A lack of buyers constrains the size of exits, further discouraging investors.Ĭhips attract less than 1% of total U.S. Third, the chip industry is too consolidated, dropping from 160 companies in 2010 to 97 in 2020. This long period, again, discourages both innovators and investors that typically prefer to “fail fast”. It typically takes a few years before the first product sample is created and it may take just as long again to see revenue. Second, the gestation period for new ideas is too long. This recipe leads to incrementalism, not disruption. As a result, not many chip startups are formed, and the few that get funding come from teams of seasoned chip veterans. These extraordinarily large sums of money discourage risk-taking, entrepreneurship, and funding.
It often takes $10-30 million just to get the first product, and another $70-100 million to scale up. The chip industry doesn’t have many of those experiments.įirst, the cost of experimentation is extremely high. It’s hard to make chips smaller than they already are-transistors, at their thinnest dimension, are only a few atoms thick. It’s now so expensive to build advanced machine learning models that they are now the exclusive domain of rich, powerful corporations. And that’s making computing expensive for everyone. The gap between what’s needed and what’s provided can only be filled by more chips. The most advanced machine learning models, like those that power GPT-4 and ChatGPT, have increased by 75 times, again much more than the power of the graphics processing units (GPUs) that underlie them. The size of models used for tasks like computer vision, natural language processing, and speech processing has increased by 15 times in just two years, an order of magnitude higher than the increase in computer power in chips over the same period. Chips are simply not able to keep up with some of the most computation-intensive applications yet seen. This slowdown could not have come at a worse time.
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And the process of rearchitecting software for these chips can be expensive and buggy. Now, a microprocessor’s performance increases by only about 10-15% each year-and the actual increase in speed for a given software application is often much smaller. Historically, the computing power of chips has doubled every two years in what became to be known as “Moore’s Law.” But we haven’t seen that jump in performance for a while. But all this optimism shouldn’t distract us from one of the chip industry’s key problems: Chips have stopped providing real jumps in computing power, right as we see an explosion of power-hungry applications like generative A.I.
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