The Moat Has Really Moved: Even Anthropic Is Desperately Hiring Salespeople
Deep thoughts on AI and aspirations —— ByteDance Deep Thinking Circle
Open Anthropic’s careers page and you’ll notice something unusual: the department with the most openings isn’t AI research or product engineering—it’s sales.
This could be dismissed as a one-off. Model companies chasing enterprise revenue naturally need more salespeople. But zoom out and this detail becomes a microcosm of an industry-wide moat migration: even AI companies with the deepest product moats are allocating most of their hiring budget to “how to sell” rather than “how to build.” Behind this lies a judgment worth taking seriously. The moat has moved away from the product and into distribution: how you make people aware of you, trust you, and choose you among countless similar offerings.
The Moat’s Four Migrations
The history of enterprise software is the history of moats constantly migrating outward. Each era has a constraining cost variable. Whoever controls it has a barrier to entry. When new technology collapses that cost, the moat shifts one step outward.
The first era was capital plus technology. Software meant CDs and server racks, deployment cycles of 12 to 24 months. The barrier was capital itself. Only companies that could sustain years of cash burn could get in the customer’s door.
The second era was SaaS cloudification. When same-day provisioning replaced multi-year deployment, the capital barrier collapsed. The moat shifted to product construction and sales systems: multi-tenant architecture, SDR/AE machinery, partner ecosystems.
The third era was PLG. The product itself became the sales funnel. Users tried before buying. Slack and Figma both reached billion-dollar valuations without a single salesperson. The moat shifted to viral growth and community.
The fourth era is now. The first three migrations share a common trait: they compressed costs “around the product”—deployment, sales, procurement friction. None compressed the cost of “building the product itself.” AI is the first. A two- or three-person team with the right toolchain can build in days what previously took dozens of engineers months. The window for replicating features has shrunk from months to days. You launch a feature users love, and similar competitors ship comparable versions a week later.
The barrier has been forced to the last place it cannot be moved: the audience, and the ability to reach that audience.
Distribution Beats Product—This Isn’t New
This pattern wasn’t invented by AI. It’s played out repeatedly over the past thirty years.
Salesforce versus Siebel. Siebel had deeper CRM and stronger enterprise relationships, with contracts starting at a minimum of $5 million. Salesforce had a thinner product at $50 per seat per month, but hired actors in red T-shirts to protest outside Siebel’s user conference, holding signs shouting “End Software,” attracting police, bystanders, and free Fortune magazine coverage. The outcome: Siebel sold to Oracle for $5.85 billion. Today Salesforce has a market cap exceeding $280 billion—roughly 50 times that figure.
HubSpot versus Marketo. Founded the same year, Marketo had more mature marketing automation. HubSpot did something more aggressive: wrote a book, named a category called “inbound marketing,” then spent half its marketing budget evangelizing that category—annual conferences, certification programs, free CRM, dominating virtually every important search term in the industry. By the mid-2010s, “inbound” was practically synonymous with HubSpot.
Notion versus Evernote. Evernote had a ten-year head start and 200 million registered users. Notion’s playbook was community: a landing page inviting power users to participate deeply, selecting 20 ambassadors from 400 applications. These people created templates, recorded tutorials, answered questions in forums at 3 a.M. Today the vast majority of Notion’s new users come from referrals.
All three stories follow the identical pattern: whoever names the category first, establishes trust first, occupies user mindshare first outlasts competitors with functionally similar products. The difference is that this used to be an exception path. Now it’s the only durable path.
What the Numbers Say
There’s a batch of public benchmark data to cross-reference. The median AI-native startup reaches approximately $2.1 million ARR by month twelve and closes Series A nine months after generating revenue. Top-quartile companies hit $5.3 million in year one. A decade ago, “best-in-class” for SaaS startups was $1 million in year one. What was once peak performance is now below the AI company median.
Another founder survey shows the number-one issue keeping founders up at night has become GTM execution: how to sell, how to reach audiences, how to build a brand—ranked ahead of product execution, fundraising, and AI strategy. Founders voted with their anxiety: whether you can build it is no longer the hardest question. Whether you can make people know, trust, and choose you is.
This also explains why “brand first, product follows” has become the dominant playbook. Cursor first had a developer community that couldn’t stop discussing it, then became the fastest company in B2B to reach a billion dollars in ARR. Legal AI player Harvey locked in over half of the top-tier law firms before its product could meet all buyer needs. By the time competitors caught up on features, Harvey was already the default option in the category.
But the Product Must Still Be Excellent
The biggest trap here is pushing the conclusion too far.
Counter-evidence is right in front of us. The moat for model-layer companies still rests on research and compute. No matter how many salespeople Anthropic hires, its moat isn’t built by stacking sales roles. The “distribution era” thesis means products no longer automatically form barriers—on the condition that the product itself is excellent. The requirement hasn’t changed, only the sequence. Companies that treat distribution as a panacea while their product is terrible will only expose themselves faster.
Another hidden risk is distribution inflation. When every company competes for mindshare, creates content, and names categories, the cost of attention capture will rise again. The “first-mover” window keeps shrinking. At that stage, what truly endures is often “deeply trusted within small circles”—names repeatedly validated in physician circles, lawyer circles, developer circles. Deep trust is harder to replicate than broad awareness.
My judgment: the moat has indeed moved, but it hasn’t necessarily moved to a place money can buy. It’s moved to your first cohort of users—why they chose you, whether they’ll speak on your behalf, whether you’re worthy of their sustained trust. Products can be copied. Audiences and trust are difficult. So “who are your first 100 users and why are you finding them” should be figured out before you write the first line of code. This is part of building the product.
Key points: Four moat migrations: capital → product & sales → PLG → distribution. AI is the first to compress the cost of building the product itself, shrinking replication windows to days. History repeatedly shows category naming and mindshare capture beat better products. But excellent product remains the prerequisite, and distribution will inflate. Deep trust within small circles is the ultimate barrier.