hajile
$16B revenue in first half means $32B for the whole year at that ARR. Divided into 47.2M devs gives us $677/yr per dev assuming that ZERO non-devs ever pay for Anthropic. Add in all the OpenAI and Chinese spend and spread them among developers only and the average is still only around $1k/yr/dev.
Top 1% of AI users spend $9,567.53/mo or $114810.36/yr while median spends $15.70 or $188.40/yr. 80% of the top 1% of AI users are backed by venture capital.
https://ramp.com/data/ai-index#adoption#overall
Anthropic + OpenAI seem to have in the area of 70-100M users.
Only problem is that 1e6*114,810.36 ~= $114.8B which exceeds stated income of OpenAI, Anthropic, and all the Chinese firms combined.
When you crunch those top 1% guys, you get a number more like just 250-500k or so accounting for almost all current AI spend which leaves 46.5-47M devs using very little AI. You can adjust these numbers by massive margins (around an order of magnitude) and still see that the math doesn't work out like you seem to think.
When all that venture capital/private equity gets crunched, those companies won't be able to afford to keep blowing a hundred thousand per dev. When they back off, AI sales will drop further lowering available venture capital risking a massive downward spiral.
I've built large FE systems with millions of lines of code pushing tens of billions of dolllars in revenue (I was lead FE dev on that one).
The idea that failing to do a small project (that should be clearly represented in the training data) means failing even harder at bigger projects doesn't suddenly gain merit because I have a lot of experience and have worked on big projects.
The overwhelming majority AI spend is being done by startups funded by venture capital.
Meanwhile, big companies have massively cut/capped AI spending at the exact time these companies need to triple prices to get any margins at all.
Perhaps dead internet theory applies more than normal to programming forums.
All of these thing still matter no matter what you are coding with.
Centuries ago, "thou" was singular second person and "you" was plural second person.
"Thou" got abandoned and "you" lost its original meaning to replace "thou" as the new singular second person.
"You guys" or "y'all" are simply attempts to re-introduce a needed pronoun.
Old work is always harder than new work. It varies by how your home is designed. It varies by where your panel is located and where your garage is located. You will pay a premium for the extra time and pain in the butt it causes.
As a great example, my friend asked for assistance with their home's street light not working. Long story short is that they ran a UF (underground feed) line directly under her concrete driveway (the contractor was too cheap to run it in conduit). I megged that line and it had a short. Their light stays off because rerouting through their house then digging up their yard isn't something they want to do (don't want to pay as I don't have that much free time and they don't want the massive headache it would cause). I was able to disconnect/wire off the failed part of the circuit so everything else works for them, so that's a win (and I didn't charge, so that was a win for them too I guess).
I got lit up by 277/480 when I worked as an electrician because someone removed a lockout and turned on the breaker.
In any case, not all jobs can have the power turned off.
I really think the AI companies think there's no turning back on the infringement and they have to infringe in a "too big to fail" manner to the point where they can force an exception for AI not caring about the fallout for IP rights.
That leaves the only real difference (other than degree inflation) as being 3 years of residency, but most of the older generation of doctors effectively did the same thing entering into an existing practice or working in a hospital under an experienced physician.
If you climbed the ladder (like a lot of nurse practitioners do), the ADN+BSN took 5 years with 3-4 more years for the MSN or 4-5 years for the DNP.
Put another way, that nurse practitioner has MORE applicable years of schooling than a doctor who got some random degree with the handful of med prereqs then went to med school, internship, and residency. Lots of nurse practitioners also work in the field all the while they are in school which further increases their actual experience.
The real takeaway is that all these extra years, loans, and interest combined with fewer years to practice medicine translate into more expensive care without improving outcome.
https://www.stlouisfed.org/on-the-economy/2026/jul/how-are-d...
At the same time, the doctor with just 4-5 years of school and hundreds of thousands less debt can not only treat patients more cheaply, but will have more years of practice too.
If these companies will steal from deep pockets like Disney or Sony (some of the most infamously litigious copyright trolls to ever exist), they won't think twice of stealing every bit of code you upload to them.
If your code passes through an AI company's servers, you can assume you just gave it to them. In turn, when your competitor tries to copy that new feature you just added, the AI is now trained in exactly how to copy you and eliminate your competitive edge. Unlike your employees, the AI isn't bound by the same rules and even if it were and violated them, your company probably doesn't have enough money to prove it in court (and that's if we somehow reverse some of the stupid "AI is the most transformative use of copyright I've ever seen" judges who have drunk the coolaid).
Most companies could build the compute to run GLM or Kimi models for way less than the potential loss due to IP theft from using third-party systems.
All this indicates to me that loads of GPUs were only purchased on paper or are sitting in warehouses unused. This almost certainly means a drop in orders followed by a drop in RAM prices (though that would indicate a demand drop to investors, so maybe it’s better for stock prices to keep paying to bills bigger warehouses and stuff them full of unused GPUs).
The second RAM prices normalize, local LLM becomes much cheaper. A machine that doesn’t make much sense at $10-15k suddenly becomes a lot more feasible at $3-5k. If those stored GPUs flood the market, we might see even bigger price drops.
CL is very competitive with popular managed languages like C#, Go, Java, etc despite those all getting tens to hundreds of millions in investment while SBCL gets basically nothing (imagine what it could do with serious investment).
Even if that child doesn’t have as much raw talent, the desire itself (paired with extra years of experience —- especially when the brain is at its most malleable) will drive success in a way the pursuit of money alone never can.
Keeping financiers of their IPO from understanding what they might be buying seems to swerve into fraud territory. Keeping their investors from knowing/understanding what the problems are also seems fraudulent.
Have you ever been in poverty? I grew up that way and the then-equivalent of $230 would have meant not skipping Christmas when I was 6. It would have meant a full refrigerator, gas for the car for a couple of months, or so many other things.
Google, Amazon, Oracle, Microsoft, Meta, SpaceX, and tons of other startups are spending trillions on data centers, but everyone is renting them out. They aren't renting them to each other (and paying the extra overhead when they have their own servers).
That leaves basically just OpenAI and Anthropic on the hook to pay for everything BEFORE the GPU half of it depreciates away, but they are busy cutting prices to compete with Chinese models which runs counter to their need to increase prices to fulfill their obligations.