The Day AI Disrupts Software Pricing: 3 Perspectives to Distinguish Stocks to Sell from Stocks to Keep
The "software sell-off" in U.S. stocks shows no signs of stopping. It was triggered by individual earnings reports and guidance—but at its core lies the fear that generative AI is shaking the very foundations of software pricing and competitive moats. On a Bloomberg program, this situation was framed as "stocks on the 'other side' of the AI revolution are being sold off," while also discussing the turning point between winners and losers, and the overarching theme of how "power demand" is driving the next investment narrative.
1. The True Nature of the 'AI Shock' Causing Software Stocks to Sell Off
A symbolic point made on the program was the view that "if AI can write software, the cost of creating software will plummet." This is not just about efficiency; it is about how we estimate long-term profits (terminal value).
In other words, investors are bracing for the possibility that the equations that have underpinned SaaS valuations until now—such as "feature scarcity," "development costs," and "headcount growth equals revenue growth"—might collapse. As a result, the market's instinct is to "sell first, ask later."
2. The 'Two Truths' Existing Simultaneously in the Market
What makes the program's discussion interesting is that it clearly identified two conflicting "truths" occurring at the same time.
Truth A: AI bubble concerns (excessive investment might lead to disappointment)
Truth B: AI is already powerful and capable of disrupting industries (actual replacement might be underway)
Because of this contradiction, investors fear "where the next collapse will occur," causing suspicion to spread to a wide range of stocks, including finance, insurance, and tax software. The program also mentioned names like Charles Schwab and Raymond James.
3. Amidst the 'Sell Everything' Sentiment, How to Select Stocks
The point is to re-apply the obvious fact that "not all software is the same" to investment decisions. The program's breakdown was practical.
Small to mid-sized 'point solutions': easily swallowed by adjacent giants along with their features
Large-scale 'platforms': parts of their business will be damaged, but it is not a total negation (though margin structures will change)
Security, enterprise, and SMB-focused: even within software, the 'terrain' differs
The important message here is that "bottom fishing" requires not courage, but an understanding of architecture (what is differentiated and where).
4. Why Hardware and 'Advertising/Purchasing' Are Growing While Software Falters
The program repeatedly contrasted the idea that "software is a source of anxiety, but infrastructure spending will continue." As AI demand grows, investment in computing resources, power, and data centers becomes necessary—this is the structure.
Symbolic of this is how Google is embedding "buy" pathways into its AI search/chat experience, attempting to reshape the form of advertising. Vidhya Srinivasan on the program noted that in AI mode, "the binary choice between 'fast' or 'smart' shopping disappears," suggesting a concept where the conversation flow leads directly to purchases and direct offers.
5. The Bottleneck of the AI Era is 'Power'—Capital Flows Toward Nuclear Fusion
Another major theme of the program was that the next constraint for AI is shifting from "semiconductors" to "power." This is where the nuclear fusion startup co-founded by Jeff Lawson, Inertia, comes in. Having raised $450 million in Series A funding, the program discussed their vision of moving from "the lab to the power grid" using a laser-based approach.
Furthermore, the program introduced Doug Burgum's statement that "AI competition requires more power," highlighting the atmosphere where increasing energy capacity has become a national priority.
6. The Imagination of Capital Markets Connecting Even to "Space x AI"
In the latter half, the "narrative of massive capital," such as the integration and restructuring of SpaceX and xAI and future IPO speculation, was also brought to the table. In fact, reports of post-integration corporate value, listing speculation, and organizational restructuring have been appearing one after another.
However, what is important here is not the gossip, but the reality that how to procure and sustain AI demand (computing, power, and capital) has become corporate strategy itself.
