Is AI Your Newest Form of Competitive Moat?

Private equity and venture capital firms have always known how to spot a moat. Network effects, switching costs, proprietary data, and defensible IP can be the kinds of markers that identify a company that’s worth investing in, rather than just operating well.

The marker dominating that conversation now is, of course, AI.

Not whether a company uses it since, at this point, that’s almost a given. But whether a company’s AI creates an advantage that competitors can’t replicate by buying the same subscription.

Surprisingly, most companies can’t answer that question clearly. And somewhere in that lack of clarity is where the next wave of valuation differentiation is being built.

Not Every Company Needs Proprietary AI, But Every Company Should Know If They Do

As a topic we’ve explored many times, adding AI features for their own sake is a trap. What it comes down to is whether AI is core to your competitive position, or not:

If it’s not, commodity tools (fast, cheap, and good enough) are the way to go.

If it is, then your AI implementation decision is a distinctly more consequential strategic choice. 

This blog is for that second group.

The Difference Between Using AI and Owning It

Using AI means subscribing to tools that any competitor can access immediately. Owning AI means building proprietary models trained on your data. The major advantage being that these models improve as your business grows and become harder to replicate the longer they run.

Using AI shows up on a P&L as a cost. Owning it shows up in the valuation as an asset.

TechCXO’s AIIM framework maps this spectrum into three tiers: from commodity tools to fine-tuned models to fully proprietary systems. Each tier represents a different level of investment, differentiation, and defensibility. Which tier a company occupies tells sophisticated investors a lot about where the competitive advantage is.

• Tier one tools deliver efficiency anyone can buy
• Tier two fine-tuned models start to reflect your data and domain
• Tier three proprietary models are built entirely on data only available to you, and they get ever more difficult to clone

Most companies default to tier one and stay there because it suits who they are and what they need. That’s fine. But some recognize that their data, domain, and business problems are unique enough to make it worth going further. Those are the moat builders.

What a Real AI Moat Looks Like

Proprietary AI moats share a few common characteristics: they’re built on data to which competitors don’t have access; they improve over time through feedback loops unique to the business, and; in the strongest cases, they generate patentable IP that becomes a new revenue stream in its own right.

This is what that looks like in practice. A few years ago, TechCXO worked with a media and entertainment company that had identified a growing challenge: isolating the most interesting moments in podcasts was a tedious, time-consuming manual process. With the volume and length of podcasts growing rapidly, relying on manual analysis just wasn’t a scalable option.

The company had a proprietary dataset and a clear business problem. We built a custom model that could analyze podcast content quickly, deployed it in the cloud, and scaled it to process thousands of podcasts a day. What had been a manual bottleneck became an automated capability that major media companies, even those with far greater resources, couldn’t easily replicate. The work resulted in multiple patents and gave our client a competitive position that generic AI tools could never deliver.

That’s tier three in action. Not just a better tool, but a defensible business asset.

What Does This Mean for PE Firms and Their Diligence Checklists?

The due diligence questions around AI are constantly evolving. As AI becomes a standard line item in technology assessment and generic adoption is no longer a unique signal, the questions have moved on from whether a portfolio company uses AI and are now:

• Is the company building proprietary AI capability, or just subscribing to commodity tools?
• If a competitor buys the same tools tomorrow, what’s left of their advantage?
• Is there a model development roadmap, or just a ChatGPT account?
• Does their data strategy support a proprietary AI advantage over time?
• Is AI embedded in the product, or bolted on as a feature?

Companies that can clearly and definitively answer these questions stand apart in the deal process. The others are going to leave valuation on the table.

The podcast example above illustrates why these questions matter. A firm evaluating that media company would have found a business with patented IP, a scalable cloud-deployed model, and a capability that major competitors couldn’t match. Compare that to a company doing the same thing with a consumer AI tool: a different asset and a very different valuation picture.

What Portfolio Companies Should Be Building Toward

You don’t have to start at the proprietary model tier, but you should understand if that is your ultimate destination. Which means making decisions now that will support getting there.

The most important of those decisions is around data. The proprietary models of the future run on the data you’re accumulating now. Treating that data as a strategic asset and not just an operational “byproduct” will build the foundation of an AI moat whether you realize it or not.

You have to be very deliberate about what data you collect, how you store it, and whether your current AI implementation is helping you accumulate the kind of proprietary training data that will matter later. A company using a generic AI tool today that doesn’t capture any of its own interaction data is establishing a different trajectory than one that’s logging, labeling, and preparing that data for future model development.

That distance only grows as time goes on. Just like the valuation difference between them.

The Moat Question

Investors reward defensibility, and AI is just the latest way to build it. Or not.

The media company that started with a podcast problem didn’t set out to build a moat. They set out to solve a real business problem with data they already had. The moat was the result of doing that methodically, and doing it at the right tier.

Ultimately, the issue isn’t whether AI matters to valuation, but whether yours does: is it just another line item, or is it a competitive moat in the making?