DeepSeek has been a very important breakthrough for understanding the future of economics in software in a world of AI. There’s been an open question for a couple of years now - especially from public market investors - around whether more value goes into the AI models or into the application layer of AI over time. The specifics of the pie graph don’t matter as much as the core direction of the space.
Imagine two different scenarios: one in which AI was extremely proprietary and very expensive, and another where AI is almost completely free and relatively open. You could easily game out two different outcomes in these worlds.
In the world of very expensive and proprietary AI, the providers of AI could and likely should choose to keep all the economics for themselves - basically crowding out opportunity for developers and the ecosystem. In a world of insanely cheap AI, then the value is less about the models, but what you do with the AI models to make them useful - in that world, more value is available to the application layer (which could include the AI companies, to be clear).
With the latest breakthroughs from DeepSeek, we can nearly definitively say this question has been answered, and we’re clearly moving closer to the latter. We’ve already seen incremental steps toward this direction with the continuous cost and quality improvements from labs in the past couple of years, but DeepSeek shifts our understanding of this even further.
In a world where the cost of intelligence will continue to drop rapidly, more value will accrues back into the app layer. Products that combine AI, customer workflows, and likely some degree of unique data, will generate substantial value from these models going forward.
Now, everyone wants to live in a binary world of winners and losers, but I don’t think it’s that simple here. The leading AI labs will incorporate the relevant lessons from DeepSeek into their models, and we’ll get cheaper and more intelligent AI. As a result of that, the cost of intelligence will continue to drop, and we will find even more ways to use the technology as it becomes affordable for even more use cases.
If we can make AI 10X more efficient today, it’s exceedingly obvious we will have 100X more use for it in 5 years from now, more than making up for the efficiency gains. Making demand for GPUs and data centers bigger than ever.
In all, fantastic to see that we continue to have companies and teams pushing the limits of AI. This is a great win for software developers at the app layer, and it will push labs to go even further. Incredible times.
