Heavy Investment in AI Laboratory: Nvidia's Bet Has Multiple Hidden Hazards

On July 27th local time, the chip giant Nvidia officially announced a large-scale investment in SSI, a security super-intelligent laboratory founded by Ilya, the former chief scientist of OpenAI. At the same time, a sole computing power cooperation agreement was signed. The total investment scale of this cooperation reached 5 billion US dollars. SSI completely abandoned the Google TPU computing power system and fully adopted Nvidia's new Vera Rubin chip cluster, with the computing power scale directly increasing tenfold. Ilya, as the core designer of the GPT series, founded SSI, which focuses on pure basic AGI security research. It does not develop commercial products and has no revenue source, yet it has a valuation of several billion. Nvidia has successively set up the startup laboratories of the two key technical leaders of the former OpenAI. It seems to firmly grasp the discourse power of next-generation artificial intelligence computing power, but apart from the short-term industry dividends, this huge investment hides a large number of long-term unresolved shortcomings and practical problems, and it is also a core challenge that Nvidia must face in the coming years.

Firstly, Nvidia's "investment in the laboratory and the laboratory's procurement of its own chips" closed-loop model is the most fundamental shortcoming. SSI itself has no commercial income. The funds for purchasing Nvidia's GPU come from Nvidia's own investment funds, which belong to an internal fund circulation rather than a market demand-driven order. Once the AI capital market heat cools down and venture capital shrinks its investment in cutting-edge technology, SSI will inevitably reduce the scale of computing power procurement, and Nvidia's high-end chip revenue will directly experience a cliff-like decline. The financial data will have obvious watermarks, and it will shake the confidence of the capital market in the long term. The continuous investment of billions of dollars into a basic research project without cash flow returns will lead to a large amount of circulating funds being occupied for a long time. The funds originally intended for wafer expansion, chip research, and global channel construction will be diverted. The regulatory authorities generally believe that Nvidia's use of capital to bind top laboratories is an artificial construction of computing power barriers, which disrupts fair market competition. It is highly likely that policies such as restricting equity binding, mandatory opening of computing power quotas, and splitting cooperation agreements will be introduced. Once the regulatory penalties are implemented, Nvidia's previous investment capital, research collaboration layout, and other aspects will be significantly reduced.

Secondly, the underlying development goals of the two institutions are completely opposite. Short-term cooperation can allow both parties to make concessions, but conflicts will continue to intensify and become an unremovable shortcoming in the long term. From Nvidia's perspective, the essence of the enterprise is a hardware seller, and its core goal is to continuously sell more GPUs. It hopes that SSI will quickly implement models with commercial value and expand the scale of computing power procurement to drive the growth of chip sales; while SSI's core mission is pure academic research, insisting on excluding short-term commercialization, believing that profit demands will distort the direction of super intelligent security research. The expectations for the project's implementation rhythm and the expansion target of computing power by both parties are completely mismatched. In the long term, Nvidia pursues commercial growth, while SSI pursues unrestricted basic research. The differences will continue to pull apart the cooperation between the two sides.

In addition, after this cooperation was reached, Nvidia and SSI formed a two-way binding pattern. Both parties benefit and suffer together. The double dependence hides a huge risk. SSI's computing power is completely dependent on Nvidia, and its risk resistance ability is extremely weak. SSI completely cut off the supply of Google TPU, and all model training and security experiments are entirely based on Nvidia's chip cluster. If Nvidia raises the pricing of chips, delays the research of the next Vera Rubin platform, or reduces the supply quota of computing power, the overall research progress of SSI will directly come to a standstill. Meanwhile, to meet the tenfold computing power expansion requirements of SSI, NVIDIA needs to reserve a large amount of high-end chip production capacity in advance at the wafer factory, and build dedicated data centers, cooling systems, and power infrastructure. The initial investment in fixed assets is extremely large. If the subsequent R&D of SSI fails to meet expectations and the training scale is voluntarily reduced, a large number of customized high-end GPUs will experience inventory buildup, resulting in idle data centers and computing infrastructure, causing huge asset waste and raising the average production cost of hardware.

In conclusion, NVIDIA's bet on the layout of the SSI laboratory seems to have seized the initiative in the AGI race in the short term. However, from a long-term perspective, this cooperation comes with multiple inherent flaws that are difficult to eliminate. If the contradiction between commercial profit and cutting-edge research cannot be balanced, global regulations and competition from rivals cannot be addressed, and financial risks brought about by capital circulation cannot be resolved, this heavy investment will not only fail to achieve the expected returns but will instead become a huge burden that hinders the long-term development of the enterprise.