Analysis of Semiconductor Cyclicity and AI Market Expectations

In this article I would like to highlight a few important viewpoints based on economic theories and data:

1. Efficient market hypothesis (EMH) & rising competition. This article does not discuss the hard version of EMH, but the soft version, which allows temporary market inefficiencies.
2. Semiconductor business cyclicity & extrapolation of growth in markets
3. Large-scale investment & AI commoditization: AI model efficiency improvements & competition & OSS alternatives
4. Beneficiaries of AI

This article presents a long-term analytical view. The main thesis should be understood as a rough directional assessment rather than a precise forecast, and may remain relevant during the 2026-2028 period if market conditions do not change materially.

1. AI Hardware Economics and Competitive Pressures

NVIDIA’s gross margins have recently remained around 70–75%, which is historically exceptional for the semiconductor industry [1]. To compare this to any other sector in economics this is unprecedented and based on EMH it is most likely not long-lasting in the long term. There are already signals of rising competition like AMD, Intel, Google's TPUs, and many other companies developing and investing to AI semiconductor GPU computing hardware. Margins of 70–75% is natural to attract high amount of competition and so investments. These investments will drive capacity of AI related semiconductors. There are also empirical data-based signals about lowering margins already as Photo 1 suggests based on current data.

Photo 1: NVIDIA gross margin history [1].

2. Semiconductor Cyclicity and Growth Extrapolation

Cyclicality in semiconductor markets is not a new phenomenon, and historical data indicate that the industry is characterized by significant cyclicality, as shown in Photo 2.

Photo 2: worldwide semiconductor revenues [2].

Despite the semiconductor industry’s pronounced cyclicality, long-term revenue growth in AI-related compute infrastructure has materially outpaced broader economic growth. For equity investors, however, the key question is not only whether growth exists, but at what price that growth is being capitalized. Current valuations imply that recent demand strength, particularly in AI infrastructure, can persist at an unusually elevated rate far into the future. Given the sector’s historical cyclicality, the durability of current growth rates is difficult to estimate with confidence, and linear extrapolation from an exceptional demand phase carries a meaningful risk of valuation error.

3. AI Competition, Efficiency, and Commoditization

Open Source Models and Declining Compute Intensity

Important aspects to consider also include the rising amount of competition, the rise of free open-source software (FOSS) in AI technology, rising popularity of locally run AI models, improved model algorithms & efficiency and how AI models are used in some cases practically. These are mutually reinforcing factors. As open source models are getting significantly more efficient it is reducing the need for computing capacity running the models, rising competition may be making GPUs more affordable by reduced margins and so encouraging for local running or the models for reduced costs and increased privacy & security.

AI Commoditization as a Market Outcome

All of these factors, combined with the considerations in previous sections of this article, make the commoditization of AI one well-founded future scenario justified by empirical data and economic reasoning.

4. Narrative-Driven Markets and Mispriced Expectations

In simplified terms markets believed in 2024 that "AI will kill Google search" and about 1 year later from that point Google's share price has increased 100 - 120%. Now the more recent sentiment in the market has been "AI is killing all SaaS". These anecdotes could be considered as an example of narrative-driven market conditions and part of behavioral economics phenomenon that allows soft version of efficient market hypothesis be plausible instead of the hard version of the EMH. Clearly the first market sentiment thesis (google) did not turn out to be true. The thesis about the SaaS sector although we do not have clarity yet at this point, but based on considerations about underlying theories just mentioned the sector may be undervalued if the assumptions are not based on real fundamentals of the businesses in the sector as it was the case with the Google.

Instead of AI killing SaaS and commoditizing software, the data and reasoning presented in this article suggest that an equally plausible outcome is that AI itself becomes commoditized, while SaaS companies and traditional industries emerge as the largest beneficiaries.