In late 2025, a company called Dinamicka did something telling. One engineer, working with Claude Max, spent three weeks building a time-tracking tool to replace Tempo, a product they had been paying $500 a month for. The result was better than the original: it fit their Jira workflow, auto-filled timesheets, and the team didn’t have to learn a new interface.
The story seems unremarkable on its face: AI helped someone build a small tool. But imagine you’re the founder of Tempo for a moment. Your customer discovered they don’t need your product anymore. They can build a better version themselves, in three weeks.
Andrej Karpathy spent a weekend building a multi-model debate system entirely with AI. Patrick Bishop, head of growth at Loomery, had never written code before. He built and deployed a complete application over three nights.
People who couldn’t do this before can do it now. AppDirect’s 2025 report documented a shift happening in real time: enterprise “build” is replacing “buy.” Internal teams are developing applications five times faster than traditional IT. Gartner predicts that by 2028, 90% of enterprise software engineers will use AI coding assistants—a number that was below 14% in early 2024.
M Accelerator analyzed 50 B2B SaaS companies in the project management space. Two years ago, feature overlap between competitors was around 40%. Now it exceeds 85%. One founder spent two years and $1.2 million developing a recommendation engine, only to see a competitor replicate it in a single weekend using ChatGPT’s API and 400 lines of code. Anthropic had 16 AI agents collaboratively write a complete C compiler: 100,000 lines of Rust, passing 99% of GCC’s torture tests. Total cost: $20,000. Time: under two weeks.
If it were just a supply-side problem—more people building competing products—that would be about competition intensity. The real trouble is that demand is contracting in three distinct ways at the same time.
The first is demand substitution. Dinamicka’s story is not an isolated case. A new consensus is forming in the r/buildinpublic community: “Building is easy now. Getting attention isn’t.” A team that used to buy Jira for project management now has an engineer spin up a custom tool with AI, perfectly tailored to their workflow. That demand didn’t vanish—it moved from the external market to internal builds. For SaaS companies, this is deadlier than demand decline, because it’s irreversible. Once a user discovers they can build something better themselves, they won’t come back to buy.
The second is demand contraction. AI lets one person cover more work. Workday laid off 8.5% of its workforce for exactly this reason. One person with AI handles what used to require three, so companies stop buying three seats. IDC predicts that pure per-seat pricing will be obsolete by 2028. The total headcount of demand is shrinking.
The third is demand fragmentation. Teams used to share a single set of tools. Now everyone is building their own micro-tools with AI. The granularity of demand is getting finer, but the value of each unit of demand is shrinking. You used to sell one Jira license to an entire team. Now each person generates their own board with AI, and nobody pays for it.
Meanwhile, supply keeps flooding in. Feature overlap rose from 40% to 85% in two years. AI also lowers switching costs: migrating from one product to another no longer requires an engineering team spending three months on it. Even when you land a user, keeping them is harder than before. Indie Hackers forum data shows 75% of new tools see a sharp traffic decline within the first month of launch. One founder lost $47,000 before their product ever shipped—not on development, but on marketing and distribution that went nowhere.
Demand is contracting on multiple fronts while supply floods in. That’s the competitive landscape. But a deeper shift is happening at the same time: trust is being redistributed, and the direction of that redistribution traps two kinds of companies on opposite sides of the same problem.
First, let’s follow where trust is flowing.
Stack Overflow’s 2025 global developer survey points in one clear direction: people don’t trust AI-generated code. 46% of developers actively distrust the accuracy of AI-generated code, while only 33% trust it. Just 3% report “highly trusting” AI output. The more experienced the developer, the deeper the skepticism—among senior developers, the highly-trusting share drops to 2.6%.
In a world where code is increasingly untrustworthy, the words “battle-tested” gain value. Red Hat’s code has been free for decades. What IBM paid $34 billion for in 2019 was two decades of enterprise support records, a certification training ecosystem, and the weight of the phrase “Red Hat Certified.” In February 2026, HSBC maintained “buy” ratings on Oracle, Microsoft, ServiceNow, Salesforce, and Palantir. Their logic: enterprise software takes years to prove 99.999% uptime. LLMs trained on public data don’t understand the large-scale private architectures these companies spent decades optimizing. In the contest of trust, time is on the incumbent’s side.
But that’s exactly why they can’t move.
A company that has built trust on “we’ve run without incident for twenty years” faces a dilemma: introducing AI is itself a risk. AI might generate vulnerable code, offer unauditable recommendations, or break compliance boundaries. McKinsey’s data: 78% of enterprises have deployed AI, but only 19% of executives report more than 5% revenue growth from it. Anthropic’s Economic Index found that 77% of enterprise AI deployments are focused on automation tasks, not collaboration and judgment. Most companies only dare to put AI in places that don’t touch their core trust—scheduling meetings, generating weekly summaries, nothing that touches payments, audits, or patient data.
So both sides are stuck.
Incumbents have trust, but can’t put AI into their core processes. Their trust asset is too expensive to risk for a 10% efficiency gain. They’re falling behind on efficiency, but trust is too valuable to chase.
New entrants can go all-in on AI—product design, delivery, the entire pipeline driven by AI, maximum efficiency. But efficiency doesn’t matter when users don’t trust something that was just built. Their speed advantage is canceled out by a trust deficit.
This is a stalemate. Neither side is comfortable, and neither can move. But a stalemate itself is a signal: whoever crosses first claims the next phase of advantage. For incumbents, crossing means finding a way to introduce AI that doesn’t damage trust—auditable decision chains, AI that augments rather than replaces human judgment, building a safety record on non-core processes first. For newcomers, crossing means finding a way to build trust that doesn’t depend on time—fully transparent architectures, incentive alignment (“if I mess up, I lose more than you do”), third-party certification and compliance.
This stalemate isn’t new. Every time the cost of one link in the chain collapses, the bottleneck shifts to an adjacent link, and old and new players each get stuck on one end.
The open-source movement of the 1990s made code free to copy and distribute. Incumbent software companies had trust but charged for code. New open-source projects had code but no trust. Red Hat crossed the gap: it sold not code, but the reliability of something “Red Hat Certified.” IBM paid $34 billion for exactly that.
AWS launched in 2006, turning infrastructure into a credit-card expense. Heroku made deployment beautifully elegant, but after Salesforce acquired it in 2010, it went a decade without major updates. Once the underlying layer commoditized, the abstraction layer’s value withered. The companies that crossed weren’t the ones with the most elegant abstractions. They were the ones that became infrastructure.
The App Store launched in 2008, dropping publishing costs to $99 a year. A decade later, the top 20 publishers—representing less than 0.005% of all apps—took 60% of total App Store revenue. Discovery was broken. The apps that crossed weren’t the ones with the best features. They were the ones that didn’t need to be discovered because they had network effects: WhatsApp, Instagram, Uber.
In March 2026, Morningstar re-audited the moat ratings of 132 companies. They downgraded 22 wide-moat and 18 narrow-moat companies, and upgraded only two: Cloudflare and CrowdStrike. The downgraded companies largely relied on “users are too lazy to leave”—data migration is a headache, switching workflows is costly. But AI is erasing that friction. The two upgraded companies rely on “users lose something if they leave”—the bigger the network, the stronger the capability. Leaving means giving up a network that keeps getting more valuable.
The same pattern repeats: every bottleneck shift traps old and new players on opposite sides. The players who cross aren’t the fastest builders. They’re the ones who find the new bottleneck.
PlatformEngineering.com summarized the dynamic in 2025: “When every small team can build their own tools, the value of coordination infrastructure doesn’t drop. It rises. The more fragmentation, the more coordination is worth.”
Dinamicka replacing Tempo has already happened. For the next Tempo, the question isn’t “how do I make my features harder to replicate.” That direction holds no answers. The question is: which side are you stuck on? Are you the one with trust who’s afraid to move, or the one who can run at full speed but nobody believes you? Then take a step toward the other side.
These questions are harder than writing code. But in an era where writing code gets cheaper by the day, the harder part is exactly where the bottleneck just moved.