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Analysts and pundits in finance have an endless supply of clever, simplistic explanations for complex market movements. Over the years, culprits have ranged from El Niño and climate change to the World Trade Organization, rock 'n' roll music and the fluoridation of water. Many of these explanations proved wrong, yet commentators continue to reach for overly simple narratives to explain market moves. The latest example is the sell-off in software stocks. For the past year, markets have vaporized roughly $2 trillion in software value by asking the wrong question about AI. The question behind the so-called "SaaSpocalypse" — can an AI agent do what this software does? — is misguided because it treats a software company as nothing more than a bundle of tasks waiting to be automated away. The right question, and the clearest test we know of for separating AI's winners from its roadkill, is different: Does this company own something AI agents cannot operate without? Salesforce's recent blowout earnings show why the balance of power may be shifting away from frontier models and back toward software companies that investors had prematurely left for dead.
The 'SaaSpocalypse' narrative has been such nonsense. Marc Benioff Salesforce CEO
For months, analysts have assumed that frontier large language models will accrue an ever-greater share of the value created by AI, with the valuations of those companies soaring commensurately. But intelligence is precisely the thing being commoditized before our eyes: Frontier models now leapfrog one another every few months, growing ever more capable but also increasingly interchangeable. AI may also be approaching RSI — recursive self-improvement — which could accelerate those improvement cycles even further, while the price of raw intelligence collapses amid heated competition from well-funded rivals and increasingly capable Chinese open-source models. So in this new world, if LLMs are the commodity, where is the moat? The answer, we believe, is data. As analysts at Wells Fargo put it, "Lower cost of intelligence increases value of incumbent data." That is Economics 101: When an input becomes abundant and cheap, value migrates to its scarce complements. And for AI agents, the scarce complement is trusted proprietary data. An agent closing a sale still needs somewhere to research the customer, log the interaction, store the contract and customize the terms. Without trusted proprietary data, AI agents are far less useful. As the old adage goes, "Junk in, junk out." Salesforce is one of the largest repositories of enterprise customer data in the world, and far from being the beneficiary of a lucky earnings beat or one-time aberration, it may be positioned as a long-term structural winner from the commoditization of AI. Agents cannot work without data, so the data keeps flooding in: Salesforce's Data 360 ingested a staggering 104 trillion customer records this quarter, up 355% year over year. Meanwhile, AI agents themselves generate still more data, all of which has to land somewhere trusted. Salesforce delivered 3.2 billion units of agentic work this quarter, nearly double the prior quarter. That flywheel — agents generate work, work generates data, data deepens the moat and makes each successive generation of AI agents more valuable — is why Salesforce's combined AI and data annual recurring revenue has reached $3.9 billion, more than tripling in a single year, as the accompanying chart shows. Agent force alone rocketed from $100 million to over $1.5 billion in ARR within 18 months of launch, up 240% year over year.