The specialization moat

Why solving hard problems has outsized advantages

Last week, we mapped the AI application landscape into a 2x2 defined by technical complexity and adoption friction. In this landscape, ease of adoption is a signal of low defensibility. If a product is easy to adopt, it is easy to displace. While the easy quadrants have captured the market’s attention and initial revenue, the most sustainable moats are being built in the hard-hard quadrant — the space where technical complexity and organizational friction act as a structural filter against competition.

Source: Gemini.

We are biased because we’re building in the hard-hard quadrant – we’re building an AI SRE at RunLLM. But we’ve placed a bet intentionally: We believe the hard-hard quadrant is one of the most interesting and exciting places to be in enterprise AI today because hard-hard applications create fascinating data moats that are difficult to attack. There are two themes to this data: Tenure data that comes from agents accumulating invaluable experience, and pattern data that comes from repeatedly solving hard problems.

In the easy quadrants, a single model can rule the market because the problems are largely standardized. But in the hard-hard quadrant, there is no standard problem. Every company has a unique combination of tech debt, custom workarounds, and undocumented quirks that live in a senior engineer’s head. As a result, there is no zero-shot solution for these complex problems. The key moat is the context you build to navigate each customer’s environment.

The Tenure Moat

Experience: When AI is your most tenured employee

The feedback an agent collects isn’t just used to improve its next response; it is a repository of institutional memory. In the “hard-hard” quadrant, an agent’s value scales with its experience within a specific company because the problems being tackled aren’t going to be solved in a single attempt. Accumulated experience will make an agent more effective over time.

This is quite similar to the way humans work. On day one at a new company, a software engineer has general skills which they’ve learned over time – but they don’t know all the specifics of the company they’ve joined. After a year, they are intimately familiar with the ins and outs of the stack: They know which components break under load and which legacy modules are built in a way that creates scalability issues. When a crisis hits, they diagnose it faster because they have the context. That context only comes from experience – from trying, failing, and learning.

Agents learn the same way, but by their nature, they are able to learn much faster than humans can. They can replay historical scenarios, compare their work to what experienced engineers actually did, and teach themselves how to improve. Just as critically, when new issues pop up, the experience an agent learns won’t be siloed in one person’s head – that experience will be immediately available for the whole team to benefit from.

In this quadrant, your agents accrue meaningful tenure. When an agent accumulates this level of context, it becomes functionally impossible to “fire.” Switching to a competitor doesn’t just mean a new UI – it means firing a senior employee with a year of context and replacing them with a junior one who has to start from scratch.

Economics: How experience makes you more valuable over time

There is a direct correlation between adoption difficulty and economic value. Something a junior employee can do in fifteen minutes is naturally easier to automate, but it can also be solved by many different agents – that means your product likely is not very sticky. These simple tasks are dramatically less valuable than a task that takes a senior engineer 8 hours to complete. The hard-hard quadrant focuses on the latter.

There are two factors that contribute to the complexity of these problems. First, it is the agent’s capability itself. Solving a support ticket will usually require a couple actions. Debugging a complex production outage requires correlation across observability tools, source code, and historical knowledge. Access is only half the battle – you then have to know how to analyze, reason over, and correlate that data with the expertise that a senior engineer would display. That doesn’t happen overnight – it likely requires you to integrate carefully with your customer’s environment and learn quickly. That depth of integration isn’t just an onboarding process – it shows your customer that you can leverage all the necessary information to grapple with the complexity of a hard-hard problem (more on this below).

Paradoxically, it also lowers the standard for success. If you’re building an agent that automates 8 hours of work per-task it solves, you will likely demonstrate a positive ROI even if you only have 50% accuracy. 8 hours of time for a $100 per-hour ($200k annual salary) employee is a savings of $800. A 50% accuracy rate means an expected gain of $400 per issue tackled.

Solving hard-hard problems means that you need to put in the work to get incrementally better – if you do that well, you’ll be off to the races.

The Pattern Moat

Patterns: Learning across companies

Beyond individual company context, there is immense value in learning patterns across an entire industry. Hard-hard agents are the equivalent of experienced hires – they’re bringing their accumulated experience across many roles and many years to your organization. They might not yet know all the details of your company, but an experienced employee will be able to contribute quickly.

Consider building an AI SRE like RunLLM. While every company’s infrastructure has custom details that cannot be shared, the patterns of failure are often universal. For example, when an RDS instance runs out of memory, the signs will look the same no matter what application it’s serving. Similarly, security attack vectors are going to be the same across all companies. An agent that has seen this happen across fifty different environments will recognize it faster than a human who has only seen it twice.

The pitfall here is that enterprises are (rightly) conservative about their data. Growth in this space depends on finding ways to learn from fully anonymized, non-implicating metadata. The ability to bridge “individual company context” with “global failure patterns” is a technical moat in itself. How exactly that looks in practice still remains to be seen – but it’s something we’re actively investing in.

Process: Implementation is the product

There is a significant danger in this quadrant: over-promising and under-delivering. When you claim to solve a customer’s hardest engineering problems, you cannot simply hand over an off-the-shelf agent and hope for the best. Your core agent won’t work out of the box – you’ll need to go through an implementation and customization process for each customer.

Hard-hard problems require a hands-on, highly technical implementation process.

An anecdote we heard from a head of customer success recently stands out: their team moved from a self-serve model to meeting with customers weekly for six months to establish clear business goals before giving them free rein of the product. Without that high-touch guidance, churn was inevitable. Interestingly, this was for an easy to solve, hard to adopt product – so the standard is going to be much higher for a hard-hard agent.

You might be scared of this implementation overhead because you’re worried it doesn’t scale. In reality, this friction is to your advantage. First, it creates product stickiness – the cost of replacing a product plus the value of accumulated experience is high. Second, your team and your product will become increasingly well-suited to deploying in complex production environments.

Anecdata: Overcoming the skepticism barrier

Because these problems are legitimately hard, the default state of a prospect is skepticism. Most of the prospects we talk to today don’t believe these problems can be solved by AI yet. Even in cases where we are confident in a solution, customers often fear we are over-promising.

The only real solution here is data and time. Pointing to 100 successful customers is a more compelling argument than any technical white paper will be. The more time you spend in the quadrant, the more your reputation becomes a barrier to entry for new competitors. Social proof is a critical part of the market maturation process.

Drawing from a larger set of examples also helps because different enterprises are going to have different levels of expectation. Pointing to what you’ve learned from other customers – and how you’ve configured agents to match specific scenarios – will be critical to building trust with the Nth customer.

The Long Game

Every quadrant in AI is competitive, but the hard-hard space offers the unique opportunity to establish technical depth that is difficult to displace. It’s not going to be easy to establish this depth – it’s going to take thoughtful product design, disciplined process, and creative technical workarounds for enterprise data concerns.

Once you are entrenched with any team, your opportunity to expand to adjacencies is dramatically higher. A support bot is often siloed, but an engineering agent is sitting at the center of the organization’s most critical workflows. In the last generation of enterprise technology, the public cloud providers built compute primitives to anchor themselves in the stack, then layered higher-level services on top.. Once customers were in the ecosystem, it became natural to use more cloud provider tools. With agents, the gravity lies in data access and context. Once an agent understands everything needed to solve your biggest problem, it’s a short hop to solve the next ten.

If you are starting a company today, don’t be afraid of the problems that take more than a weekend to prototype in Cursor. Some of the most valuable businesses in the world were built on problems that looked impossible on day one. With the current pace of AI research and the power of coding agents, you can solve these problems faster than ever before. But if you hit on a hard-hard problem and invest in it, you aren’t just building an app — you’re building one of the most sustainable and innovative businesses in the market.