Brutal stuff.
It could easily turn into a Red Queen situation where companies that don't spend hard on security are going to face huge potential losses, and both sides will be scrambling constantly to get an advantage.
We've known for decades that more lines of code isn't good. Bill Gates was joking about it in the OS/2 days!
We've known for decades that the actual creation of the code is the smallest part of an actual software developer's job.
We've known for decades that "every line of code is a business decision" that has to be written with an understanding of the underlying goal, and the hardest part of training up software engineers is making them understand that.
We threw all that out because "the chatbox writes code really fast, software is a solved problem!"
This may be true if you work in unfamiliar area or on frontier of programming. But this is not were most of programmer jobs are. That writing code itself is hard is a view of non-programmer. Programmers know that past some threshold of familiarity designing and weighting decisions becomes central problem.
Anyone doing consulting has seen it, where now the "I vibe coded this last week" competition is way more intense. Basically the people with the problems now have a chance to spin up something that looks like the solution they want, they then get in trouble and need someone to sort it out.
LLMs _are_ great tools for software development but if you haven't noticed their near complete inability to reason about why what they're doing might work you haven't been trying hard enough.
So from the top it doesn't look like much has changed, whereas workers are trying to offload as much work as they can onto their claude subscription.
People in desperation to min/max work per unit dollar, will leverage AI to free themselves from as much work as possible, while still claiming credit for the work.
So as the labs start ratcheting up the price, people will pay more and more to keep their "secret" assistant.
[0] https://businesschief.com/news/why-are-executives-using-ai-m...
They call out the MIT study in the article, which found very low adoption of corporate AI initiatives.
However if you read that study, they found that almost every worker uses AI heavily, but they just use their personal AI account.
Again, people are using their own AI to benefit themselves, it makes the most sense. Using company AI shows that the AI is doing xyz, and maybe perhaps you aren't that valuable. Using your own AI makes it look like you are doing xyz, and you are the valuable one.
For unskilled people, it creates a false sense of productivity, but if the user of the tech does not understand what they are doing they can’t apply the tool productively or judge its output or deal with the things beyond its ability.
So like all other tools before it: it works best in the hands of a skilled user.
Just like in all other fields.
There is no tool that makes an unskilled user skilled, but there are many that can amplify or extend the capability of skill.
If mech suits existed and people actually lived in them they would either get super fat or super skinny. In either case they'd lose all muscle mass.
https://newlearningonline.com/literacies/chapter-1/socrates-...
Humans are tool makers and this is what happens when you make tools.
And I run my own firm... but I have to compete against a global market of other programmers using similar tools who are also trying to compete on price, and then there are hordes of new entrants who can vibe-code things that superficially look like they'll meet a customer's needs and are better at marketing than I am, so my area of business gets constrained to just customers who need help after the problems with the vibe-coded solution appear, the vendor who made it is long gone, and they already spent an inordinate amount of money on the vibe-coded solution since they thought it was complete and it looked pretty good.
Agentic coding could help a lot for the latter, not necessarily the former.
If the aspiration of a 10x or more programmer is enabled through AI then the capital class win, in the typical case. Software eats the world.
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> All these complex calculations roughly tally with a much simpler one: adding up the ai revenue of the firms selling most of the ai. Anthropic pulls in perhaps $75bn, annualised; Openai makes tens of billions; Google, via its ai model Gemini, and Microsoft probably get a bit less. SpaceX may have a few billion dollars’ worth of revenue from enterprise aithis year. Meta also makes a few bucks from ai. Add this up and you land at roughly $150bn a year.
1) people like me exist: I don't live in the USA, yet it is possible for me to buy things which are made in the USA
2) fiat money is only created or deleted by laws the government controls (plus a tiny quantity from forgery and damage to coins and notes), so everyone losing their jobs doesn't make the money disappear, just who has it available to spend
3) previous waves of automation have created new business opportunities; while this has not been too good for the people who lost jobs to the automation, it has generally boosted the overall economies this happened in, so it is absolutely possible to wipe out tech employees without it seriously harming consumers collectively
Still, there are plenty of people who take their cut of the money on the way. So at least those people will be happy.
So between $170bn-220bn in annualized AI revenue today. Maybe it doesn't cover trillion-dollar bets but this is a very substantial number.
[1] https://en.macromicro.me/charts/148532/world-openrouter-toke...
Once the Pathfinders find out where the real value is there is much more room for growth from companies who have be mindful of budget.
Yes, I will concede this point. But how are the Chinese labs going to thrive with giving the weights away for free? Will they suffer the same fate open source database companies did at the hands of AWS?
For the state, like BVD, the goal is to be a loss-leader for Chinese infrastructure + manufacturing.
AI inference needs to get cheaper or there will always be this "terminal velocity." I suspect AI labs' incentives to reduce costs is only where there is overlap to free up hardware/utilization (to then provide inference to more paying customers.) I highly doubt they will want to make things cheaper for users -- they have debts to pay.
Open weight models are the way and forward, it is the only way an organization can truly control costs by self hosting or buying cheaper inference. Relying on closed weight models is a business risk. Kimi models are only marginally worse than opus but significantly better than the bleeding edge of yesterday's sonnet.
The answer is that all companies will not be using the same frontier LLMs competitively
This will be yet another technology that will benefit from economies of scale. Big businesses and those in PE portfolios will lead the charge, adopt AI, increase efficiency, and gain market share at the expense of mom and pop shops.
This will be good for our 401ks because we're all primarily invested in big business
In other words usage per regular worker doesn't matter. Revenue overall does.
I noticed that were a lot of traditional, non-tech companies interviewing for AI engineers in the Feb/March timeframe that have halted hiring in those roles entirely. It seems that if those roles didn't close by mid-April that they didn't close at all. This seems to match the timeframe in which cost suddenly became prominent in the AI zeitgeist.
I don't know _anyone_ outside of SV who has successfully replaced even a single employee completely with the current models. Maybe someone has pulled this off in call centers, but the POCs have all failed.
I'm very much pro-AI, but I just think we're on a false summit. As the article points out, there is absolutely no way to recoup the investment costs unless the models allow companies to start displacing human workers by the millions _and_ recapture a significant fraction of the displaced workers total comp. If either of those aren't true, then the bubble is going to pop... soon.
Turns out 516B of the revenues are from Nvidia, which is not an AI company, but just selling shovels. Nvidias revenues are effectively costs to actual AI companies.