That trend will continue.
I figure there are 6 order of magnitude events that could happen in the next decade to lower token prices:
- more specialized / better chips
- IC technology: smaller feature size, higher clocks, etc.
- more efficient algorithms
- solar power is getting cheaper at an order of magnitude per decade, batteries even faster.
- pricing pressure from open source models
- breaking of the Nvidia monopoly and it's 75% gross profit margin
Maybe all 6 won't happen, but certainly a 1000x reduction in price in the next decade seems highly likely. Jevon's paradox says that the 1000x reduction in price will likely result in more spend on AI, not less.
At work (we're a medium sized manufacturing firm), we bought our own inference server for $107k and run Kimi 2.8 for nearly all of our use cases (and dropped our cloud AI spend to $0).
And, oh yeah, our local server is much faster and more available for our 30 or so local users than the SaaS services. Win-win.
I mean the physical hardware will be fine but the owners and investors should be worried then right
Don't worry, the already stretched taxpayer will be on the hook for everything just like in 2008!
This time the relief mechanism is already baked into the system (capital does learn from its past mistakes, even if it may not be the lessons you'd hope for!)
https://prospect.org/2026/08/03/ai-bailout-could-be-baked-in...
If the cutting edge OpenAI token prices are $80 per 1M token, and the open source tokens are $1 per 1M token, that's a huge gap of "this will never be able to make money under any scenario if the bubble bursts" that will catch a lot of these new datacenters. No one will run a datacenter that costs $5 per 1M token to sell at $1 per 1M token even if the debts are cleared.
That $5 per 1M token doesn't literally cost $5 per 1M token. It's more like they had to build a datacenter for $500M that can service 100T tokens over its lifetime. They did this by borrowing money on the capital markets, and now they have to pay interest to those bondholders, interest that they can recoup with their $80/1MT prices. But if it turns out they can't charge $80 and have to charge $1, they won't be able to make those interest payments. They enter bankruptcy, the court wipes the debt clean, and now they don't have to pay interest, only the actual operating costs, which may be more like 50c/1MT. The company gets recapitalized with the new owners being largely the bondholders, the existing equity holders get wiped out, and they can compete with the commodity producers now.
You have land taxes and or rent, building upkeep, staffing costs, electricity, water, hardware replacement costs.
And new build DCs have blown all these costs through the roof justifying the decision because the price of compute is so high. When the prices come crashing down, the expenses will remain fixed where they are now.
That whole thing sounds deliciously evil - I’m not even sure who to be mad at — too many to pick from.
The two conditions where they could win are:
1. When they have liquidation preferences over the other bondholders. In this case, their claims come first at bankruptcy, which means they can end up owning the company at the expense of the other bondholders and stockholders. The company's overall profits might not be sufficient to generate a return at the interest rate of all bondholders, but it might generate returns over what a select group of bondholders would otherwise get.
2. When the company can't generate sufficient profits now, but their revenues and earnings are expected to grow over time. In this case, the new equity holders would take a significant haircut on the value of their investment at the time of bankruptcy, but improving financial positions means the value of their investment could grow to be worth significantly more than the bonds over time.
I can't rule out either of these for AI companies. The principals of many of the companies involved have a record of self-dealing that's very similar to #1 - it's illegal if it can be proven in court, but it's often very hard to prove, particularly if there are other parties involved. And the economics of AI are likely very similar to #2.