Bain is reportedly using AI to build rough replicas of software products during M&A diligence. Sit with that for a second.
A buyer considering an acquisition can now try to recreate parts of the company before deciding what the company is worth. The build-versus-buy calculation has always been part of M&A. AI is giving buyers a much faster way to test that calculation. Technology that took years and significant capital to develop can increasingly be pressure-tested against what a capable team can reproduce today.
For founders, this puts a different kind of scrutiny on enterprise value. A strong product still matters. So does proprietary technology. But if parts of the product can be replicated faster than they could five years ago, the value of everything surrounding that product becomes easier to see: proprietary data, customer relationships, embedded workflows, distribution, institutional knowledge, and the company’s ability to learn from its own operations.
Those assets are difficult to reproduce because they accumulate through the life of the business.
Buildability Is Becoming Part of M&A Diligence
Acquirers have always looked at what it would cost to build a capability internally rather than buy it. AI gives them another way to test the answer.
The Financial Times reported that Bain has been creating rough AI-generated replicas of software products during diligence. Prospective buyers can use those prototypes to understand how difficult a target’s technology might be to reproduce before committing to an acquisition.
That gives buyers a more direct way to test a company’s technical moat. They can recreate parts of the experience, test assumptions, and see which elements are genuinely difficult to reproduce.
Sometimes the technology will prove much harder to reproduce than it initially appears. In other cases, the code may be accessible while the real value sits somewhere around it.
Years of proprietary customer data are hard to recreate. So is a product embedded in a critical workflow across hundreds of customers. The same goes for specialized regulatory knowledge, contractual protections or credibility in a market where relationships take years to develop.
AI gives a buyer the ability to prototype software quickly. Reproducing the operating history behind a company is a very different proposition.
The Strongest Companies Learn as They Operate
Every company generates information as it grows. Customer calls reveal recurring problems. Product behavior shows what people actually use. Support conversations expose friction. Campaigns reveal how the market responds. Sales teams hear objections long before they appear in a quarterly report. The value comes from how well a company learns from the information it already has.
A company creates institutional value when what it learns from one decision improves the next one. Market information enters the organization, teams identify patterns, and decisions are made and measured. The outcome becomes context for the next product launch, customer segment or investment.
Over time, the business develops knowledge that exists because it has been operating in the market. A competitor starting today doesn’t inherit any of it. This becomes especially important in founder-led companies.
Founders often carry an enormous amount of context in their heads. They remember why a customer left three years ago, why a product decision worked, which market assumptions proved wrong, and which relationships matter when something gets difficult.
There’s real value in that instinct. A business becomes more vulnerable when that knowledge remains dependent on the founder.
The stronger business turns that knowledge into shared context, documented decisions, and systems other people can use. Now the company retains what it learns instead of resetting every time a person, team, or strategy changes.
That kind of institutional knowledge compounds, and recreating everything a company learned along the way takes time.
Distribution Is Part of the Asset Base
I’d put audience and distribution in the same category. A founder can now build direct relationships with customers, employees, industry peers, and potential partners through newsletters, social platforms, video, events, and community. Done well, those relationships give a company access to the market that it doesn’t have to rebuild every time it launches something. Audience size matters less than what that audience can do for the business.
An owned audience can influence pipeline, provide product feedback, improve recruiting, lower the cost of reaching customers, or create access to strategic relationships. A trusted founder voice can also create continuity when the company changes direction, enters a new market, or goes through a transaction.
The value of that direct market access becomes clearer when a company launches something new. One business starts from zero and pays to find the right people. Another already has thousands of relevant customers, operators, and industry peers paying attention. The second company begins with context, trust, and a direct path to feedback.
That’s an operating asset, and it took time to build. It becomes even more durable when the relationship extends beyond one person. Company-owned subscriber lists, recurring editorial formats, documented audience insights, and a broader bench of credible voices make distribution more transferable. A smaller, highly relevant audience can be a meaningful business asset.
Buyers Are Looking for Evidence of AI Value
There’s an irony in all of this. AI is creating some of the pressure on defensibility while companies are simultaneously trying to use AI itself as evidence of differentiation.
We’ve spent the past few years watching companies add AI features, announce AI strategies, and put AI into road maps. Buyers are getting more selective about what those claims are actually worth.
PwC’s 2026 deal outlook argues that acquirers need to understand whether AI will accelerate or erode a target’s value over the next three to five years.
That pushes the conversation into the economics. Buyers can look at whether AI has improved margins, reduced cycle times, strengthened retention, accelerated product development, or helped the company allocate capital and make decisions more effectively. Those outcomes tell you considerably more than the number of AI features in a product. The same applies to the downside.
If a competitor gets access to similar models tomorrow, a company needs other sources of value that remain difficult to reproduce.
That could come from proprietary data, distribution, workflow integration, domain expertise, network effects, or customer relationships. The answer will be different for every company. But more founders are going to need one.
Durable Value Compounds
None of this matters only when someone is preparing to sell a company. The same things that make a business harder for an acquirer to reproduce tend to make it stronger to operate.
Proprietary knowledge gets better as the company encounters more situations. Customer relationships deepen through years of successful execution. Distribution becomes more efficient as trust builds. Institutional knowledge improves the next decision because the organization remembers what happened the last time. Those assets compound through use.
AI will keep making certain capabilities faster and less expensive to build. Buyers will get better at testing what they can reproduce themselves. Founders will have more tools to create sophisticated products with smaller teams and less capital.
All of those things can be true at once. Increasingly, enterprise value depends on what remains after someone tries to replicate what you built.
A product can be copied. A feature can be rebuilt. But the knowledge, relationships, market access, and operating history accumulated around them take time to recreate. Increasingly, that may be where the real value lives.
What Marketers Should Know Now
AI visibility is becoming a real brand metric. Vogue reports that major fashion brands are beginning to measure how they appear in ChatGPT, Gemini, and other AI answers, with earned media playing a significant role in the sources AI systems reference. Why it matters: PR, editorial authority and search visibility are increasingly becoming the same conversation. Read the Vogue report
Google is bringing traditional Search advertising into AI Mode. Google is testing exact- and phrase-match Search campaigns inside AI Mode when queries show clear intent. Why it matters: AI discovery is moving from an organic visibility question into a paid-media question too. Read the Search Engine Land report
ChatGPT is building a more mature advertising business. OpenAI has expanded ChatGPT Ads into additional markets while adding audience targeting, conversion measurement and reporting capabilities. Why it matters: marketers may soon be planning for ChatGPT as both an answer engine and a media channel. Read the Search Engine Land report
Microsoft is beginning to measure AI visibility directly. Microsoft is positioning Clarity to show brands which AI platforms cite them, the topics associated with their brand and what AI-referred visitors do afterward. Why it matters: GEO is moving from a conceptual discipline toward something marketing teams can actually measure. Read Microsoft’s guidance
SEO isn’t disappearing underneath AI search. Google’s latest guidance says its generative search experiences still rely on core Search systems, including retrieval, indexing, and ranking, and explicitly says strong SEO remains the foundation. Why it matters: marketers shouldn’t abandon SEO for a shiny new acronym. The job is expanding from ranking pages to becoming a credible source across search and answer experiences. Read Google’s generative AI search guidance
