In the early hours of February 5, 2026, Anthropic unveiled Claude Opus 4.6, sending shockwaves from Silicon Valley to Manhattan. Software stocks plummeted despite strong earnings. Even Bitcoin tanked as investors fled to stable assets.
The Anthropic news signaled a broader threat: AI models are pushing into the application layer, destroying moats and forcing companies to rethink business models. If that feels familiar, that’s because we have been here before. In November 2022 ChatGPT made investors question even whether companies like Google had a future. In January 2025, China’s DeepSeek challenged the entire American approach to AI, which relied on costly cutting-edge hardware. The stocks of AI-powerhouses like NVIDIA took a beating. To say that both Google and NVIDIA have been doing fine since then would be an understatement. But this is not the reason why the recent panic is overblown.
Technology companies are driven by two types of cycles. The classic macroeconomic business cycle – recession, recovery, expansion, and slowdown – and various technology cycles. The latter ones describe phases of technological progress, hype, and adoption. Technology cycles are longer and eventually more important for investors. While the macroeconomic mood can cause hefty share price swings and temporarily limit access to capital, its impact is comparably short-lived. Moreover, in the tech world the macro often has little impact on the bottom line. Corporations will always spend on laptops for their employees and they will keep their firewalls up, no matter how the GDP is doing and what the FED decides.
But why do these AI shocks occur so frequently and are they a warning sign? The answer lies in an unfavorable overlapping of the market cycle and the technology standardization cycle. During all of the three AI shocks the American economy has been perceived near the end of an expansion phase with a looming slowdown. Tech stocks react very strongly to slowdowns and recessions, which is why investors have been nervous about them. Up to this day the fear of an AI bubble is specter on Wall Street. Technologically, on the other hand, GenAI has been in the phase of competition and ferment ever since ChatGPT launched it onto the world stage. This phase is marked by intense rivalry, frequent entry and exit of firms, and rapid, often chaotic, product changes. Every emerging technology passes through this stage but it is rare that both the macroeconomic environment as well as the technological dynamics are in an utter state of uncertainty. Add to this the gargantuan AI spending of companies of all hues and it is clear why investors get jittery and why market chatter about software carnage leads to massive sell-offs.
From a techno-market perspective there is no reason for this pessimism. While Claude Opus 4.6. was a significant leap in AI-capabilities, it did not change the technology’s trajectory. We all knew that things were going that way. There was never a shortage of grand claims about AI’s potential to eliminate coders. A software company without deep specialized industry knowledge or unique feedback data was always expected to be displaced by GenAI at some point. The Anthropic announcement might have expedited the timeline, but it didn’t change any investment-theses for tech investors. This is corroborated by the fact that the Bitcoin price plunged on the Anthropic news. Bitcoin has always been driven primarily by the economic mood, not technological developments. It reacts to sentiment changes even stronger than tech stocks and is therefore a good signpost as to how much of the price swings is driven purely by sentiment.
Given the tremendous funding volumes and the sheer endless application possibilities of GenAI, the technology will most likely stay in this dynamic and chaotic phase for another couple of years. But long-term investors can gain an edge by tuning out the short-term noise and instead focusing on what follows after this phase.
The next stage in the technology standardization cycle is where the dominant design of a technology is established and scaled. The direction becomes clear and the question turns to who can get to the goal fastest. Research shows that in this phase large companies, often diversifying incumbents, perform best. In the case of GenAI, these are cloud computing heavyweights and AI scalers. Furthermore, should the market mood sour, they are the least affected group, as they are not reliant on external capital. Yet in an AI-driven world of relentless change, the enduring winners won’t be the behemoths alone. They’ll include those nimble, highly specialized players that are adaptive enough to harness the AI basics built by others and, if necessary, can even pull off a business model pivot. This rang true amid the ChatGPT frenzy of 2022, holds firm today, and will endure through 2030.
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