This is probably the best one for coding? The two main findings are that developers didn't want to do tasks without AI (implication being that they would find it too tedious) and for the tasks that were measured, there was a speedup (and more of a speedup if you had more experience with AI tools)

Unfortunately "productivity" is very hard to measure directly. I prefer looking at how much money companies are paying Applied AI companies (a lot) because in aggregate, that meant these companies justified ROI vs. OpenAI/Anthropic directly, and sufficiently enough that large enterprises are willing to go through the time and money to spend on a vendor. It's not foolproof but it dampens the effect of companies tokenmaxxing their Codex/Claude Code to look productive.

2. Even if we were to assume an analogy to a petro state, it seems like we as a society can decide if we go the route of Norway or Venezuela

Wouldn't UBI be funded by the wealth generated by the automation in this case? So is the difference only the amount people receive that changes UBI from an economic cushion to sharing the wealth?

In addition the premise that everyone will be fired is a little presumptuous to me. So far we've seen that agents are very capable of automating well-scoped, verifiable tasks but the majority of jobs don't consist of those

So the two best metrics are annualized recurring revenue (take last month * 12 or last 4 weeks * 13) and QoQ growth %.

There are two caveats:

- If the revenue is high quality (e.g. annual enterprise contracts, good NRR), then last 4 weeks * 13 is actually a conservative estimate as your company will likely continue to grow.

- But if the revenue is more volatile (e.g. consumption, token usage, bad NRR) then annualized recurring revenue can be used to hide worse performance because companies will juice revenue one month and report high "ARR"

Also I provided the list of actual hype ha

they're quite generally intelligent, and you can leverage them in nearly every business function. marketing, sales, ops, research etc. which basically every business needs

clearly we still need humans running companies for the intangibles but im saying the premise doesn't hold up under the market reality

If we start to see spend go down because projects fail and companies run the ROI calculation and determine it's not worth it, then ill stand corrected and happily admit that

code wrappers - cursor (special case), lovable, replit

part model part applied - perplexity, 11labs, cartesia, suno

applied branches of model labs - codex, claude code, deployment cos & fde teams

ai roll ups - thrive, longlake, some stealth ones

applied - cognition, sierra, fin, harvey, legora, glean

part data part applied - scale

Margins vary, but many of these companies' revenue are already a chunk higher than what was last publicly reported

Wouldnt be surprising to see some of them 2-5x rev in the next few years

There are applied AI cos making 100-400M+ in just a few years of incorporation, does that count as financial gain?

Academia is currently 6-12mo behind the frontier of the industry due to secrecy and publication times, so any "long term" study, even for a year, would be out of date on arrival

My view is they both have a clear use case for AI, because every business has a use case for more intelligence on tap. Enterprises big and small already shell out billions upon billions for AI so I'm not sure how your premise holds

In fact AI has resulted in more startups than ever starting to take market share from the incumbent software companies (and the market has started to price that in)

Not denying that China is a close #2 btw.

If the LLM was GPT-1, most people wouldn't even use it for free. So clearly there's another axis here?

I think it's obvious that demand is overwhelming supply right now. I agree that we don't know how much of the demand is due to perception, perverse incentives, or poor management, and how much of the demand is 'real'. I personally believe that the demand is mostly real and will continue to go up, but I don't have a crystal ball.

I also acknowledge that the productivity gains are highly dependent on your specific company's implementation and the work that you're doing. I think the role of a technical IC (which I am as well) is going to be managing fleets of agents, and many people who aren't suited to that type of work will leave the industry (and many people who are will join).

I generally agree with you on the points about American politics, I don't think the way they are cracking down on immigration is very wise.

As for correctness - it's a nontrivial problem to deploy AI in prod that works and doesn't blow up over millions of runs+. Hence why the initial value has accrued to the intelligence layer (labs) but the bulk of the remaining value will accrue to the applied layer in my opinion.

I'm sad that America is making it more difficult for foreign talent to come in. But with the flip-flops between D/R in the white house it's really hard to predict what immigration looks like even 5 years from now

That results in the winners providing insane value to both customers and equity holders