The product is becoming the admission ticket

A strategic analysis of competitive advantage in the AI era

One of the foundational assumptions of the software industry has always been that great products create durable competitive advantages.

For decades, investors, founders and executives operated under the same logic: if you could build something meaningfully better than the alternatives, and if competitors required years of engineering effort to catch up, you could create extraordinary enterprise value.

Entire software categories were built on this premise. Salesforce established leadership in CRM, ServiceNow in IT workflows, Workday in ERP, and Atlassian in developer collaboration - largely because their products were difficult to replicate and continuously improved faster than competitors could respond. The economics of software rewarded product excellence because software itself was scarce. Building enterprise-grade applications required significant capital, large engineering teams, years of development and deep domain expertise. The moat was not merely the code. It was the accumulated knowledge, integrations, customer feedback loops, implementation experience and organizational capability required to produce the code. Product leadership therefore translated into market leadership.

The emergence of AI is changing this equation more profoundly than most software executives currently appreciate.

The popular narrative focuses on how AI will transform products. A more important question may be how AI transforms competition itself. The critical shift is not that software is becoming less valuable - it is that software is becoming easier to create.

Throughout technology history, whenever the cost of producing something falls dramatically, the source of competitive advantage migrates elsewhere. Cloud computing reduced the cost of infrastructure and shifted value toward applications. Open-source software reduced the cost of foundational technologies and shifted value toward user experience, distribution and ecosystems. AI appears to be reducing the cost of software creation itself, forcing another migration of value.

If AI reduces the time required to reproduce functionality from years to months - or even weeks - the traditional product moat begins to weaken. That does not mean products stop mattering. It means product superiority may no longer be sufficient. And that raises a fundamental question: If the product is no longer the moat, where does competitive advantage live next?

The Golden Age of Product Moats

For most of the SaaS era, software creation was constrained by multiple forms of scarcity. Engineering talent was limited. Infrastructure costs were significant. Enterprise integrations were difficult. Distribution channels were expensive. Product development cycles often stretched across several years before meaningful market adoption occurred. This created natural barriers between leaders and challengers.

Consider Salesforce: building a CRM was never particularly difficult - building Salesforce was. The platform benefited from decades of accumulated customer feedback, ecosystem investments, implementation knowledge, partner relationships, integrations and workflow optimization. Competitors could understand what these companies were doing, but replicating it was another matter entirely.

The result was a powerful asymmetry: leaders improved faster than challengers could catch up. The best product often became the dominant product. The dominant product attracted more customers. More customers generated more data, feedback and resources. The flywheel reinforced itself.

This is the world most software executives grew up in - and the one many continue to optimize for today. The challenge is that AI is compressing many of the forces that made those advantages durable.

The Great Compression

Every major technological wave has reduced the cost of something fundamental. The internet lowered the cost of communication; cloud computing transformed infrastructure economics; open-source software reduced the cost of foundational development. AI appears poised to do something more consequential still: reduce the cost of creating software itself.

The numbers are beginning to reflect this shift:

• McKinsey's 2024 State of AI report found that organizations using AI-assisted development report a 20–45% increase in developer productivity.

• GitHub's own data shows that developers using Copilot complete tasks up to 55% faster.

• Andreessen Horowitz has documented cases where AI-native startups ship features at 5–10x the velocity of traditional teams.

These are not marginal gains - they are structural changes to the economics of software production. Not long ago, building a credible enterprise software company required substantial capital, large engineering organizations and years of development effort. Today, small teams equipped with AI-assisted development tools can prototype, build and iterate at a pace that would have seemed implausible only a few years ago.

The consequence is not that innovation disappears - if anything, innovation may accelerate. What changes is the relationship between innovation and defensibility. Features that once required years to reproduce can now be replicated in quarters, and in some cases weeks. The result is what might be described as a great compression of competitive advantage: innovation continues, but the half-life of differentiation steadily declines.

The defining strategic question is no longer whether competitors can eventually build what you have built. In most cases, they can. The question is how long it will take them to do so. And for a growing number of software categories, the answer is becoming uncomfortably short

The Product Is Becoming The Admission Ticket

None of this should be interpreted as an argument that products no longer matter. Customers will continue to prefer better software, poorly designed products will continue to struggle, and product innovation will remain a prerequisite for success in virtually every category. The mistake is not believing that products matter - the mistake is assuming that what is necessary for success is also sufficient to create durable differentiation.

As AI reduces the cost of building software and accelerates the speed at which functionality can be replicated, product excellence increasingly resembles the price of admission rather than a lasting competitive advantage. Having a great product remains essential. Possessing a great product may no longer guarantee meaningful separation once competitors can reproduce similar capabilities in a fraction of the time previously required.

The product is not disappearing as a source of value. It is evolving from the moat that protects a business into the admission ticket that allows it to participate in the market.

Whenever the cost of creating something falls dramatically, value tends to migrate toward whatever remains scarce and difficult to reproduce. As software creation becomes increasingly democratized through AI, defensibility is likely to move beyond the product itself and toward the broader system surrounding it.

The Rise of System Advantages

One useful way to think about this transition is to distinguish between product advantages and system advantages. Product advantages emerge from functionality. System advantages emerge from position.

A system advantage exists when a company becomes so deeply embedded within a customer's operating model that replacing the software becomes difficult — not because the product is unique, but because the surrounding ecosystem has become indispensable.

Several forms of system advantage appear particularly important.

Data Gravity

The value of data does not primarily reside in generic datasets or publicly available information. It resides in contextual proprietary data: the accumulated record of customer interactions, support conversations, invoices, renewal discussions, product usage patterns and operational workflows that collectively reflect how an organization actually functions.

This distinction becomes critical as foundation models themselves become more capable and more widely available. If every company has access to similar underlying intelligence, competitive advantage shifts toward the quality of the context surrounding that intelligence. In an environment where intelligence becomes increasingly commoditized, context becomes the scarce resource.

Workflow Ownership

Organizations do not purchase software for the sake of owning software. They purchase it because critical business processes need to function reliably, repeatedly and at scale. As workflows, teams, reporting structures and decision-making processes become intertwined with a system, replacing that system becomes increasingly difficult — not because a superior alternative does not exist, but because the organizational cost of change continues to grow.

AI may dramatically reduce the effort required to recreate software functionality, but it does little to reduce the effort required to change organizational behavior. Features can be copied; habits, processes and institutional workflows are far more resistant to disruption.

Trust

Trust may be one of the most underrated strategic assets in modern software. As AI lowers barriers to entry and accelerates product creation, enterprise buyers face not a shortage of options but an abundance of them. Evaluating which solutions will remain reliable, secure and strategically relevant over time has become its own challenge.

Unlike features, trust cannot be shipped in a product release. It is accumulated gradually through consistent execution, customer success, reliability and reputation — and precisely because it is earned rather than built, it remains one of the few competitive advantages that becomes more valuable as software itself becomes easier to create.

Distribution

The history of technology repeatedly demonstrates that superior products do not always create superior businesses. Microsoft understood this long before the cloud era. Salesforce built one of the most formidable enterprise distribution machines of the SaaS generation. HubSpot transformed content and inbound marketing into a scalable customer acquisition engine.

AI may lower the barriers to building products, but it does not automatically lower the barriers to building trust, brand recognition or go-to-market excellence. In a world where more companies can build compelling software, the winners may increasingly be determined by who can distribute it most effectively.

Which Software Categories Remain Protected?

It would be a mistake, however, to assume that every software category is equally exposed to these dynamics. While AI is undoubtedly accelerating the commoditization of certain forms of functionality, some categories benefit from structural advantages that are far more resistant to technological disruption. The common characteristic shared by these categories is that their value extends beyond the software itself. Their defensibility is rooted not only in what they do, but in the position they occupy within the organizations they serve.

Systems of Record provide perhaps the clearest example. Categories such as ERP, CRM and financial management software benefit from powerful forms of data gravity that strengthen over time. Every transaction, customer interaction, operational workflow and business decision enriches the system, making it increasingly valuable as a repository of institutional knowledge. As a result, replacing these platforms is rarely a simple technology decision. It often requires retraining teams, redesigning processes, migrating years of historical data and accepting significant operational risk. These systems remain vulnerable to disruption - as every incumbent eventually discovers - but their competitive advantages tend to erode far more slowly because the cost of replacement extends well beyond functionality.

A similar dynamic exists within financial infrastructure. Payments platforms, banking systems, treasury management solutions and accounting software operate within highly regulated environments where compliance, risk management and trust are often as important as product innovation. AI may accelerate development cycles and enable new forms of automation, but it cannot eliminate regulatory requirements, reduce fiduciary responsibilities or remove the need for institutional trust. In these categories, the barriers to entry are often legal, operational and reputational rather than technological.

Healthcare software benefits from many of the same characteristics. The complexity of healthcare workflows, combined with strict regulatory oversight, liability concerns and deeply embedded operational processes, creates significant resistance to disruption. While AI may make healthcare applications easier to build, replacing the systems that coordinate patient care, medical records and clinical operations remains an extraordinarily difficult undertaking. In many cases, the software itself represents only a small portion of the value proposition; the real moat resides in trust, compliance and workflow integration.

Cybersecurity presents a slightly different case. Unlike many software markets where AI primarily benefits challengers, cybersecurity is an environment where both attackers and defenders gain access to increasingly powerful tools. The result is a continuous arms race in which software functionality alone rarely provides a sustainable advantage. What matters increasingly is access to threat intelligence, visibility across large networks, deep security expertise and the trust required to protect mission-critical assets. In this context, the moat often extends far beyond the application itself and into the broader intelligence ecosystem surrounding it.

Developer platforms also demonstrate a form of resilience that is frequently underestimated. Products such as GitHub, GitLab, Stripe or Twilio derive much of their value not simply from the capabilities they provide, but from the ecosystems that have formed around them. Developers invest heavily in integrations, workflows, communities, documentation, tooling and accumulated expertise. Over time, these ecosystems become increasingly valuable because they reduce friction and accelerate adoption. GitHub, for example, is not merely a code hosting platform; it is a foundational layer of the modern software development ecosystem. Its defensibility comes as much from its position within developer workflows as from its underlying functionality.

Perhaps the most protected category of all may be vertical software. While AI is exceptionally good at generalizing across common patterns, it is considerably less effective at instantly acquiring decades of industry-specific expertise. Vertical software vendors often encode highly specialized knowledge about regulations, workflows, terminology and operational requirements that exist within a particular sector. Whether serving healthcare providers, logistics companies, insurers, manufacturers or legal professionals, these platforms benefit from a depth of domain understanding that cannot easily be replicated through access to better models alone. The more unique the workflow and the more specialized the expertise required to support it, the more durable the competitive advantage becomes.

Taken together, these examples highlight an important nuance in the AI discussion. The categories most likely to remain resilient are not necessarily those with the most sophisticated products. They are often the categories that sit closest to critical workflows, accumulate the richest proprietary context, operate within complex regulatory environments or benefit from deeply embedded ecosystems. In other words, they are protected not because their software is difficult to copy, but because the systems surrounding their software are difficult to replace.

The Outcome Stack

These shifts point toward a broader transformation in how value is created and captured in software. Throughout the SaaS era, the underlying logic was straightforward: companies built products, customers used those products, and outcomes emerged as a consequence.

Increasingly, that sequence is evolving. The emerging value chain looks less like:

Product → Usage → Outcome

And more like:

Data → Context → Intelligence → Workflow → Outcome

The product remains important, but it is no longer the center of gravity. Value increasingly accumulates around the ability to transform proprietary data into actionable intelligence, embed that intelligence within operational workflows, and reliably generate measurable business results.

Consider Customer Success as an example. Few executives wake up hoping to buy a Customer Success platform. What they actually want is lower churn, higher net revenue retention, more predictable renewals and scalable customer growth. Historically, software vendors delivered tools that enabled teams to pursue those outcomes. Increasingly, customers will expect platforms to actively contribute to achieving them.

Viewed through this lens, the most important product being delivered is no longer the application itself. The product is the outcome.

The New Strategic Question

For decades, software companies asked a relatively straightforward question: how do we build a better product?

The next decade may require a different question entirely: how do we become indispensable to the customer's operating model?

Those are not the same thing. One optimizes software; the other optimizes outcomes. One creates features; the other creates dependency. One sells tools; the other sells success.

AI does not eliminate competitive advantage — it simply changes where competitive advantage lives. The winners will still build great products, but they will also own workflows, aggregate context, accumulate proprietary data, build trust, create ecosystems and increasingly assume responsibility for outcomes.

The product remains essential. It simply stops being the destination. It becomes the vehicle.

The product is not disappearing. But the moat is moving. And the companies that recognize where it is moving first may define the next era of software.

Bonus - A Practical Framework For Evaluating AI-Era Moats

If the product is no longer the primary source of defensibility, executives need a different way to evaluate competitive advantage. Instead of asking "How much better is our product?", leadership teams may increasingly need to ask "How difficult would it be for someone else to replace us?"

One practical way to think about this is through six questions.

No company needs to score perfectly across every dimension. But the stronger a business performs across these questions, the more likely it is that its competitive advantage survives even as software itself becomes increasingly commoditized.

Conversely, companies whose differentiation rests primarily on product functionality may find that their competitive position erodes faster than expected as AI continues to compress development cycles.