Back Research Notes Why Buying “Cheap” Software Is the New AI Bubble Trade Published on January 20, 2026 By Jordi Visser From Ozempic to AI Agents: How Demand Gets Suppressed Last year, between X posts and conversations with investors, one of the most common AI questions I heard was simple: When do I buy software stocks? After I wrote a paper at the end of 2025 arguing that software was entering a re-rating phase, many investors reached out to say that re-rating had already happened. This year, there are fewer questions about an AI bubble, but the software questions persist and they sound more defiant than curious. Much of that defiance comes from price action. Investors look at the parabolic charts of scarcity-driven hardware winners like Micron and SanDisk and compare them to the abundance-era drawdowns of Salesforce, Adobe, and ServiceNow. From that comparison, they infer value in what has fallen and excess in what has run. I think they have it exactly backward. Stepping in to buy software because it looks “overdone” ignores what is actually happening in AI. Will there be relative bounces? Of course. But this isn’t a typical sector rotation or a buying opportunity in beaten-down software names. We are watching the opening act of a demand-destruction cycle, driven by the steady deflationary pressure of exponential AI progress, where coding is increasingly ubiquitous and effectively free. They are confusing the suppressant for the bubble and the bloat for the value. To understand why this is different from every prior software cycle, you have to start with how companies historically decided to adopt software in the first place. For decades, the build-versus-buy decision acted as a natural brake on software sprawl. Building software was expensive, slow, and risky, which made buying SaaS the rational default for most organizations. That friction didn’t just shape IT decisions; it enabled bloat. Buying software became easier than saying no. Every workflow justified a new vendor, every siloed department accumulated its own stack, and administrative surface area quietly expanded. AI removes that brake. When software can be generated, modified, and orchestrated by agents at near-zero marginal cost, “build” stops being a strategic project and becomes an everyday action. At that point, the entire buy-side market for software isn’t disrupted, it’s suppressed. The Era of Biological Bloatware For four decades, the processed food industry perfected a simple formula: engineer products to hijack biological reward systems, then scale distribution to make them ubiquitous. This wasn’t a competition between brands for market share; it was the systematic expansion of total caloric consumption. The consumer packaged goods industry grew not by stealing share from competitors, but by expanding the total addressable market for food itself. They made people hungrier. The architecture was biological bloatware. Human metabolism evolved for scarcity on the savanna, where every craving was a survival mechanism. In an environment of engineered abundance, that legacy code became pathological. Hyper-processed combinations of salt, sugar, and fat could override satiety signals. This created “Food Noise”, the constant, low-frequency hum of inadequacy that drives us to consume more than we need. The more the food industry sold, the hungrier people became. Appetite expansion became the business model. The Rise of Software Metabolic Syndrome The enterprise software industry followed an eerily similar playbook. The SaaS revolution didn’t compete for a fixed pool of IT spend; it expanded the total surface area of software consumption by creating administrative layers that hadn’t existed before. Every workflow became a separate subscription. Every department got its own point solution. Every integration became a paid connector. The result was Software Metabolic Syndrome. Enterprises now manage hundreds of disconnected tools, requiring thousands of employee hours just to move data between them. The software industry reached its current scale not by solving problems, but by creating middlemen. Friction became the revenue model. Just like the food industry, SaaS companies co-expanded the total addressable market by creating new categories of administrative work, tasks that exist only because the software itself is fragmented. We grew used to the “Software Noise”, the endless pings, the tab-switching, and the manual data entry that fills the modern workday. We mistook this noise for productivity, much like we mistook the craving for hyper-processed sugar for genuine hunger. Chamath Palihapitiya and the Order of Magnitude Shrink Chamath Palihapitiya has been one of the voices consistently identifying this “bloat” as a terminal condition rather than a temporary slump. On the All-In Podcast’s 2025 and 2026 outlooks, he didn’t just predict a downturn; he predicted a structural collapse of the enterprise software market as we know it. Palihapitiya’s thesis is that the market for enterprise software is set to shrink by an order of magnitude. He famously noted that instead of an industry fighting over a $5 trillion pie, we are moving toward a world where we are fighting over $500 billion. This isn’t because businesses will do less; it’s because the cost of doing those things is being liquidated. Chamath’s move to launch 8090, his “AI Software Factory,” is a direct bet on this deflation. The company’s name, representing the goal of providing 80% feature completeness at a 90% discount, is a declaration of war on the legacy SaaS pricing model. If a solo founder can use an AI factory to build a billion-dollar product without an engineering team, the thousands of “middle-layer” software companies currently charging $200 per seat per month have no floor beneath them. Their “value” was always tied to the friction of hiring 500 engineers. When that friction vanishes, so does the valuation. The Mechanism of Suppression: Liquidating the Middleman Now, both industries face the same existential threat: internal appetite suppression rather than external competition. GLP-1 medications are eliminating food noise. Agentic AI is eliminating software noise. Both are appetite suppressants. Both are liquidating the middleman, whether that is the biological craving or the digital interface. Exponential Progress vs Linear Organizations There is a second force accelerating this suppression, and it explains why legacy software companies are unable to keep pace even when they recognize the threat. AI progress is exponential. Software organizations are linear. Modern AI systems do not improve incrementally. They advance in step functions. Capabilities that once required entire product categories, large engineering teams, and months of implementation suddenly collapse into a prompt. Costs fall faster than pricing models can adjust. What was impossible last quarter becomes trivial this quarter. Enterprise software companies are not built for this environment. Their roadmaps are annual. Their release cycles are quarterly. Their architectures must preserve backward compatibility. Their incentives reward revenue protection, not self-cannibalization. Even when they “add AI,” they are embedding a rapidly moving intelligence frontier into a fixed product surface. This creates an unwinnable competitive dynamic. Legacy software is no longer competing with other vendors. It is competing with the rate of AI progress itself. Every improvement in model capability compresses the need for a standalone workflow, a dedicated interface, or a paid seat. By the time an incumbent refactors for today’s AI, tomorrow’s models have already erased the need for the workflow entirely. The suppressants are not just reducing demand. They are advancing faster than the organizations built to metabolize them. The critical insight from the early adoption of GLP-1s is that users aren’t simply switching to “healthier” software; they are consuming less overal