OpEx is the New CapEx

How to evaluate AI startups' financial health

The world of technology is no stranger to hype cycles, and the artificial intelligence (AI) space is no exception. As we witness increasing investments in AI startups, particularly those focused on training large machine learning (ML) models, it is crucial to assess if this trend is driven primarily by hype or if it truly represents a sustainable model for value creation.

In recent years, one area that has attracted significant attention and capital is the development of large ML models, especially after ChatGPT’s phenomenal rise to fame. These models require massive computing resources and specialized expertise, leading to high OpEx in the form of compute costs and headcount cost. The OpEx of AI startups can run into hundreds of millions each year, as is shown by DeepMind (~$500m a year per its official financial record) and OpenAI. This unprecedented burn-rate would pose a large strain on a company’s financial health and its survivability.

In order to adapt to this new reality, there's a growing trend of investors treating Operating Expenditures (OpEx) as the new Capital Expenditures (CapEx). This shift is due to the unique financial dynamics of the AI industry, where startups often require massive amounts of computational resources and expertise to develop cutting-edge products and services.

While some AI startups have demonstrated impressive technological feats in natural language processing, computer vision, and reinforcement learning, the path to monetizing these achievements at meaningful scale remains ellusive at best. As such, the influx of capital into this space raises questions about the sustainability of this trend and the potential for another investment bubble.

OpEx without Revenue is no different from CapEx

One of the hallmarks of AI startups is their ability to consume vast amounts of OpEx, often into the hundreds of millions per year. In traditional industries, this level of expenditure would typically be associated with CapEx, where companies invest in long-term assets to drive future growth. However, AI startups can burn through these funds without generating substantial revenue, as they invest heavily in research, development, and talent acquisition to push the boundaries of artificial intelligence.

This cash flow behavior highlights the need to reassess how we categorize these expenses and evaluate AI startups' financial performance, which in turn determines an investor’s real return from betting on these projects.

To better understand the dynamics at play, we can draw comparisons to the ridesharing market, which saw a similar influx of capital in the early 2010s. Investors poured billions into Uber and Lyft, with the expectation that their rapid growth would lead to market dominance and ultimately, profitability. However, despite impressive growth in both user base and revenue, both companies have yet to achieve consistent profitability. For Uber and Lyft, the bulk of capital went towards subsidizing rides in an attempt to win market share and achieve economies of scale.

While early investors profited from subsequent funding rounds and IPOs, the majority of capital invested in these companies yielded little, if not negative, returns. This demonstrates the risks associated with investing in high-growth, high-burn rate ventures, where the path to profitability remains uncertain.

Why Cloud Expenditure is more CapEx than OpEx in the AI Industry

Returning to the AI space, we observe that the high OpEx associated with training large ML models could be viewed as a de facto CapEx. The compute costs are significant and unavoidable, as the quality of ML models is directly tied to the size of model and data, which translates to computational power used in the training & inference process.

The analogy bears even more sense when one observe the predictability of AI OpEx. From an accounting perspective, the point of seperating different expenses into buckets is precisely because of similarity of spending “pattern” within the bucket. CapEx, for example, is expected to be:

• planned ahead and paid upfront

• large amount

• eligable for depreciation (which makes the P&L looks much better)

The traditional OpEx on the otherhand, is paid on-demand and generally at smaller amount. However in reality, many AI startups choose to lease cloud GPUs annually to save cost, instead of paying on-demand (much higher per unit cost), or waiting for spot instance (almost no capacity available since everyone is grabbing as many GPU as they can get their hands on).

These cloud expenditures often have a predictable and upfront cost structure, making them more similar to traditional CapEx than variable OpEx. In addition, the intense competition for top ML talent has driven up salaries and increased the cost of hiring and retaining skilled researchers and engineers. In a tight labor market today, companies seem to prefer “hoarding” talent over headhunting them as needed. This is especially true in the AI market where the supply of top talents are almost inelastic with top schools like Stanford, CMU only churning out numbered PhDs and dropouts each year.

How to invest SMART in a hype?

To be clear, I have no doubt that generative AI and large ML models will reshape how we work and live at a fundamental level, during which trillions dollar worth of value will be created. And I have been seed investing in this space since 2016. Yet, as an investor myself, I have personally seen enough “good ideas” and “good projects” end up in flame due to poor planning and poor execution.

As an AI investment thesis, I’m pretty confident that if one were to put together a portfolio of MSFT/Nvidia/Google weighted by their market cap, this portfolio will outperform at least 75% of AI-themed pe/vc funds in the next 10 years.

I can say this with some confidence cuz a similar portfolio strategy (weighted FAANG) indeed outperformed most TMT-themed pe/vc during 2010-2020; another similar portfolio strategy (weighted Alibaba, Tencent, Baidu) also outperformed most China funds during 2005-2015; same is true for crypto (weighted BTC/ETH vs Crypto funds).

One should also note that this is, by no means, the first AI hype. In the early 2010s, when deep learning first became a thing, leading AI researchers lined up to form paper companies with no visible business plan and was able to raise billions collectively. One of these companies recently got acquired at less than 1/10 the valuation at which they raised their seed round.

The lesson here is elementary (no pun intended).

Summary

AI startups often claim to be "lean" due to their low CapEx requirements in the traditional sense. However, this argument can be misleading, as it overlooks the massive OpEx commitments these companies undertake. In some ways, this perspective resembles the accounting tricks used by companies that capitalize expenses through depreciation to make their Profit & Loss (P&L) statements look more favorable. To gain a more accurate understanding of an AI startup's financial health, it's essential to peel back these accounting “tricks” and focus on the real cash flow dynamics that drive these businesses.

Ultimately, the sustainability of the AI startup ecosystem will depend on the development of viable business models that can monetize the impressive technological advancements in this space. As the market matures and the hype subsides, the true winners in this sector will be those who can successfully navigate the challenges of high OpEx and create lasting value for their investors and customers alike.