AI 2027 (2025)(ai-2027.com) |
AI 2027 (2025)(ai-2027.com) |
I like this one better
For example: META made a bit more than $5 bn revenue since 2022?
It hasn't been a reliable predictive metric so far. All runaway growth tech sectors and businesses tend to look economically insane until they're not.
They also said it about loads of other huge tech things that died. Pointing at the ones that made it is literally survivor bias.
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.
damned if you do.
damned if you don't.
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.
> 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.
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.
https://www.anthropic.com/threat-intelligence-report-septemb...
Everyone knows the Chinese are capable of whatever they put their minds to. But stealing IP to skip some steps is part of the system.
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.
Wait until you hear about Amazon's practices.
The Internet ended up being both more incredible and more mundane than predicted.
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.
They underestimate AI's existing impact on some job markers and overestimate it's impact in such short a timeframe.
https://publichealth.jhu.edu/2026/the-safety-data-on-autonom...
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.
In other words: bias. Tons and tons of self-congratulatory, glue sniffing bias.
> Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something.
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.
> The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt.
https://x.com/hilbertspaess/status/2097476203863224394
> The dynamics are plausible
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?
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.
Poulsen treatment for the rich (https://hyperioncantos.fandom.com/wiki/Poulsen_treatments)
The race is for things that does not exist today.
> 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.
P.S. we do have mass production of AI designed systems today e.g. https://en.wikipedia.org/wiki/Evolved_antenna There's nuance to this whole thing.
Frontier labs are also reinvesting their tens of billions of revenue back into infra scaleout, sounds like Amazon.
If 2 of 3 were like this, and Azure numbers were never split out, want to take a guess what the economics of the third was like too?
Cloud computing was still profitable the entire time. Amazon alone could cover all of GCP expenses.
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.