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a16z General Partners David Haber, Alex Rampell, and Erik Torenberg discuss why 19 out of 20 AI startups building the same thing will die - and why the survivor might charge $20,000 for what used to c...
Three a16z partners dissect why AI moats still matter despite unprecedented competition. They reveal that while 20 companies might build identical products, only 1-2 will survive—but the survivor can charge $20,000 for what used to be a $20 feature because it now replaces entire employees. The key insight: AI hasn't killed defensibility, it's shifted the market from IT spend to labor spend, creating trillion-dollar opportunities in previously untouchable markets like plaintiff law and auto loan servicing. Success requires reaching 'gravitational scale' before competitors, operating in the 'Goldilocks zone' where you're too small for giants to care but big enough to build moats.
David Haber argues that AI is a differentiation tool, not a defensibility mechanism. The fundamental moats remain the same: owning end-to-end workflows, becoming the system of record, and embedding deeply within customers. The critical difference is that AI software can now do actual work, shifting the addressable market from IT spend to labor spend—a vastly larger opportunity.
Alex Rampell introduces the 'janitorial services problem'—companies operating in a sweet spot where they're too small for customers to care about switching (9% improvement isn't worth the mental energy) but sticky enough to never lose customers. Examples include payroll companies like ADP and Paychex, worth hundreds of billions despite being theoretically replaceable.
The partners explain why momentum isn't a moat but is essential for survival. With 20 companies building identical products, only those reaching 'gravitational scale' quickly enough will survive. The key is patient capital targeting greenfield opportunities with high rates of new company creation, avoiding markets like hospital EHR systems where customer creation rounds to zero.
David Haber reveals a counterintuitive pattern: the best vertical AI companies are built by technical founders who aren't native to the industry but hire for context early. Example: Eve (plaintiff law AI) founded by Rubrik engineers who hired plaintiff attorneys on staff to understand how each new model release impacts legal workflows. The technology must reinforce, not compete with, the business model.
AI has created a 'Cambrian explosion' of viable markets that were previously too small or boring for software. Examples include plaintiff law, auto loan servicing (Salient), and orthodontic clinic management. These markets were never interesting when TAM was IT spend, but become massive when TAM is labor replacement. OpenAI won't compete in these spaces—they're gold bricks 100 feet away while OpenAI has gold bricks at their feet.
The traditional hierarchy of feature < product < company has been disrupted. Features can now command $20,000/year because they replace entire employees (e.g., orthodontic clinic receptionist). The key is rapidly backfilling the feature with product capabilities before platform owners wake up. Steve Jobs told Drew Houston Dropbox was 'just a feature'—but Houston had a plan to build a company.
David Haber explains the 'messy inbox' wedge: hook into unstructured data sources (email, fax, phone), extract information with AI, plug into downstream systems of record. Example: Tenor extracts patient info from messy sources, feeds EHRs, then expands to scheduling, prior auth, eligibility—eventually becoming the system of record itself. This wedge works because it lives upstream of existing software.
Unlike cloud and mobile, AI adoption is completely consensus—no CEO is saying 'nobody will use that tool that makes you 100x more productive.' This eliminates the white space that enabled previous disruptions (Workday vs. PeopleSoft, Salesforce vs. Siebel). Incumbents aren't screwing up or ignoring AI. The opportunity exists in markets too small for incumbents to care about, not in disrupting existing categories.
Business process outsourcing companies (Tata, Wipro, Infosys) face existential questions. Bull case: they maintain relationships (e.g., JPMorgan), add AI, reduce headcount 100x, and make 100x more profit. Bear case: customers partner directly with AI startups or build in-house, eliminating BPO relationships entirely. The outcome is genuinely uncertain and explains public market volatility.
Alex Rampell debunks the job destruction narrative with the Uber analogy: people didn't take fewer taxi rides post-Uber, they took massively more rides because abundance and lower costs created new demand. Similarly, AI won't eliminate jobs—it will enable tasks that were never economically viable at human labor costs. Example: every JPMorgan customer could have a personal financial assistant for questions like 'I can't download the app.'
Why AI Moats Still Matter (And How They've Changed)
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