The Tarpit is Getting Bigger: Rethinking AI’s Role in Startup Innovation
LLM coding tools are undeniably impressive. But here’s the thing: they’re not really solving the hard problems—they’re making the easy problems trivial.
LLM coding tools are undeniably impressive—I have subscriptions to Cursor, Windsurf, and Augment, and I use Cline and Roo-Code regularly. But here’s the thing: they’re not really solving the hard problems, they’re making the easy problems trivial.
More critically, however, they’re creating a seductive distraction for founders, convincing them that AI is the magic bullet for any challenge, and that they are somehow suited to solve the problems that have tantalized the “idea guy” types for years.
For investors and VCs, this trend deepens a long-standing challenge: distinguishing genuine innovation from well-polished demos built on hype.
The Illusion of Simplicity
Dalton Caldwell and Michael Seibel famously coined the term “tarpit ideas.” These ideas seem so straightforward and attractive that you wonder, “Why hasn’t anyone done this before?” Yet, when you look closer, you find they’ve been attempted repeatedly without lasting success. With LLMs now churning out code snippets and handling well-defined tasks with ease, many founders believe that the ideas they once deemed impossible or too technically complex are suddenly within reach—simply because AI is available.
This leads to a dangerous new variant of the tarpit: ideas that don't just look easy, but appear to be practically autonomous via AI implementation. What was once recognizably impossible (or so complex as to not be feasible to solve) becomes seemingly trivial with a few API calls and some prompt engineering. This lulls founders into the belief that the LLM will either obfuscate away the complexity or that the LLM has effectively reduced the complexity “to practice” (to borrow some language from in intellectual property space).
There’s another dimension to this illusion, however: All of the other truly difficult parts of building a software business—user adoption, market fit, scalability—remain just as challenging as ever. And now, if building a working prototype with a narrow (and shallow) use case is trivial, the pool of investors who may be fooled into writing a check gets much larger.
The Fuzzy Boundary
What was once a fairly well-defined challenge has now become murkier. The one-two punch of LLMs being adept at small coding tasks and well-scoped functions has convinced many wannabe founders that they can build something incredible with minimal effort. The result? A flood of AI-powered demos that look impressive on the surface but often mask deep-rooted complexities playing out in public.
The most obvious, tangible example of how this is causing the edges of the pre-LLM tarpit to crumble and expand is in "AI-powered" analytics dashboards. Why tackle the complex challenge of building truly insightful data analysis tools that deliver actionable business intelligence when you can quickly implement an LLM that generates impressive-looking charts and explanatory text based on minimal data? These flashy solutions give the appearance of sophisticated analysis while often providing minimal genuine value beyond what traditional methods already offered.
The LLM analytics dashboard syndrome illustrates a troubling trend: products that create an impressive illusion of value without addressing fundamental business needs. This example demonstrates how AI's capabilities can actually mask the absence of rigorous problem-solving rather than enhance it. When evaluating startups in this new landscape, investors must look beyond the veneer of technical sophistication to determine if the application genuinely solves a problem worth solving.
Keep Asking “Why?”
For investors and VCs, the evaluation of AI-powered startups is entering a new phase. It’s not enough to be dazzled by an AI demo. They need to dig deeper and repeatedly ask: Why exactly is AI necessary for this functionality? What problem is it solving, and how was this need validated? Why can’t this problem be solved without the use of AI? How would you solve this problem if you couldn’t use an LLM?
As AI tools make it easier to generate functional prototypes, the risk of being misled by superficial demonstrations increases. True differentiation will come from those founders who can prove that their technical challenges genuinely require AI—and that their solution is underpinned by solid user research and rigorous problem validation.
Adapting Evaluation Strategies
This is probably going to get worse before it gets better, and investors must adapt their due diligence process as rapidly as the technology is evolving. Larger Internal teams with specialized skill sets are going to become the new norm, something only the largest VCs and PEs have consistently utilized. User researchers are vital for rigorously validating whether AI features genuinely address real user needs, effectively separating buzz from substance. Application architects with deep AI expertise can assess whether technical challenges presented in demos are truly robust or merely implementations of off-the-shelf LLM solutions. Additionally, LLMs themselves can serve as effective evaluators—they excel at critiquing content and concepts they would struggle to generate independently, making them valuable tools for due diligence.
I know some VCs are already doing some or all of these things, and they are creating a decisive competitive advantage that is going to make what they say “no” to today look like pure genius over the next decade.
And for Founders
Here's what you need to understand: the AI gold rush is creating both opportunities and pitfalls that require careful navigation. When developing AI-powered solutions, focus on problems that truly matter. Ask yourself whether you're solving a genuinely difficult problem or simply using AI to make an already straightforward process marginally better. The most valuable innovations address challenges that users genuinely struggle with—not just what's technically impressive.
Before pitching your AI-powered solution, become your own harshest critic. Consider whether your core value proposition can stand without the AI component. Would your solution be compelling if described without mentioning AI at all? This self-critique process will strengthen your offering and prepare you for investor scrutiny.
The founders who will succeed in this environment aren't those with the flashiest AI demos, but those who demonstrate deep understanding of user needs. Invest in rigorous user research before and during development—this evidence of problem validation will increasingly differentiate you in investors' eyes. As VCs become more sophisticated in evaluating AI startups, prepare for deeper technical due diligence. Be ready to articulate exactly why AI is essential to your solution and how you're approaching the technical challenges in novel ways.
YC and other major investors are narrowing their focus primarily to AI-driven innovations, and the temptation to force AI into your solution is stronger than ever. However, the most successful founders will be those who resist this pressure and instead use AI judiciously—only where it genuinely creates transformative value that couldn't be achieved through traditional methods.
Remember, as the tarpit expands, disciplined problem validation becomes your lifeline. Ground your innovations in genuine user needs rather than technological capabilities, and you'll build something that transcends the current hype cycle.
What Happens Next?
The deepening of the tarpit presents a significant challenge, but also an opportunity for strategic differentiation. In the coming years, I anticipate a necessary correction in how AI-powered startups are evaluated and funded. Those who build on solid product fundamentals—genuinely addressing user problems with thoughtful implementation of AI—will ultimately emerge victorious, while the wave of superficial AI applications will gradually recede.
For founders genuinely passionate about solving real problems, this evolution means doubling down on what truly matters: rigorous user research, technical due diligence, and a crystal-clear value proposition that exists independent of the AI hype cycle. The winning formula isn't about how effectively you use AI, but rather how effectively you solve human problems—with or without it.
For investors and VCs, the emerging challenge is clear: discerning genuine innovation amidst the AI-amplified marketplace requires more than technical evaluation. It demands an understanding of user problems, market dynamics, and the fundamental question that separates lasting innovations from passing trends: does this solution meaningfully address a problem that users are
The tarpit is getting bigger.
Are you an investor or VC facing these issues? I want to hear from you! Things are changing rapidly. There is much work to be done.