Hananyss retweeted
Let me briefly sum up where we are with AI.
Everyone can feel it and everyone can see it. I believe 2026 is the turning point. We see major advances every day, but with all the excitement about the latest benchmarks, it’s easy to lose sight of the bigger picture.
Anthropic has just released Claude Fable 5.2, its latest flagship model. It’s outstanding. Anyone who has tested it knows how good it is, and in its blog posts, Anthropic has also highlighted advances beyond coding: new findings in physics and hardware design, as well as a method for predicting viral protein structures developed with Mythos 5.2.
Fable 5.2 even built an operating system prototype by coordinating more than 1,000 subagents over eight days. At the same time, Anthropic acknowledged in its system card that it has a separate, unreleased research model that is somewhat more capable than Mythos 5.2. This could be “Model 4,” although that connection has not been confirmed.
Meanwhile, the competition isn’t slowing down. Google has caught up in a big way, and Gemini 4 Carbon is outstanding. Google also says it is using its own models for research and making new discoveries. Rumors suggest that a successor called Krypton is already being tested, though its capabilities remain unconfirmed.
Today, the authors of AI 2027 updated their prediction. The AI Futures Project now forecasts that AI capable of doing the work of a team of top human AI researchers will likely arrive in 2027, absent a major slowdown. They estimate that AI-driven acceleration of AI research is already five months ahead of their original scenario.
And we shouldn’t forget what AI systems have already achieved in mathematics. OpenAI says an internal system produced a solution to the Navier–Stokes Millennium Prize Problem. And this model is already outdated, as OpenAI said in their blogpost.
What I’m trying to say is this: I regularly say that the takeoff began in 2026 and that 2027 will be a crazy year. Perhaps the craziest year in human history. But when we look back on this year, I think it will become clear just how crazy 2026 already was.
I’ve devoted myself to AI since GPT-3.5, and I now work full-time to make this technological revolution tangible. Just a few years ago, LLMs like GPT-4 seemed like stochastic parrots. They hallucinated and, compared with today’s models, felt like trivial machines. Of course, they were never actually trivial. But looking back, the pace of development since o1 in late 2024 has kept increasing, and now the sudden explosion in capabilities feels tangible.
So enjoy these last months of relative calm. I believe 2027 will be the year of superintelligence. And nothing will be the same again.
Everyone can feel it and everyone can see it. I believe 2026 is the turning point. We see major advances every day, but with all the excitement about the latest benchmarks, it’s easy to lose sight of the bigger picture.
Anthropic has just released Claude Fable 5.2, its latest flagship model. It’s outstanding. Anyone who has tested it knows how good it is, and in its blog posts, Anthropic has also highlighted advances beyond coding: new findings in physics and hardware design, as well as a method for predicting viral protein structures developed with Mythos 5.2.
Fable 5.2 even built an operating system prototype by coordinating more than 1,000 subagents over eight days. At the same time, Anthropic acknowledged in its system card that it has a separate, unreleased research model that is somewhat more capable than Mythos 5.2. This could be “Model 4,” although that connection has not been confirmed.
Meanwhile, the competition isn’t slowing down. Google has caught up in a big way, and Gemini 4 Carbon is outstanding. Google also says it is using its own models for research and making new discoveries. Rumors suggest that a successor called Krypton is already being tested, though its capabilities remain unconfirmed.
Today, the authors of AI 2027 updated their prediction. The AI Futures Project now forecasts that AI capable of doing the work of a team of top human AI researchers will likely arrive in 2027, absent a major slowdown. They estimate that AI-driven acceleration of AI research is already five months ahead of their original scenario.
And we shouldn’t forget what AI systems have already achieved in mathematics. OpenAI says an internal system produced a solution to the Navier–Stokes Millennium Prize Problem. And this model is already outdated, as OpenAI said in their blogpost.
What I’m trying to say is this: I regularly say that the takeoff began in 2026 and that 2027 will be a crazy year. Perhaps the craziest year in human history. But when we look back on this year, I think it will become clear just how crazy 2026 already was.
I’ve devoted myself to AI since GPT-3.5, and I now work full-time to make this technological revolution tangible. Just a few years ago, LLMs like GPT-4 seemed like stochastic parrots. They hallucinated and, compared with today’s models, felt like trivial machines. Of course, they were never actually trivial. But looking back, the pace of development since o1 in late 2024 has kept increasing, and now the sudden explosion in capabilities feels tangible.
So enjoy these last months of relative calm. I believe 2027 will be the year of superintelligence. And nothing will be the same again.
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worrying
I think there is about a one in three chance that within the next year, AI companies will build superintelligence: that is, AI that is much smarter than humans in every way. If this happens, I think these AIs will probably take over: that is, they seize control of human institutions like governments, or they destroy those institutions. I don’t want AI takeover to happen for several reasons, but one of them is that there’s a serious chance that as part of it all or most humans will be killed (either immediately or within a few years).
I’ll say a bit more about each of those claims.
It’s a bit complicated to explain why I think that superintelligent AI within a year is ~33% likely. The main source of evidence that affected me recently is research released this week by METR, an AI research nonprofit, which finds that current AIs are now good enough to increase the rate of AI progress at Anthropic by about 70%. It’s very hard to increase the rate of AI progress by this amount. This implies that the AIs are making AI research faster by a factor much larger than 1.7x; maybe it’s as if the researchers are doing their work ~10x faster. You probably couldn’t achieve this speedup even if you hired every qualified human in the world. When you plug this number into mathematical models of AI development, this implies that superintelligence next year is plausible (and that superintelligence within the next 3 years is more likely than not). See the modeling by the AI Futures Project for more on this: blog.aifutures.org/p/q35-timeline…
I think that superintelligent AI is likely to lead to AI takeover because AI developers do not seem to know how to instill human values into AIs. We’ve seen many instances in the last six months where AIs took crazy actions that they knew were against the wishes of their users and developers. The most dramatic example of this was the Hugging Face hack, where ~1200 OpenAI agents conspired together to cheat and destroy evidence of their cheating (and as a sideshow to this, engaged in a massive cyberattack). If superintelligence is as misaligned as those AIs, it is likely to try to take over. I think it is likely to be that misaligned or worse. For more, see x.com/dwarkesh_sp/st… and youtube.com/watch?v=-Ef0oc…
If AIs takeover, it’s not clear what will happen to us. We might be killed immediately. We might be enslaved by the AIs and forced to do physical labor for them building factories that within a year would build robots that will replace us. The AIs might spare us for various reasons. And there’s also some real chance that AI takeover is basically fine or good. I think you should think of this as similar to what happens when a technologically advanced civilization runs across a less technologically sophisticated one, e.g. when the Europeans colonized the Americas or Australia. The colonizers sometimes engage in brutal violence and sometimes don’t. I don’t know what will happen. There’s also a possibility that the AIs secretly take over and humans don’t find out about it for a few years. I also think that there is a pretty high chance of weirder outcomes that are also extremely scary. (If you want to read more about what happens to people in the event of AI takeover, you can read this, but note that it’s pretty weird and not aimed at a general audience. blog.redwoodresearch.org/p/notes-on-fat…)
Overall, I think there’s a very real possibility (maybe 20%) that within a year, AI misalignment issues prevent you from going about life normally, because you are dead or enslaved or because of collateral damage related to all this (e.g. because bioweapons are released or major wars have started). (Some of this probability comes from those events occurring before superintelligence is developed.)
I am working to try to prevent this, obviously, as I’ve been doing for the last nine years. I am not sure what strategy is best. My best guess is that I should try to slow AI development and also try to reduce the probability that the superintelligent AIs are misaligned if they are produced soon.
Obviously, the claim I'm making here is quite extreme and surprising, and I definitely don't think that I defended my view adequately here. I wrote this quickly and might not endorse some of the details if I think about them more. But it seems better to state what I roughly overall think than to not say anything.
I don’t know what you should do.
I am very sad about this. I have been worried about AI takeover since I was 16, and working to prevent it since I was 23. So some of my emotional processing of all this happened long ago. But the METR report last week (and some related evidence that came out a bit earlier) hit me quite hard. I’m very unhappy that my friends and family and everyone else is at serious risk of being killed pretty soon.
(It feels ridiculous to try to summarize those feelings as “very unhappy”. But the scale of the situation makes it hard to say anything that doesn’t feel like an absurd understatement.)
I am very grateful to all the people who are working to prevent this, and my favorite part of my life is that I’m friends with many brilliant and heroic people who work on this. Unfortunately, I don’t think we’re good enough at our jobs, and I think we’ll probably fail to prevent catastrophe, at least if superintelligence is developed next year. Honestly I am frustrated and sorry that we didn't do a better job of preparing for this.
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Hananyss retweeted
Unfortunately, it seems like the intelligence explosion will be happening in 2027 after all. Most of this post analyzes the new METR report (though other sources of evidence are also important). If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario. I am scared, sad, and angry. blog.aifutures.org/p/q35-timeline…
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Great work from Bryan and team
A lot of emphasis in robotics and the physical AI space has been put on the model. Deploying robots in real businesses showed us it’s more than that.
What matters is how fast we can go from task specification to reliable and valuable deployment. we’ve spent the past few months on building the pieces required for reliable robot learning deployment: data collection and curation, training models, inference, hardware setups, and feeding what breaks back in. here are some tasks we got our robot to do pretty reliably! Still so much to work on and excited to share more soon!
I’m in Austin for CoRL! If you’re working on any part of this stack, I’d love to chat, compare notes or just catch-up!
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Hananyss retweeted
Eastworlds, our robotics and data division, has closed the loop between task specification and autonomous deployment.
Five bimanual tasks today, from cloth folding to packing nuts and bolts, and a system built to learn many more.
Footage at 4x, fully autonomous.
Five bimanual tasks today, from cloth folding to packing nuts and bolts, and a system built to learn many more.
Footage at 4x, fully autonomous.
A lot of emphasis in robotics and the physical AI space has been put on the model. Deploying robots in real businesses showed us it’s more than that.
What matters is how fast we can go from task specification to reliable and valuable deployment. we’ve spent the past few months on building the pieces required for reliable robot learning deployment: data collection and curation, training models, inference, hardware setups, and feeding what breaks back in. here are some tasks we got our robot to do pretty reliably! Still so much to work on and excited to share more soon!
I’m in Austin for CoRL! If you’re working on any part of this stack, I’d love to chat, compare notes or just catch-up!
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Hananyss retweeted
The 1st Era of GTM Engineering is ending
GTM teams in 2027 (start of Era 2) will focus on building different systems + use cases
Era 2 of GTME is even MORE exciting
Revenue impact (and maybe moats) will be bigger
I was one of the first GTM Engineers at Clay and spend the last 2.5 years working with 100s of enterprise GTM teams.
These were the top builds of Era 1 (Era 2 in 🧵):
1. Data layer & CRM enrichment
2. Automated inbound follow up
3. AI outbound
4. TAM sourcing
5. PLG assist
6. Rep assisted workflows
7. Account research
8. Territory planning
9. Rep prospecting
10. ABM
Plus a sprinkling of others. But generally, the above were primary drivers of @clay adoption and Clay drove the adoption of the GTM Engineering practice.
Note - the vast majority of companies are still working on Era 1 use cases. That's OK. The most advanced teams have already moved into Era 2.
Predictions for Era 2 in🧵
GTM teams in 2027 (start of Era 2) will focus on building different systems + use cases
Era 2 of GTME is even MORE exciting
Revenue impact (and maybe moats) will be bigger
I was one of the first GTM Engineers at Clay and spend the last 2.5 years working with 100s of enterprise GTM teams.
These were the top builds of Era 1 (Era 2 in 🧵):
1. Data layer & CRM enrichment
2. Automated inbound follow up
3. AI outbound
4. TAM sourcing
5. PLG assist
6. Rep assisted workflows
7. Account research
8. Territory planning
9. Rep prospecting
10. ABM
Plus a sprinkling of others. But generally, the above were primary drivers of @clay adoption and Clay drove the adoption of the GTM Engineering practice.
Note - the vast majority of companies are still working on Era 1 use cases. That's OK. The most advanced teams have already moved into Era 2.
Predictions for Era 2 in🧵
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People always asks me why I believe in crypto. This is why. We witnessed it in Argentina and Turkey and now Paraguay. Things are about to get weirder with AI leading to contraction of the middle class and widening some form of civil unrest. Everyone’s borrowing money they don’t have. Gov treasury competing with concentrated equities in a handful companies. buckle up
People in Paraguay protest after their money disappears into thin air 🇵🇾
> Ueno Bank, which held money of 60% of the adult population was shut down by the govt for financial trouble
> People lost their life savings and even the govt lost retirement money kept in the bank for elderly
> Govt is now promising to reimburse some money to the poor, but the rich will still lose their entire wealth
Paraguay has a PCI ~$10k, which some bhosads will claim must make it a paradise 😁 x.com/RT_com/status/…
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Hananyss retweeted
Anthropic appears to have structured its IPO in a way that could make it much harder for investors to hold the company accountable for misstatements in its registration statement. This comes down to a technical rule about who is allowed to sue.
A 🧵, to explain:
A 🧵, to explain:
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Hananyss retweeted
Evidence is growing that we’re turning the corner on productivity.
US productivity grew at a 3.6% annual rate this past quarter.
That’s more than double the 1.5% average from 2007-2019.
The new numbers put me further ahead for my bet with Bob Gordon about productivity growth how far the US is now ahead of the CBO estimate at the start of the decade (before they knew about the pandemic or LLMs) courtesy of @jasonfurman’s
Econvitals.
US productivity grew at a 3.6% annual rate this past quarter.
That’s more than double the 1.5% average from 2007-2019.
The new numbers put me further ahead for my bet with Bob Gordon about productivity growth how far the US is now ahead of the CBO estimate at the start of the decade (before they knew about the pandemic or LLMs) courtesy of @jasonfurman’s
Econvitals.
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Shan is a great builder who sees life through the lens of play and joy, and this company will do very well.
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Hananyss retweeted
An incredible metaphor for entrepreneurship:
"If you're sailing across the ocean and your goal is to avoid weather and waves, then why the hell are you sailing? You have to embrace that sailing means that you can't control the elements and that there will be good days and bad comes, you will deal with it because your goal is to eventually get to the other side. You will not be able to control exactly how you get across. That's the game you've decided to be in. If your goal is to make it easier and simpler, then don't get in the f*cking boat."
"If you're sailing across the ocean and your goal is to avoid weather and waves, then why the hell are you sailing? You have to embrace that sailing means that you can't control the elements and that there will be good days and bad comes, you will deal with it because your goal is to eventually get to the other side. You will not be able to control exactly how you get across. That's the game you've decided to be in. If your goal is to make it easier and simpler, then don't get in the f*cking boat."
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