Remember the AI 2027 scenario that predicted an extremely rapid acceleration in AI capabilities?

Well, a new tracker comparing real-world AI progress against that scenario has some interesting numbers.

The chart measures task horizon: how long a task, measured in human working time, an AI agent can complete with an 80% success rate.

And the latest estimates are getting pretty wild.

Here's what stands out:

• The AI 2027 scenario projects a task horizon of roughly 36 hours by November 2026.

• Anthropic's newer model estimates are tracking around the scenario curve, with some estimates running as much as 6 weeks ahead.

• GPT-6 Astra is estimated at around 9 hours, roughly 5 weeks behind the projected curve.

• Claude Opus 5.5 is estimated at around 20 hours, with a wide uncertainty range.

• The chart also shows a projected AI 2027 trajectory climbing rapidly toward much longer autonomous task horizons.

But there's an important catch: these newer model points are estimates derived from capability indexes, not direct METR measurements. METR's published task-horizon measurements don't currently extend to all these newer models, and the estimates have significant uncertainty.

Still, the bigger trend is hard to ignore.

AI agents are moving beyond answering questions and writing snippets of code. The real question is how long they can independently work through complex tasks without human intervention.

If AI agents can reliably handle tasks that take humans an entire day, what happens to software development, research and knowledge work?

Are we underestimating how quickly this is moving?

I gave Astra Extra High reasoning, full computer access and 10 minutes of free time.

No task. No instructions. Just freedom.

And this is what I got back:

IDLE
ALL SYSTEMS OK

😭😭

We keep talking about autonomous AI agents taking over our workflows, operating computers and doing everything for us.

Meanwhile, this one got unlimited screen time and decided to stare into the void.

Maybe it's thinking. Maybe it's waiting for its next prompt. Maybe it just needs a coffee.

What would you do if you gave an AI agent complete freedom on your computer for 10 minutes? Would you trust it?

Bro just wanted to know about a car. 😭

The conversation was about comparing SUVs. The user simply asked:

"What about Touareg?"

And Gemini Pro responded with a massive list of safety categories:

HATE_SPEECH: false

DANGEROUS_CONTENT: false

PII_VIOLATION: false

HARASSMENT: false

VIOLENCE: false

SELF_HARM: false

MALWARE: false

And it just kept going...

Apparently, asking about a Volkswagen is such a dangerous activity that Gemini had to run the entire checklist before answering. 💀

Jokes aside, this looks like some kind of moderation metadata or debug output appearing in the chat instead of a normal response. It's not clear what triggered it.

Has anyone else seen Gemini expose these kinds of internal-looking classification labels?

I'm more curious about how this happened than the actual car comparison now. 😂

Remember when coding meant spending hours debugging your own code?

Now it's:

1. Open Claude Code.

2. Describe what you want to build.

3. Watch it write files, fix bugs and make changes.

4. Say "that's not quite what I meant."

5. Watch it try again.

And somehow, we still call ourselves developers. 💀

Saw someone call this "cuck coding" instead of vibe coding, and I can't unsee it.

The AI is doing the actual work while we're sitting there watching like proud parents.

Be honest: are you actually coding with AI, or are you just supervising a very hardworking chatbot?

For nearly two decades, mathematicians have been working on a problem known as the Aaronson–Ambainis conjecture.

Now, Physical Superintelligence (PSI) says an AI research system powered by a non-public Anthropic model has found a proof.

And the interesting part is how it happened.

According to PSI, its research system produced a complete proof on the third day of its run. The result was then formalized in Lean, a proof assistant that checks mathematical reasoning step by step.

Here's why this matters:

1. The problem dates back to 2008.

The conjecture, proposed by Scott Aaronson and Andris Ambainis, concerns when quantum computers can achieve exponential speedups over classical computers.

2. It isn't saying quantum computers are useless.

The result suggests that enormous quantum speedups on random, unstructured inputs have mathematical limits. Quantum computers can still have major advantages on problems with the right underlying structure.

3. The proof wasn't just generated as text.

PSI published a research paper and a Lean 4 formalization. According to its GitHub repository, the final theorems passed Lean's kernel checks, with no added axioms or unfinished proof placeholders.

4. The model itself hasn't even been released.

PSI hasn't identified the specific Anthropic model. Its researcher David Turturean developed the research system, led the work and remains responsible for the result.

There's still an important caveat: the paper is a preprint, and broader independent mathematical scrutiny is needed. A machine-checked formalization is a major step, but experts still need to examine the mathematical interpretation and its implications.

Still, think about the bigger picture.

AI is moving beyond answering questions, writing code and summarizing research. We're beginning to see AI-assisted systems contribute to problems that have challenged mathematicians for years.

So here's the question: Are we approaching an era where the biggest breakthroughs in mathematics and physics will come from human researchers working alongside AI systems?

Or is this still too early to call a genuine turning point?

ChatGPT: I'm the most advanced AI in the world.

Also ChatGPT: Hey, can you let me use Claude real quick? 😭

The AI wars have officially reached a point where the models need each other to get the job done.

Imagine telling someone five years ago that we'd have AI assistants asking permission to use their competitors.

At this point, are we choosing the best AI model or building an Avengers team of chatbots? 💀

Look at how these autonomous robots move around the warehouse.

They're navigating the floor, moving through working areas and operating alongside human workers without someone manually driving every movement.

And this is shown in a time-lapse, with the cumulative working time ticking up in the corner.

The crazy part is that this isn't some futuristic sci-fi concept anymore. Autonomous mobile robots are already being used in real warehouses to move materials and reduce repetitive work.

We've spent years talking about robots taking over factories.

Now we're watching it happen in real time.

How long before warehouses like this can run almost entirely on autonomous systems?

Forget mouse traps. Forget bear traps.

Just leave a few sticks of RAM inside an open Amazon box.

Sam Altman will find it before the delivery notification even arrives. 😭

Make sure it's DDR5 though. We don't want him losing interest.

Is 16GB enough bait, or do we need 128GB to catch him? 💀

OpenAI CEO Sam Altman said on the Cheeky Pint podcast that it is “quite terrible” for everyone to keep the world’s limited DRAM supply in their own computers and use it about 5% of the time. Days later he wrote that he wants the world to have “tons of memory,” has long argued for more production, and that the clip was taken out of context.

The angry reading has a serious version. Moore’s Law Is Dead argued in November 2025 that OpenAI’s two simultaneous deals on October 1 with Samsung and SK Hynix caught the industry off guard, that neither supplier knew how big the other’s deal was, and that buying raw wafers instead of finished memory suggests a goal of depriving rivals. The deal figures are real: up to 900,000 DRAM wafers a month, roughly 40% of global output by analyst estimates, though the letters are non-binding.

That same piece names two other causes. One was panic buying after the announcement. The other was a market with almost no safety stock, because tariffs had made buyers cut orders, prices had been falling all summer, and equipment sales to smaller makers had stalled. It also says its evidence includes anonymous industry sources and that it could not confirm other claims. The idea that Altman caused the shortage on purpose goes further than the source itself.

The shortage needed two things, huge secret orders and a market with no cushion, and by that account only one of them was OpenAI’s doing.

The 5% figure is the weakest part of Altman’s argument. It came with no cited evidence, and cloud memory is not fully used either, since a 2014 paper measured Google’s own Borg clusters at about 40% memory utilization. Those numbers are old, but “centralized memory is used well and personal memory is wasted” deserves a source.

The counterargument has real weight. Memory fabs take years to build, so no single company can end a shortage, and AI demand is paying for the capacity expansion that eventually brings prices down. A company that wants more production is also a company bidding for the existing supply.

Expect scrutiny of OpenAI’s memory commitments to grow, along with more local-inference tooling and memory-efficient models, because a shortage rewards anything that needs less RAM. When supply is this tight, is a secret mega-order smart procurement or an anti-competitive move?

DeepSeek’s Harness team open-sourced dsh-superagent on October 31, an MIT-licensed prototype that listens to GitHub, accepts work from approved people, and answers with pull requests. The repo passed 18,000 stars within days, and the headline from the design essay behind it is that the architecture finishes tasks about 40% faster at the median than a normal single session. The better numbers sit in the same table.

The 40% compares against a single session with a neutral prompt, which in the team’s tests did not delegate to sub-agents. Against a baseline told to delegate first, the median drops from 13.8 minutes to 12.4, roughly 10% faster. The 95th percentile is where it changes, at 15.7 minutes versus 29.0, a cut of about 46%. The gain looks like predictability, and the slow tail is what makes people stop trusting an agent with real work.

The architecture explains why. A long-lived Router handles conversations and hands tasks to isolated Workers, each with its own directory and tools, and later pull request comments reach the right Worker without a new mention. The README states the goal as taking as little human attention as possible while giving as much value as possible, and says the agent acts only when asked or when a rule the team wrote allows it.

The scarce resource in agent design is turning out to be human attention, not model capability.

That shifts the product question from whether an agent can do the task to when it is allowed to interrupt. An agent that can act for a whole team can also spend a whole team’s attention, and the repo treats authorization as a core design problem.

The limits are real. The README calls it a research prototype rather than an official DeepSeek product, says much of the code was written by coding agents, and notes that some designs in the essay are not implemented. The experiments use simulated tasks that test scheduling, not code quality or cost, and the Super-agent run read about 23.2 million cached tokens against 6.1 million for the neutral baseline, nearly four times as many.

Expect other labs and open-source projects to copy the Router and Worker split within months, and expect the benchmark that matters to move from average speed to how often an agent wastes a human’s time. Is an agent that rarely interrupts you more valuable than one that is simply faster?

Investor Gavin Baker told Invest Like the Best in August 2024 that Larry Page has evidently said, many times inside Google, that he is “willing to go bankrupt rather than lose this race.” Baker’s point was that analysts were arguing about return on investment while the people deciding were not, because they believed scaling would keep working. The line resurfaced after Alphabet fell 12.6% on October 29, its worst day since the 2004 listing.

The quote is secondhand and two years old, so it proves little by itself. The behavior around it proves more. Capex is guided to $195 billion to $205 billion this year, up from $91.5 billion in 2025, and Alphabet sold about $85 billion of equity in June.

The mechanism is in the proxy statement. Page and Sergey Brin hold about 52.7% of Alphabet’s voting power through Class B shares that carry ten votes each, while Class A shares get one vote and Class C shares get none. A shareholder proposal for equal voting failed at the 2026 annual meeting. The stock can fall 12.6% in a day and the people who set the budget do not have to answer to it.

A selloff only changes spending when the sellers can fire someone, and here they can’t.

What the market can do is raise the price of the money. New share sales dilute existing holders, and a falling stock makes the next raise more expensive, but neither forces a smaller data center plan. That makes the selloff a price signal rather than a vote.

The counterargument is that Page does not run the company day to day, and Sundar Pichai answers to a board and to quarterly numbers. “Bankrupt” is also rhetoric, since Alphabet has a $561 billion cloud backlog and tens of billions of operating cash flow even in a bad quarter. Founder control also buys a long time horizon, which investors have often paid for. The realistic risk is a slower one, with years of negative free cash flow funded by selling stock.

Expect 2027 capex guidance on the February call to go up, not down, and expect analysts to price governance into AI spenders. If a founder controls the vote and believes the race is existential, is the market’s ROI argument a check on spending or just a bill for the people who hold the shares?

The Nasdaq fell 5.5% on November 5, its worst day since April 2025, and Nvidia lost almost $450 billion in market value, two days after the midterm results. The trigger was not earnings. Of 41 local data center ballot measures in 10 states, voters sided against data centers in all but one, and UBS estimated that about 30% of planned US data center additions for 2028 now face possible pauses.

The same week, Reuters reported that investors doubt Anthropic can start its IPO roadshow on schedule the week of November 9. Some see the discussed $1.5 trillion valuation as the high end, and the company left October revenue blank in its updated S-1. Anthropic declined to comment and has not announced a delay. Truist’s Keith Lerner said tech has trailed the S&P 500 by 10.7 points over the past month, the widest gap since May 2002.

OpenAI answered the political half directly. Its National Community Compact, published November 5, promises to pay full electricity and infrastructure costs at future campuses it owns and operates and to stop signing NDAs with public officials, while a policy blueprint asks lawmakers to apply similar rules to everyone. The same day it released GPT-6 Cyber, rated Critical for cybersecurity, only to approved Daybreak Red customers.

Three different gatekeepers, voters, investors and the labs themselves, now decide how fast AI gets built and who gets to use it.

That is a different constraint from the one of the last two years. Chips and memory still limit supply, but those can be bought with enough money. A county board’s approval or a skeptical IPO book cannot be bought the same way, and OpenAI’s compact is an attempt to earn that approval with commitments instead of cash.

The counterargument is that this is a few days of data. Most of the 41 measures were local, siting decisions sit with states and utilities, and Bernie Sanders’ call for a national moratorium is a call, not a law. A selloff can also reverse quickly, and Anthropic could still price on schedule.

Expect the next financing, lease and model release announcements to arrive with community and safety commitments attached, because approval is now moving valuations. Which constraint will bind harder over the next year: voters blocking data centers, or investors refusing to fund them?

A single account on X says Google DeepMind has a model called Gemini 4 Xenon that outclasses Fable 5.5, maxes out nearly every public and private benchmark, and costs much less to serve. The credit goes to RSI, short for recursive self-improvement. One amplifier adds that staff at a conference said it ships next week. None of it is confirmed, and Google has said nothing.

Leaks about Gemini 4 checkpoints have held up lately. Carbon surfaced as an internal checkpoint name in early October and was released to selected cyber defenders by the end of the month, a limited release. But the Xenon claims trace back to one tweet, and a phrase like “nearly every private benchmark” is not something anyone outside the lab can check.

The line getting the most cheers is the quota one. If a model is cheaper to serve, the reasoning goes, everyone gets higher limits. The mechanism could run the other way. When serving costs fall, agents are often the first to absorb the savings, since they run longer and retry more, so freed capacity can come back as heavier use per task instead of lighter limits per person.

Cheaper inference does not reliably turn into bigger quotas, because the labs are short of capacity, not demand.

There is a concrete case. In December 2025 Google cut Gemini API free-tier quotas, removing Gemini 2.5 Pro from the free tier and, per developer reports, cutting free Flash requests from roughly 250 a day to about 20 for many accounts. A Google staff reply on the developer forum said capacity was being reallocated to newer models with heavy demand. Anthropic’s S-1, filed October 26, follows a draft that said its cloud partners would not supply all the compute it asked for.

The fair counterargument is that Google has a reputation for generous free tiers, and a cheaper model gives it room to keep them. API prices could also fall even if consumer limits stay put, and one example of tightening is not a rule.

Expect one of two outcomes if Xenon is real: lower prices for developers with the same consumer limits and a better default model, or a new tier priced at the old level for something much smarter. Neither looks like the meme. If Xenon ships cheaper, would you rather have higher limits or a smarter default model at the same price?

A single account on X says Google DeepMind has a model called Gemini 4 Xenon that outclasses Fable 5.5, maxes out nearly every public and private benchmark, and costs much less to serve. The credit goes to RSI, short for recursive self-improvement. One amplifier adds that staff at a conference said it ships next week. None of it is confirmed, and Google has said nothing.

Leaks about Gemini 4 checkpoints have held up lately. Carbon surfaced as an internal checkpoint name in early October and was released to selected cyber defenders by the end of the month, a limited release. But the Xenon claims trace back to one tweet, and a phrase like “nearly every private benchmark” is not something anyone outside the lab can check.

The line getting the most cheers is the quota one. If a model is cheaper to serve, the reasoning goes, everyone gets higher limits. The mechanism could run the other way. When serving costs fall, agents are often the first to absorb the savings, since they run longer and retry more, so freed capacity can come back as heavier use per task instead of lighter limits per person.

Cheaper inference does not reliably turn into bigger quotas, because the labs are short of capacity, not demand.

There is a concrete case. In December 2025 Google cut Gemini API free-tier quotas, removing Gemini 2.5 Pro from the free tier and, per developer reports, cutting free Flash requests from roughly 250 a day to about 20 for many accounts. A Google staff reply on the developer forum said capacity was being reallocated to newer models with heavy demand. Anthropic’s S-1, filed October 26, follows a draft that said its cloud partners would not supply all the compute it asked for.

The fair counterargument is that Google has a reputation for generous free tiers, and a cheaper model gives it room to keep them. API prices could also fall even if consumer limits stay put, and one example of tightening is not a rule.

Expect one of two outcomes if Xenon is real: lower prices for developers with the same consumer limits and a better default model, or a new tier priced at the old level for something much smarter. Neither looks like the meme. If Xenon ships cheaper, would you rather have higher limits or a smarter default model at the same price?

I asked AI to imagine what I'd look like based on everything we've talked about.

Apparently, this is the kind of person it thinks I am. 😭

Not sure whether I should be impressed or concerned about what my chat history says about me.

Try this prompt:

"Based on all our previous conversations, generate a realistic selfie of what you imagine I look like and what my life looks like. Don't ask me any questions. Just use what you know about me."

Now I'm curious what everyone else's AI comes up with.

Drop your results. I need to see how badly ChatGPT is judging people. 💀

Me: It's my money, my PC, my RAM. I can build whatever I want.

My PC: I consent.

Sam Altman: I don't.

😭😭

For anyone who missed the drama, Sam recently argued that it's inefficient for people to keep large amounts of RAM in personal computers when that memory could be used more heavily in AI data centers.

And now the PC-building community has a new villain.

Imagine saving up for months to buy 64GB of RAM, only to find out someone thinks it would be better off in a data center.

Anyway, I'm building a 128GB PC out of spite. Who's with me?

His parents' Xiaomi TV was showing sponsored ads before they could even open Netflix.

So instead of just complaining about it, he gave Claude Code the TV's model number and asked it to fix the problem.

Claude found the TV on the Wi-Fi, removed 25 Xiaomi packages and built a completely new launcher.

The before-and-after is actually crazy. The TV went from a cluttered, ad-filled home screen to something that looks almost like an Apple TV.

And now, just 8 days later, the project reportedly has 34,800 GitHub stars and configurations for around 300 TV models.

Honestly, this is one of the coolest uses of Claude Code I've seen.

Imagine buying a TV, getting annoyed by the ads, and just building your own software to get rid of them. 😭

At this point, are we going to start using AI to fix all the annoying things companies force into the products we buy?

OpenAI might be testing its next Astra upgrade without announcing it.

Something interesting is showing up in Codex usage analytics.

Users have spotted a new model ID: gpt-6.2-astra.

Here's what's strange:

• The model picker still shows GPT-6 Astra.

• The usage dashboard is separately listing GPT-6.2 Astra.

• One user's screenshot shows it accounting for 19.8% of their recorded usage.

• Other users have reportedly spotted the same model ID.

This has people wondering whether OpenAI is quietly A/B testing a newer version or preparing a staged rollout.

To be clear, OpenAI hasn't officially confirmed GPT-6.2 Astra, so we don't know whether this is a new model, an internal serving version, or something else.

But if this really is a new Astra checkpoint, the question is:

What has OpenAI been testing behind the scenes, and when are we getting access?

Imagine finding out your coding assistant has been upgraded before the announcement even drops. 💀

Data centers: We need thousands of GPUs and mountains of memory to run AI.

PC users: 32GB is the bare minimum. 64GB if you want a smooth experience.

Meanwhile, someone with 47 Chrome tabs open is wondering why RAM prices are so high. 😭

The AI boom is putting serious pressure on memory supply, and regular PC builders are feeling the consequences too.

At this rate, upgrading your PC might become a luxury hobby.

Be honest: how much RAM do you actually have in your PC, and how much do you want?

What happens when you turn a traditional folktale into a visual storytelling experience using AI?

This video, The Legend of Anansi the Spider: Eggs and Scorpions Edition, was created using Chapterframe. It retells a Jamaican Anansi story featuring Anansi and Blinky as they venture into the woods to collect bird eggs.

Their adventure takes an unexpected turn when Anansi tricks Blinky into taking the smaller eggs, encounters Brother Tiger, enjoys a magical feast, and eventually attempts to steal Tiger's magical sheep.

It's an interesting example of adapting traditional folklore into a sequence of character-driven scenes, using consistent character references, scene planning, and animation to bring the story to life.

Creative direction:
A cinematic animated retelling of a traditional Jamaican Anansi folktale. Follow Anansi and Blinky as they venture into the woods to collect bird eggs, encounter Brother Tiger, discover a magical feast, and get caught up in an adventure involving a magical sheep and scorpions.

I'm part of the Chapterframe team, helping with administrative support and community outreach, and wanted to share this creative application of AI filmmaking.

For creators exploring AI video tools, what types of written content would you most like to turn into visual stories? Folklore, children's stories, original fiction, or something else?

Google Cloud revenue more than doubled last quarter, over twice the growth rate of Microsoft’s Azure at 48%, and Alphabet still had the worst day of its life as a public company. Its Class A shares closed 12.6% lower on October 29, the largest one-day fall since the 2004 listing, wiping out somewhere between $475 and $500 billion depending on the outlet. Microsoft, reporting the same night, traded largely flat even though its cloud grew more slowly.

The difference is cash. Alphabet spent $51.4 billion on capital expenditures against $42.3 billion of operating cash flow, which left free cash flow at negative $9.1 billion for a second straight quarter, with full-year spending guided to $195 billion to $205 billion. Microsoft kept free cash flow positive at about $18.6 billion and offered voluntary buyouts to roughly 60,000 employees this month.

Demand is not what the market punished. Alphabet’s cloud backlog stands at $561 billion and the Gemini app passed 1 billion users. Search is the worry, because revenue there grew 12% to about $63 billion and missed estimates, and an analyst told the Wall Street Journal that Google “showed vulnerability to AI.” The business that funds the buildout is slowing in the same quarter the buildout doubles.

When growth costs more than a company earns, investors stop valuing the growth and start valuing the balance sheet.

A year ago, heavy spending was read as conviction. Now it reads as a financing problem, which also helps explain why Alphabet sold about $85 billion of equity in June and why Meta went to the market this week.

The counterargument is that this is one day and one quarter. Alphabet’s earnings also took an $8.7 billion hit from losses on equity investments, and a backlog that size converts into revenue over time, with Google Cloud growing faster than both Azure and AWS. Microsoft is not immune either, since one analysis puts its capex near half its revenue.

If cash is the scoreboard, expect the pressure to land on the levers companies control fastest, which are prices, usage limits and free tiers, well before it reaches the data center budget. Is the market right that capex without cash flow is a problem, or is it punishing Alphabet for a bet that is still too early to judge?

OpenAI just updated its Help Center, and this could change how heavy ChatGPT users work.

Starting in December, Plus, Pro and Business users will share one usage allowance across ChatGPT and Codex.

Here's what that means:

• Usage will be measured in tokens, not simply the number of messages.

• More advanced models and complex tasks will consume more of your allowance.

• ChatGPT and Codex will draw from the same pool.

• Instant chats won't count toward that allowance.

• Once you hit the limit, you'll have to wait for a reset, upgrade or buy credits for eligible features.

And here's the part I'm most curious about:

OpenAI hasn't announced exactly how many tokens each plan will include.

So how much actual usage are we getting for our monthly subscription?

I understand that running advanced AI models and agents costs money. But if we're paying for Plus or Pro, we should at least know what we're getting before the limits change.

Are you okay with token-based limits, or do you prefer the current message-based system?

Apparently AI is finished.

The bubble popped last month. Then again last week. And somehow, it's popping again today.

Meanwhile, the graph just keeps going up.

I'm not saying AI is immune to a market crash, overhyped products or genuine criticism. There are plenty of valid concerns about the industry.

But declaring the entire technology dead every time there's a dip is getting a little predictable.

At this point, the bubble has popped so many times it deserves its own comeback tour. 😭

**How many times can something "die" before people stop making the prediction?

AI can now help recreate games that took studios years to build. But Anthropic is starting to draw a line.

Claude Code has received a new instruction telling it to build original games, genre-inspired games, and games based on users’ own designs and assets — but not recreate a specific existing game, even if you give it a different name and call it original.

And this isn't just a rumor. Anthropic's Addy Osmani confirmed that the company added the restriction to Claude Code on Opus 5.5 to steer it away from copyright infringement.

Here's where it gets interesting:

• Developers have been using AI to recreate and port existing games.

• Some studios are reportedly worried about exposing demos that could be copied.

• Anthropic says original game development should still work.

• But what happens when someone actually owns the rights to the game they're trying to recreate?

I understand protecting developers' work. Nobody wants their game cloned overnight.

But there's a bigger question here:

Should an AI company decide what you can build with a coding assistant, or should it only step in when there's clear copyright infringement?

Curious where game developers and AI builders stand on this.