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Notes -
AI timeline post
I have an idea. Let's post year-by-year AI timelines. That way we have public predictions and in 1 to 3 years we can see who is better at it.
2027: development begins on a startup that targets corporate managers with a fully agentic microservice creator. It promises to replace at least 5 devs with one who runs the creator. It's basically a scam like most startups, a UI gloss on claude opus 4.8, which is only 10% better than 4.5, but it panders to non-technicals so it gets a ton of seed funding. AI solves maybe 10 niche, self-value giving math problems that is considered „impressive” by the kinds of people who put calories into math which never seem to come out of math. This contributes to marketing hype because some of these people are respected by rich tech funders and finance bros for whatever reason. AI is still completely useless at real scientific research and reasoning.
2028: tech layoffs continue as the statup I mentioned enters its beta stage. It makes some bugs on the websites that engineers still need to fix but pilots of it are 5x or more compared to 2018. Tech people start pivoting to finance and robotics slowly, from JavaScript. DeepSeek releases a model that is equivalent to 5.3 Codex. General coding ability stalls and Anthropic + OpenAI are looking for spiky improvements to models plus tooling ideas. They begin to think about targeting non-coding white collar work like finance and spreadsheet work since the models are not getting much better at JavaScript, having used up all of the JavaScript data in the entire world. Some math problems continue to be solved as models are secretly trained on newly parsed math examples but nothing comes of it. One contribution is made to theoretical physics but it is on the abstract side and it is still controversial as to whether these models can do any science.
2029: Someone is arrested in the United States for plotting a terror attack with GPT. To nobody's surprise, GPT and Anthropic messages are completely unprivate and the United States secret police have been monitoring them with cooperation from the companies and zero warrants whatsoever for years. The scary part is that GPT gave a 120 IQ plan to a 90 IQ person and it could have led to more deaths than whatever 90 IQ plan he would have come up with on his own. Frontier models begin to entshittify as they are increasingly jailed to make them safer, while their private reputation is shattered among all of the normies who did not know how rights-violating the United States secret police are. More attention is turned to local models as a result, but these are hard to run on normal hardware and the best is Sonnet 4.6 level at this point and requires $10,000 worth of GPU machinery. In addition AI is increasingly being aimed at normiejobs instead of aspergers jobs. Every good normal, local football team loving, sydney sweeney gooning person hates people with aspergers and loves to see them suffer so they got off on viciously and narrowly aiming to automate their programming jobs, which are still not gone yet, but they're getting really defensive about the new AI tutors, AI nursing assistants, AI portfolio advisors, AI middle manager helpers, AI emailers and spreadsheet workers, and so on. The Blessed Sovereign People of the United States are now grumbling about regulations of their domestic AI systems which was heretofore dismissed as impossible because China has a model that is 3 years behind (really they just felt comfy at the idea of getting programmers, who all have some variant of aspergers, to dig ditches without there being any other economic side effects). Basically no improvements of relevance are made but Opus 5.0 drops along with GPT 6.0. The models are really only 10% better than GPT 5.5 at making JavaScript code but are 20% more expensive and slower (billing at 10x on Copilot, while 1x is still Sonnet 4.8, which is not better than 4.6), because they mostly rely on innovations in prompt chaining. Scott Alexander claims exponential performance based on some bad, but widely accepted performance metrics, and claims he was right that AI would end the world based on this year's secret police activities.
2030: Someone proves the Riemann hypothesis after spending $30,000 worth of tokens over 2 years. This is taken as proof that AI is super intelligent by Scott Alexander, who declares victory. 80% of web developer jobs remain from 2025. 50% of people are majoring in computer science as before. AI still has not made any scientific breakthroughs, but an experimental startup is working on tooling to let AI collect biological data and analyze it, talking about a 2040 cancer cure. Applications of AI to non-webdev are generally taking off in the startup space and people slowly quit their webdev jobs to work on these. There's robotics, scientific research, and automation of normiejobs being targeted. VCs are funding this with money made from the AI boom. Model makers focus on training edgy models for these particular tasks by mining non-software data from various sources. Some startups focus on making data collection tools that could be deployed in workplaces to monitor activities so than LLMs can be improved at non-software tasks.
2031: Some edgy models are rolled out but they are only available to licensed researchers, citing fears of some other entity getting one over on the United States with these models, because it is the most perfect union of the most perfect people in the entire universe, as everyone knows, where all of the state violence is justice and all of the state spying is privacy and all of the state socialism is capitalism and all of the imperialism is democracy and all of the power is earned. It is revealed that Palmer Lucky, who loves the United States and its perfect people and perfect government the most, has secretly developed an AI terrorist killing model that promises to automatically fly drones through terrorist windows, hopefully only outside of the perfect borders of the United States (but just you wait on the policing applications!) which then dispatch justice upon the foreign terrorist, United States style. By the end of the year, some licensed academics are saying the models have their flaws, but speed up their research pipelines a significant amount.
2032: 80% of people still have their webdev job, and now they are about 5x more effective each. It turns out the demand for webdev has 4x'd. Places like Japan are receiving modern websites for the first time. Most ex-webdevers reallocated to finance and non-webdev AI startups. GPT 5.5 now only bills at 3x the base copilot price. 6.9 is out and it's about 20% better than 5.5 but it costs 25x and takes forever. It's pretty clear to most people the general improvements to coding are dried up and a lot of the old hype was fake and tooling and chaining was the internal, secret meta from 2025 onwards. Some still believe performance increases are exponential because benchmarks have only barely started to slow down. They find it convincing that AI is doing „qualitatively different” tasks than it was 5 years ago across domains. AI researchers begin to use frontier statistics models to search for new AI structures. Just like LLMs, it's a terribly boring task, mainly consisting of a random walk around state space. A general theory of AI is still not developed and it seems LLMs will not be able to develop one on their own. $15,000 worth of tokens nets proof of the Hodge conjecture. Scott Alexander takes this as proof that AI is getting exponentially better at math. By 2040 it will only cost 10% of the price of rent in SF to solve a millennial problem with an AI system, he says. Elon Musk funds a startup to use LLMs to reverse engineer the brain, thinking this will lead to true AGI. It is very difficult to say the least.
I will stop here but I think the meta will be using LLMs to do dirty work in science to maybe get a second wave of AI starting around 2040 if we are lucky. This will be based on solid theory and brain reverse engineering. This might yield general work bots in the 2050s or 2060s.
The tech sector right now has a lower unemployment than the general US economy
https://www.google.com/url?sa=i&source=web&rct=j&url=https://www.wsj.com/cio-journal/tech-unemployment-ticks-up-to-3-8-in-april-amid-ai-driven-layoffs-214b0ca4&ved=2ahUKEwins7yEpNGUAxW7ZvUHHXKrPfsQ1fkOegoIAggACAAIHRAC&opi=89978449&cd&psig=AOvVaw0IYy6J7-fiZwA_vTGmEcwb&ust=1779690002348000
3.8% in the information sector vs 4.3%
I'm stating this first to sort of color the rest of my point in the context that a lot of what people say about what AI has already done is just bullshit. But furthermore, people fundamentally don't seem to understand how employment works. You can have mass layoffs and still have high employment. I'm not even sure tech layoffs are higher than other sectors, but I am sure thar every company that lays off tech workers gets front page news while if there's a cut in delivery drivers nobody notices.
The only smart prediction to make is that we don't really know. People here just don't realize how big and complex the economy is and the world at large is. Even if your job is gone, your skills are often still transferable. When horse carriage producers were put out of business they didn't all starve and never find jobs. They started working on building cars for the most part.
AI is just another step in a long line of automations. Is it an exceptional step? Probably. Will it ever replace all workers? No. By the nature of economics, that's basically impossible. People's desires are infinite and there arent infinite resources and labor, so there are always niches to fill. Might it make people poorer? Maybe. I kind of doubt it unless governments uses it as an oppressive system that cracks down on a lot of market activity.
My point here is really that making predictions is a fools errand. People have tried to do it, and at best a few get lucky and pretend they're geniuses and then return to the mean on the next prediction. There are obvious truths you can see, sure, like if the price of compute continues to decrease at an decelerating rate, it will significantly affect AI progress. I think that even as we see continued progress in AI, that will be the fundamental factor that's overlooked. Look at the flop count per dollar on a CPU from 2005 vs 2015 and then a GPU from 2015 vs 2025. Nvidia is squeezing some progress out in other ways, but at massive costs.
So my prediction is simple I guess. AI will be a big boon to the economy. It will take a few years for companies to learn how to cost effectively implement it.Some sectors will disproportionately reap the rewards. I suspect the gains will be in the.5-1% range of additional productivity growth a year, which is a lot. For context, the early industrial revolution was something like 2% growth year on year excluding population growth. With an extra 1% productivity growth the US would be higher than that right now I believe.
I also suspect there are factors that are huge burdens to society which AI can't overcome. Population decline. A war in Taiwan. Developed country and Chinese debt burdens. All of these things could affect AI. Which is ultimaty why all predictions beyond a year or two will be meaningfully wrong.
Minimum wage and related barriers put a finger on the scale though. Currently, very-low-skilled people are unemployable because the assorted costs of hiring them outweigh the expected benefits. In the future, will that extend to moderate skill levels? high? I don't think it'll cut off 100% of people before extinction and/or post-scarcity, but I could see the labor force dropping from about 50% of all people today to 10-20% even if AI remains a normal technology.
I'm not sure that this is entirely true. Very low-skilled people are unemployable period, and lowering the pay rate doesn't do anything. For example, there's a guy I know who isn't the brightest, retired now but comes off as someone who was definitely in special education back in the 60s and 70s. He worked as a janitor at a local elementary school. In Pennsylvania the minimum wage is the Federal $7.25. Someone in his position would be making $22.62 this yer and $24.35 next year. Of course, that's because he's been there for 35 years, but even a new hire makes $16.60 on the current contract and $18.60 on the next. Grocery, retail, and fast food wages aren't much lower, even for 16-year-olds with no experience. The only exceptions I'm aware of are for people with disabilities, but that's more because they can only make so much before they lose their benefits. I don't think there is a significant population that's employable but for minimum wage laws.
Now that you mention it, when was the last time you even saw a minimum wage job? Even the convenience stores and McDonald's around me are offering well over the state minimums. It seems like it's one of those things that exists on paper but doesn't really come up anymore.
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