Wandering Through the Noise

AI is compelling us to rethink software moats—especially those built on network effects and switching costs. Traditionally, network effects meant that value grows as more users join, creating a self-reinforcing barrier to entry. Achieve critical mass, and newcomers face an almost insurmountable uphill battle.

But today’s AI tools erode that advantage. In user-based networks, the “cold-start” barrier no longer demands years of organic growth. With compute, platforms can be seeded immediately: synthetic content, human-like bots, and simulated interactions provide the illusion of a bustling community from day one. The upshot is straightforward: what once took years can now be faked in a matter of hours, forcing incumbents to rethink how they attract and retain genuine engagement.

Liquidity-based networks—platforms whose value lies in abundant supply—aren’t immune either. Consider Airbnb or eBay: their value proposition rests on immediate availability, sparing customers the slog of searching for inventory. Today, AI agents can trawl the entire web for the best listings at virtually zero marginal cost. True, it might take an AI twenty minutes to secure a booking—an acceptable wait for a vacation home reserved months in advance, but insufficient for on-demand needs like a rideshare. Still, for a growing number of non-urgent transactions, AI effectively substitutes for a thick network of listings. In essence, the advantage shifts from amassing physical supply to securing the best algorithms.

The concept above does require listings to be “online” and I belive this is a unique opportunity. Someone should create a data pool for every individual and company to list their needs, services, and assets in one trusted personal repository. Agents can then assess that consolidated data offline, identifying mismatches between supply and demand, and executing transactions when the time, price, and party all align most favorably. By aggregating everything in one place, friction is reduced, effort minimized, and probability of value the greatest.

Integration-centric moats—companies that bundle connections across banks, insurance carriers, or other services—face a similar risk. Historically, writing each connector was a bespoke affair, a labor-intensive process that deterred rivals. Yet large language models now excel at generating data connectors: integration work that once demanded specialized teams can now be automated in hours. As integration costs plummet, the gating role of API aggregators diminishes. In short, integration becomes table stakes rather than a fortress.Switching costs, the other pillar of software moats, are also under siege. In the past, migrating data from Vendor A to Vendor B demanded months of careful planning and execution—an enterprise-grade project that deterred even the most determined customers. Today, AI-driven migration scripts and adapters reduce that friction to a few automated steps. What once was a dealbreaker for customers now risks becoming a minor inconvenience. Going forward, vendors must offer more than just sticky data: they need genuinely differentiated features, superior reliability, and increased pace of improvement.

Nothing underscores this shift more clearly than the emergence of new startups promising to generate fully functional enterprise systems (think CRMs or ERPs) with an hour or so of one business user’s intervention. I witnessed a demo at a top university lab last week that produced a HubSpot‐style CRM in fifteen minutes flat. That’s not merely spinning up a prototype; it’s recreating an entire company’s workflows and data model within a single, endlessly extensible platform. Watching software that once took teams of engineers months—or years—to craft spring to life in minutes is akin to seeing a master carpenter build a house with a single flick of the wrist. If we are months away from rendering complex migrations in minutes, are we not overstating the durability of today’s incumbents? In short, if this doesn’t force a re-underwriting of switching costs, it’s hard to imagine what will.