In such a fast-moving and dynamic environment, long-term technological progress and shorter-term public market expectations are unlikely to move forward in lockstep. Instead, there will likely continue to be times when investor sentiment overvalues—or undervalues—the evolving business and technological realities. Given that ongoing dynamic, gaining a greater understanding of the AI ecosystem may offer investors a more nuanced range of options amid the volatility and ambiguity.
Beyond the Binary
It’s tempting when evaluating the potential impact of a new technology to try to draw up a list of “winners” and “losers,” based on how likely the new technology may be to benefit or harm existing industries or companies. However, we believe the evolution and proliferation of AI is not dividing companies or industries into binary categories of winners and losers. Instead, many companies are likely to experience both positive and negative impacts from AI.
While picking winners and losers may be difficult, market narratives around these companies are always in flux. As those narratives and expectations evolve, shifts in investor sentiment often translate into flows of investment into—or out of—a company’s stock.
At some points in time, for example, investors may view the big, well-known technology firms, sometimes known as hyperscalers, as major beneficiaries of AI and be willing to pay high prices for those companies’ shares.
However, at another time, those investors may shift their focus to hyperscalers’ elevated capital spending1 and opt to reduce their exposure to these stocks. In both instances, investors are considering the same fundamentals but weighing them differently, resulting in volatility and anxiety about the return on capital being spent. Over the past two years, we’ve observed this push-forward-then-fall-back pattern in investor sentiment playing out.
Meet the AI Ecosystem
- Hyperscalers. Large well-known technology-platform companies are investing heavily in AI infrastructure. They’re major allocators of capital within the ecosystem. An important question for this group is whether returns will ultimately justify their current level of spending.
- Infrastructure suppliers. These are the companies providing the underlying tools and components required for AI development. They enable the broader build-out of the AI ecosystem.
- Closed-model providers. These are firms that develop proprietary or so-called frontier large-language models.
- Open-model providers. Organizations developing open-source alternative models from China as well as the US exist as a separate competitive force within the AI ecosystem.
- Productivity beneficiaries. These aren’t necessarily technology companies. Instead, they are organizations that use AI to do what they do better, whether that be to increase productivity, lower costs, or improve efficiency.
- Potentially disrupted companies. These are firms such as software companies that may be negatively affected by AI. The degree to which they may be disrupted remains open to debate, and their outcomes remain unsettled.
- New business models. These may arise from advances in AI technology and could come from both existing companies and entirely new ones, much as happened during the growth of the world wide web in the 1990s and 2000s.
