When the Moat Evaporates: AI, "Reverse Engineer Everything", and What CEOs Must Do Next

For decades, much of competitive strategy has rested on a quiet assumption: copying what someone else has built is slow, expensive and requires rare skills. That assumption is now breaking down, and faster than most boards realise.

This autumn, an open-source project called REA, short for "Reverse Engineer Anything", climbed to the top of GitHub's trending charts. 

It began in April as a modest script connecting an AI model to a Mac disassembler. It has since grown into a full agent toolkit that can analyse native binaries, Electron and .NET applications, Android apps and browser network traffic. It requires the AI to ground every conclusion in concrete evidence, such as memory addresses, code structures and captured network packets, rather than guesswork. In doing so, it turns a specialist's craft into an instruction that any developer can hand to an AI agent.

REA itself is not really the story. Commentators have noted that general-purpose coding agents can already approximate much of what it does, and will likely absorb these capabilities before long. The story is the trend it represents: Understanding, recreating and recombining someone else's product once demanded months of expert effort. It is fast becoming a commodity.

The Collapse of Gatekeeping

Most incumbents are protected by some blend of five barriers. 

The first is complexity, because proprietary formats, obfuscated code and undocumented logic have long made products hard to understand from the outside. The second is scarce expertise, since competitors needed hard-to-hire specialists to analyse or rebuild what you had made. The third is time: even when copying was possible, the twelve to twenty-four months it took gave market leaders room to stay ahead. The fourth is legacy lock-in, where customers stayed put because leaving meant untangling logic nobody fully understood. The fifth is simple build cost, as credible software requires large teams and serious capital.

AI is wearing down all five at once. 

The security firm TrustedSec evaluated five commercial endpoint security products and reported that, with AI assistance, reverse-engineering work that once took skilled specialists weeks was completed in days. Its conclusion applies well beyond cybersecurity: "Complexity used to be part of the protection. It is a smaller barrier now."

The crucial point is that AI does not need to reverse engineer everything perfectly. It only needs to reduce the time and skill required to find the useful parts. A competitor only needs to understand your pricing engine, your onboarding flow or your integration logic well enough to rebuild it in its own way.

The Effects Already Emerging

The most immediate consequence is that feature advantage now has a much shorter half-life. If a rival can see a standout feature, it can increasingly work out how that feature works, so a lead once measured in years may now last only weeks. Software companies are already watching value drift away from individual applications towards the general-purpose AI layer, as thin "wrapper" products are absorbed by successive model updates.

As we’ve discussed previously at Hyperscale, smaller competitors are also arriving faster. A team of three equipped with capable AI agents can now produce what once required thirty people. Incumbents should expect more challengers, appearing sooner and often targeting the most profitable slices of their offering rather than the whole.

Security assumptions are going stale too. Many organisations bought their security tools believing that attackers could not study how those tools work internally, and TrustedSec argues that this assumption "is what is failing". Defenders have access to the same AI, but they are slowed by process, from triage queues to testing and staged rollouts. Attackers can act on what they find immediately.

Legacy, meanwhile, has become a double-edged sword. The tools that let rivals examine your products also let your customers free themselves from abandoned or locked-in systems. In one showcase example, REA recovered a small algorithm from a twenty-year-old game as working C code that passed thousands of tests. Any business whose retention depends on customers being unable to leave should treat that dependence as a growing risk.

Finally, scrutiny is being democratised. Customers, regulators and journalists can now audit closed-source software for hidden data transmission or undisclosed behaviour. Products that quietly do things users never agreed to are far more likely to be exposed.

Some balance is warranted. These tools are not magic. Properly hardened and heavily obfuscated software still resists them, and serious work still demands expertise. Intellectual property law, contract terms and licence agreements remain fully in force. Under UK and EU law, decompilation is permitted only in narrow circumstances, and recreating a protected product can still carry serious legal liability. The barriers have not vanished, but they are considerably lower, and they are falling every quarter.

How CEOs Should Prepare and Respond

The starting point is an honest audit of your moats. Leadership teams should ask plainly which of their advantages depend on competitors being unable to understand what they have built, and separate the durable ones from the eroding ones. Many will discover that more of their defensibility rests on opacity than they had assumed.

The next priority is to move value towards what cannot be decompiled. Proprietary data and the feedback loops it creates cannot be extracted from a binary. Neither can trusted customer relationships, regulatory approvals, distribution, brand, network effects or an organisation that learns quickly. These deserve deliberate investment, because code can be copied while a decade of customer trust cannot.

It also follows that companies must compete on speed rather than secrecy. If features can be copied in weeks, the winning response is to ship the next one before the copy lands. That means redesigning product operating models around shorter cycles, AI-assisted development and continuous discovery. Release cadence is now an essential part of strategy.

CEOs would be wise to red-team themselves before anyone else does, by asking an internal team or trusted partner to turn these AI tools on their own products, security controls and customer-facing systems. The aim is to learn what a motivated competitor or attacker could discover in a week. The next step is to close the gap between finding a problem and protecting customers, since that gap is where the real risk lies.

In the same spirit, retention should rest on value rather than friction. It is safer to assume that customers will soon be able to migrate off your platform with relative ease. The answer is to make staying the obviously better choice through continuous improvement, outcomes-based relationships and open integrations. Legal and IP positions deserve a fresh look as well, with counsel reviewing licence terms, trade secret protections and contractual safeguards. Even so, legal protection works better as a deterrent than as a strategy.

Lastly, leaders should remember that the lower barriers apply to them too. Adjacent markets that once looked too costly to enter may now be within reach. Legacy systems in their own estate that seemed impossible to modernise may now be workable. Those who treat this shift purely as a threat will miss half the opportunity.

The Bottom Line

"Reverse Engineer Everything" sounds like a niche developer headline, but it is an early signal of a structural shift: understanding and recreating products is becoming cheap. When that happens, strategies built on complexity, secrecy and slow-moving competitors stop holding up. 

The CEOs who thrive will be the ones who rebuild their advantage on what AI cannot extract, and who learn to move faster than anyone can copy them.

Photo by Greg Willson on Unsplash