AI Labs Ditch Models for Consultants as SaaS Market Loses $Billions in Value

Table of Contents The enterprise software sector is undergoing a structural shift as artificial intelligence reshapes how businesses buy and deploy technology. Valuations across public SaaS companies have declined sharply, with 90% of stocks trading 30–80% below their 52-week highs. Meanwhile, leading AI labs are moving beyond model development into hands-on consulting. The combined moves signal a broader realignment of where value is created in the software industry. The first segment to feel the pressure has been lightweight, single-function software products. Tools priced around $49 per seat per month are losing ground to AI agents that replicate their functions automatically. Businesses no longer purchase a dedicated tool for a narrow task — they describe what they need, and AI builds and executes it. Milk Road AI noted on X that “the low end of the market is basically finished,” citing investor Chamath Palihapitiya’s recent diagnosis of the sector. Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS… pic.twitter.com/2CwMuGfHbV — Milk Road AI (@MilkRoadAI) May 17, 2026 The seat-based pricing model that built the SaaS industry does not translate to this new transaction type. As a result, the economic foundation of many small business software products is eroding. Goldman Sachs data reflects the broader damage. Software forward price-to-earnings multiples dropped from 35x to 20x — the lowest level since 2014. That multiple is also the smallest premium to the S&P 500 since 2010, pointing to a sector-wide reassessment of growth prospects. Mid-market point solutions without proprietary data assets face a similar trajectory. Products that lack defensible data flywheels or deep vertical integration are increasingly vulnerable to displacement by general-purpose AI systems. At the high end of the market, the challenge is different — but no less serious. OpenAI recently raised $4 billion from investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting division. The venture targets direct competition with firms like Deloitte, PwC, Ernst & Young, and Accenture. The structure of the deal is notable. Investors were guaranteed a 17.5% annual return — roughly $700 million per year — from a company projected to lose $14 billion in 2026. The move came after OpenAI’s enterprise LLM market share dropped from 50% to 25% between late 2023 and mid-2025, with Anthropic rising to 32%. Anthropic responded almost immediately with a competing $1.5 billion consulting venture, backed by Blackstone, Goldman Sachs, and Hellman & Friedman. Together, the two labs committed $5.5 billion to human-powered enterprise deployment within a single month. The scale of that spending reflects how difficult real-world AI implementation remains. Data supports that difficulty. Some 88% of organizations running AI agents reported a security incident in the past year. Additionally, 42% of C-suite executives said AI adoption is generating internal organizational conflict. Average year-one implementation costs through consulting run $228,000 — nearly three times the platform-based alternative.