giants. They were highly flexible and much cheaper, but they invariably lagged behind the raw capabilities of OpenAI and Google.

Now, that gap is shrinking fast. With powerful models like Meta’s Llama family, Mistral, and now GLM 5.2, enterprises are highlighting that we may not need the most expensive AI model for every single task. Many businesses don't need a model that can solve world-class theoretical logic; they just need a system that can accurately summarize massive internal document libraries, autonomously write and debug code.

If an open model can complete 90% to 95% of those jobs at a fraction of the cost, then this type of model is impossible to ignore.

Why Local AI is a game-changer

The buzz surrounding GLM 5.2 spiked when developers successfully demonstrated the model running locally on high-end Apple hardware like the mac mini.

While the average person isn't buying multiple Mac Studios for their living room, the demonstration proved that capable AI can now be owned rather than "rented" with a subscription.

When you rely on a subscription, a third party controls the pricing, privacy policies and feature roadmap. Open-weight models flip the script. For industries handling sensitive financial data, medical records, or proprietary corporate research, keeping data completely in-house on private hardware is a massive security win.

Instead of relying on a single, expensive subscription, the future of business tech will likely look like a "mix-and-match" AI stack:

• A flagship closed model handles the absolute toughest reasoning problems.
• An open-weight model powers high-volume, routine workflows.
• A locally hosted model safely manages top-secret internal data.

The brutal reality check of local AI

If running a frontier-level AI on your own desk sounds like a dream, the physical requirements are where reality hits hard. GLM 5.2 is a massive 744-billion to 753-billion parameter Mixture-of-Experts (MoE) model. In its uncompressed form, its weights consume a staggering 1.51 terabytes of storage and memory.

For perspective:

• Standard High-End PC > Maxes out at 24GB VRAM > Hits a "VRAM Wall"
• Maxed Mac Studio > 256GB Unified Memory > Can run heavily compressed versions

To run GLM 5.2 locally, developers must aggressively compress it using a technique called quantization. Yet, even when heavily compressed, it requires roughly 240GB of memory just to load.

Furthermore, GLM 5.2 boasts a massive 1-million-token context window like Claude, meaning it can digest entire codebases or small libraries of books in one go. However, tracking that much data requires its own dedicated memory allocation. Push the model to its limits, and even the most powerful consumer desktop will start to sweat.

The takeaway for everyday users

If you aren't a programmer, this news remains relevant to the way AI fundamentally changes the software we use every day. While GLM 5.2 isn't going to replace the apps on your phone tomorrow, it does highlight that open models are becoming cheaper and fiercely competitive.

As software companies gain options and no longer have to pay massive fees to a single provider to add AI features to their apps, this shift could mean the next generation of digital tools will likely be cheaper, highly specialized and significantly more private.

More from Tom's Guide

• Anthropic will pay workers $85,000 to learn AI — and it reveals the next big AI job trend
• I asked Gemini what was slowing down my Windows PC — here’s what it found
• Anthropic's Fable 5 ban exposed AI's next big problem — but Sakana's Fugu may have the answer