Funny how they include nvidia, micron, and AMD revenue as “AI revenue” and that it represents that majority of industry revenue but presumably a big chunk of everyone else’s spend. Almost might as well include electric utility revenue as AI Revenue by that metric
AI is more like an ongoing research project than it is a product. GPUs are currently the product that is profitable and Nvidia will keep funding labs to keep buying GPUs.
Yes, they said that about a lot of companies that dumped product until the competition was eliminated, then abused their monopoly position and resultant political connections to do whatever they wanted. Their sites are worse than ever. Literally worse than the day after launch.
This is exactly how Cloud Computing looked in 2012-2018. Dumping huge $ into computing buildout that wasn't profitable yet. All those co's: Amazon, GCP, Azure paid off immensely and are ridiculously profitable.
No, it wasn't. Cloud computing was almost immediately profitable.
And Amazon was famously "unprofitable" for their first 9 years because they were investing all their very real profits into a form of capital that the US tax code didn't recognize.
You are being downvoted but only a few years ago Google contemplated whether to kill GCP altogether because of lack of traction in the market or revenue goals.
IIRC Amazon chose to reinvest early revenue to grow AWS intentionally, and the revenue curve eventually evened out and obviously surpassed expenses. The problem with the AI buildout is that there's not a ton of evidence that these companies are approaching profitability, we can't even know because they're private.
Amazon's incubation of AWS was methodical and transparent. OpenAI is saying that they don't expect to be profitable until at least 2030, with over a trillion dollars in committed spend before that point. It's the largest "trust me bro" play in human history.
My main issue with this timeline is that AI still has trouble transitioning to the real world. It predicts for 2029:
> There are swarms of insect-sized drones that can poison human infantry before they are even noticed; flocks of bird-sized drones to hunt the insects; new ICBM interceptors, and new, harder-to-intercept ICBMs. The rest of the world watches the buildup in horror, but it seems to have a momentum of its own.
Does anyone really predict insect drones _in production_ 3 years from now, to the degree that we need bird drones to hunt the insect drones? How the hell are these things powered?
Lean/math/millenienium prizes are "grindable" [0]. Wake me up when AI is making order-of-magnitude improvements in ungrindable real world tasks like batteries, hypersonic engine manufacturing, and stealth/silent motors that you can't hear.
> Does anyone really predict insect drones _in production_ 3 years from now
The industrial expansion timelinen as described in AI 2027 is way too compressed; I don't think any AI doomer believes that. The dynamics are plausible though, even without China stealing the weights.
What theft? Modern humans learned from the works of past humans, LLMs learned from the works of modern humans, other LLMs learned from those LLMs. Given that the methods by which Anthropic and OpenAI obtained training data remains a legal gray area, it's not so cut and dry to me that distillation is stealing. It's just a continuation of the long tradition of building knowledge on the knowledge of others.
Please elaborate: what dynamics? This is rather vague. My point is that AI can't grind real world physics/chemistry/engineering. What dynamics are in play here?
This doesn't solve grinding? If it did, we'd see results by now (Published: 27 January 2026), and there's months of delay between submission and publication.
I'm willing to be wrong, but I'm just not seeing anything worth doomering over. There are multiple companies throwing AI at materials discovery; a research paper about a "data-driven framework" is about as unthreatening to my thesis as it gets. I'm willing to cede the point if say, Radical releases ~3 new materials that have commercial applicability and ~3x some useful metric e.g. tensile strength, but until then, to my amateur eye, it looks like AI+Real World is missing its ChatGPT moment.
Gonna note that biology is even less grindable than ordinary chemistry/physics/engineering. We'll have advancements in other real-world fields before AI-powered bioengineering becomes A Thing.
I think the idea is that AI itself is going to massively accelerate its own development, and AI with real-world competence is coming very soon. At the rate things are going, it wouldn't suprise me at all if we had mass production of AI-designed systems in the next six months, actually.
> AI with real-world competence is coming very soon
Disagree, Moravec's Paradox remains undefeated. How long has Elon promised self-driving cars? Or how long have we been seeing humanoid-robot-walking demos? Laundry folding demos?
Let's assume that a magical AI powered robot hand lands tomorrow. How long do you think it'll take to ramp up assembly/production/distribution/sourcing/materials/QA for, say, a million of them? Never mind the legal/integration/maintenance time.
And even once those have landed, and assuming they are ALL put to work on iterating on research testing to build out super-high-capacity-drone-batteries, how long do you think it would take for that to evolve into killer-insect-drones? BTW you should know that even though high capacity silicon carbon batteries exist, they haven't supplanted other Li-ion batteries for a host of reasons. A million things stand between a technology working in a lab and surviving the real world.
And during this entire process, the (geo)political/social/legal process will be churning away, changing the societal landscape in which the killer-robots land. Not to mention that mechanistic interpretability is (likely) somewhat grindable. A lab just needs to dump a billion dollars of compute into it after it declares AGI.
I'm unconvinced we get killer insect drones before we crack mech-interp.
And even once we get killer insect drones, they need to somehow have no kill-switch and then literally exterminate everyone on the globe? Can you see why I have a problem with doomers predicting human extinction within the decade?
I'm putting words in your mouth and not replying to precisely what you said, just the general vibe. LMK if I overstepped.
>Agent-2, more so than previous models, is effectively “online learning,” in that it’s built to never really finish training. Every day, the weights get updated to the latest version, trained on more data generated by the previous version the previous day.
That seems to me to be a natural progression, from discrete models to models that are just continuously improved. Maybe we'll end up with different models with different rates of improvement rather than static differences in performance, and methodologies for that improvement will be the thing we care about. Maybe over time, even benchmark tests will be primarily concerned with that kind of efficiency.
I think a huge debate right now is the relative value of the "frontier" models from Western companies at the cutting edge, vs distilled versions of those models that are good enough and exponentially cheaper coming from China. But a paradigm of 'always training' means an always active, always advancing frontier, which is a stronger moat than a one-off model that's more advanced for a few months.
One of the most biased claims IMO in AI 2027 is that a huge portion of the geopolitical and existential risk argument is hinged on the notion that China just steals the US frontier weights.
Look what's coming out of China, they are catching up on performance and surpassing the US in efficiency. They're on a different level when it comes to open releases of weights.
I don't, to me the entire premise is a bit flawed at it's core (ASI), and I read it like bad science fiction with China playing the bad guy just a narrative crux so we get to the acceleration timeline and warring nation states.
The US/China divide is one thing, but if I'm asked whether I trust OpenAI or Deepseek, as companies, more, I'm not sure how I'd answer. I suppose my default is to distrust whoever is in the lead, since the lead is power, and power corrupts.
Interesting but it degenerates into sci-fi tropes if you look at the extrapolations. Reminds me of 90s writing about what the Internet was going to do.
The Internet ended up being both more incredible and more mundane than predicted.
One of the first things I learned in ML is that the model can only learn from the information in X. If X doesn't contain enough information to determine y, no amount of compute can fully recover it.
That's why I'm skeptical of grand claims about AI. Scaling can make models much better, but it can't create information that isn't there. An AI system can be extremely useful without becoming superhuman.
And what do you think about recent developments, for example Navier-Stokes? I always thought the same but now I have doubts, but maybe it is just psyops from openai.
Fun to read this again and see actual parallels. The 2030 Takeover section is such a ludicrous leap, however. None of the supply chain infrastructure, energy, or Moravec's Paradox realities are ever addressed. Turn the page and suddenly humanity is largely annihilated with a Corgi-esque human breed kept as pets. How did these robots emerge from utter rhetorical nothingness? Robocalypse impossible? Perhaps not. By 2030? an intellectually embarrassing farce worthy of a facepalm.
Sorry, but the notion that creative writing and robotaxi's exist as proof of anything is like saying my child can drive and write; and while true, the measure of that ability is not at the level of the best humans. It's average at best.
All of the early data shows that self-driving cars from Waymo are safer than the average attentive driver. This is not the average driver, since of the 36,000 deaths a year from accidents in the US, about 12,000 of them are due to alcohol or impaired driving.
As for writing: AI is not nearly as good as the average professional writer, but they are definitely better than the average citizen of the United States, considering that 21% of adults are not functionally literate in the US. AI has no problem writing at the undergrad level.
Depends what your definitions of "requires" and "full" are. On mine there's some nagging on the scale of once every tens to hundreds of seconds if it thinks I'm not paying attention. You're willingly living a more stressful and unsafe life if you're still manually driving your car in 2026.
I wonder when the people will get that intelligence is not only directed at the external, but only really starts when you look at the internal (joy, pleasure, traumas, taboos, awkwardness, abuse etc.). Look up the word "interoception".
I like this one better
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