According to TechCrunch, veteran investor Elad Gil stated at TechCrunch Disrupt that AI has been one of the least predictable tech booms he’s ever seen, despite having backed numerous successful AI companies including OpenAI, Mistral, Perplexity, Harvey, Character.ai, Decagon, and Abridge. Gil noted that while he began investing in generative AI in 2021 when few were paying attention, the massive capability leap between GPT-2 in 2019 and GPT-3 in 2021 convinced him of the technology’s importance. He now sees clear winners emerging in foundational models (Google, Anthropic, OpenAI, possibly xAI, Meta, and Mistral), AI-assisted coding (Cursor, Devin, Magic), medical transcription (Abridge), and customer support (Decagon, which raised $131 million at a $1.5 billion valuation in June according to their announcement). Meanwhile, Gil identifies financial tooling, accounting, and AI security as wide-open markets where leadership remains uncertain despite enterprise enthusiasm.
The Consolidation Patterns Emerging in AI
What Gil is observing represents a classic technology adoption pattern playing out at unprecedented speed. Markets where network effects, data moats, or scale advantages create winner-take-most dynamics are rapidly consolidating. Foundational models require billions in compute investment and proprietary training data that create nearly insurmountable barriers for new entrants. Similarly, AI coding assistants benefit from user feedback loops that continuously improve their capabilities, creating a flywheel effect that advantages early leaders. The rapid enterprise adoption Gil mentions—where CEOs are mandating AI initiatives—is accelerating this consolidation by funneling corporate budgets toward established players rather than experimental startups.
The Danger of False Enterprise Signals
Gil’s observation about fast revenue growth being a potential “false signal” highlights a critical risk in today’s AI market. Enterprise customers are conducting widespread AI experiments with substantial budgets, but these initial contracts often represent exploratory spending rather than committed long-term partnerships. Many enterprises are running parallel proofs-of-concept with multiple vendors, creating the illusion of sustainable revenue for startups that may not survive the eventual consolidation. This phenomenon explains why companies like Harvey—which Gil cites as “just working”—can achieve such dramatic valuation jumps while others with similar early traction might eventually falter when enterprises standardize on fewer vendors.
Where True Opportunities Remain
The markets Gil identifies as wide open—financial tooling, accounting, AI security—share important characteristics that prevent early consolidation. These are typically vertical-specific applications requiring deep domain expertise rather than general-purpose AI capabilities. Financial services and accounting involve complex regulatory requirements and specialized workflows that create implementation barriers protecting against rapid market domination. AI security represents an emerging category where the threat landscape itself is still evolving, preventing any single approach from achieving dominance. These markets reward specialized knowledge and careful implementation over raw AI capability, creating opportunities for focused startups to build sustainable businesses.
The Enterprise Adoption Acceleration
Gil’s comments about enterprise willingness to experiment reflect a fundamental shift in corporate technology adoption patterns. Historically, enterprises moved cautiously with new technologies, conducting lengthy evaluations and phased rollouts. The AI boom has compressed these timelines dramatically, with companies feeling competitive pressure to adopt AI capabilities before their rivals. This creates both opportunity and risk for startups—the opportunity to land major enterprise deals quickly, but the risk of being displaced when those same enterprises eventually standardize their AI stack. The companies that will ultimately win in consolidated markets will be those that can transition from experimental projects to mission-critical infrastructure.
Investment Implications for the Next Phase
For investors and entrepreneurs, Gil’s analysis suggests a strategic pivot toward markets where specialization creates defensible positions rather than competing in already-crowded horizontal applications. The early AI gold rush focused on building foundational capabilities and general-purpose applications, but the next wave of value creation will come from solving specific business problems in regulated or complex domains. Companies targeting Gil’s identified open markets should focus on building deep vertical expertise and integration capabilities rather than purely technological differentiation, as these factors will determine which startups can convert early enterprise experimentation into lasting customer relationships.
