Benefits of Prioritizing Domain Expertise Over Coding

Everyone says prompting will be worthless in 2 years. While they wait for AI to "get smart enough" to not need prompts, companies encoding domain expertise are building $100M moats that compound daily. Here's why prompting + human expertise is the most defensible IP you're not building: 1. "AI will understand context eventually" — Sure, and your competitors will eat your lunch while you wait GPT-6 won't magically know why your enterprise deals die at stage 3. Or which contract clauses trigger 90-day payment delays. Or why your ICP ghosts after the CFO meeting. Your experts know. Their prompts encode it. That's unreplicable. 2. The data doesn't lie: Domain expertise beats technical perfection Stanford HAI's 2023 study proved it: Domain expert prompts outperform engineer-written ones by 29% on accuracy, 42% on relevance. McKinsey's 2023 report confirmed: "expertise in the problem domain is often a better predictor of AI effectiveness than technical depth alone." 3. "Prompting is just syntax" — Tell that to the 65% failure rate Cognilytica's 2023 survey found 65% of enterprise AI failures stem from domain misalignment, not bad models. A 10/10 prompt engineer writes beautiful JSON that misses the point. A 7/10 prompter with 10/10 domain expertise. hits the mark every time. 4. Your prompt library is your company's collective brain, weaponized One prompt: Your revenue strategist's "tactics, messaging archetypes, and objection-handling insights no AI engineer could infer." Another: Your legal ops leader's "risk tolerance logic and redline priorities." Not instructions. Encoded wisdom. 5. They copy features. They can't copy decades of experience Prompts aren't commands—they're "encoded representations of mental models, decision criteria, heuristics, and playbooks reflecting years, if not decades, of lived experience." Your competitor can steal your UI, they can steal a prompt and reverse engineer it, but they can't steal how your best people think and build more. 6. "Everyone will have AI" — Exactly. Human Excellence becomes MORE valuable, not less. When AI makes "OK" results universal, domain-specific mastery becomes the only differentiator. "The differentiation will shift from prompt form to prompt intent, and intent will always be a human product." Generic competence is commoditized. Expertise is amplified. 7. The Ironman principle proves it: Human + AI > Either alone "It is not Jarvis (AI) nor Tony Stark (human) alone that yields outsized results. It is the integration. AI without the right human insight is blind; the human without AI is slow. Together, they produce superhuman output." Right now, someone at your competitor is dismissing prompting as "not unique." Right now, smart companies are encoding decades of expertise into strategic IP. In 18 months, one will be desperately playing catch-up. The moat isn't the model. It's the human. And minds, encoded at scale, are unstoppable.

In a world where the cost of execution (coding, design, etc) is quickly dropping towards zero - what is only going to increase in value is a founder's strong expertise in a particular niche. There is a record number of startups in the world today. For literally any idea, you’ll find tens of contenders pitching mostly the same narratives. You have hundreds of AI SDRs and AI ad managers, and thousands of AI design and marketing tools targeting the same very broad audiences. So how do you break through? What really matters - and what’s only going to help you stand out even more in today’s world - is your obsession with a particular niche or a very narrow customer segment. This is what AI can’t do right now (or at least, not yet). It’s your depth of understanding that becomes the moat. When you’ve spent years in a specific industry or solving a specific problem, you start to see the invisible things: the language your customers actually use, the friction points no one talks about, the workflows that look simple on the surface but are filled with edge cases. AI tools can replicate execution, but they can’t replicate insight. They don’t have intuition built from hundreds of conversations, failed experiments, or deep pattern recognition from years in the trenches. That’s why domain obsession isn’t just a “nice to have” anymore — it’s your core strategic advantage. In a sea of generic AI-powered startups, the founders who truly know their people - and build relentlessly for them - are the ones who will win. And ironically, the more AI commoditizes execution - the more valuable your very human, very hard-earned expertise becomes 🙌

I just wrapped a community session for my friends at the AI Exchange, sharing how I use voice to power AI automations - it was a blast! This group never fails to amaze me, and I always love connecting with folks in our group. During the session, I shared a behind-the-scenes look at a LinkedIn automation I built. I demonstrated how I talk to a GPT I created in ChatGPT, it sends the transcript to Zapier, and through a series of steps, it crafts a LinkedIn post from that transcript and emails me 3 drafts (one written by GPT-4o, one written by Claude, and one written collaboratively by both of them). To demo the automation live, I asked if anyone had an idea for a LinkedIn post. Kevin Hardin, one of the community members, jumped in and spoke directly to my GPT to share his thoughts. Below is what we got back from the collaborative run (with only 90 seconds of edits - mostly so I could make it fit with this intro also) - shared with Kevin's permission: ----- AI isn't replacing us; it's enhancing what we can do. But to truly take advantage of its power, you need to know your field. Think about it:  • Domain expertise + AI = Faster, better results in your field  • No expertise + AI = Potential disaster I call this the "AI Illusion" — when you think AI has given you a shortcut in an area you're unfamiliar with, but you lack the expertise to judge the quality of the output. For instance, if I used AI to draft a patent or grant, I might think, "This looks great!" But if I showed it to a patent expert or grant writer, they'd probably spot major flaws I missed because I don't have the expertise to evaluate it properly. The takeaway? AI is extremely powerful, but it's not a substitute for real knowledge. You need enough domain expertise to recognize quality output and use AI effectively. Many people worry AI will replace them, but the more we use it, the clearer it becomes: AI amplifies existing skills. If you have domain expertise, AI helps you do what you already know—just faster and better. The problem arises when you use AI in areas where you lack expertise. This is where the "AI Illusion" kicks in. You might think the output looks good, but without the right knowledge, you can't tell good from bad. The bottom line is that AI works best as a tool to enhance what you already know, not as a replacement for expertise. You need a solid foundation in your field to evaluate and use AI-generated results effectively. What do you think? Have you experienced the "AI Illusion" in your work? I'd love to hear your thoughts!

"Domain experts are more important than machine learning engineers. If you have to pick one, hire a domain expert." --paraphrased take from an interview I just heard If this is true, it's hard advice for an MLE to hear-- let alone action on. So you want to build a fraud detection system for a bank? The place to start isn't "what GenAI / neural architecture should we use?" It's "how do we define fraud? what information should we collect? what is the risk and impact of fraud? What labels should we use?" These are questions that many engineers don't naturally care about. Many engineers just want to jump in and *build*. Whether it's AI, or open source, or vendors- building is getting easier. I see people calling this "left shift". Ultimately, maybe we'll end up in a place where non-technical people can build anything, so the only people who now matter are domain experts. Honestly, I don't think that will ever happen, but I think it's already *trending that direction. So I genuinely believe that domain expertise is one of the most non-replacable, scarce, valuable skills. It's hard to learn. It ties you to an industry, e.g. cancer research, trading, advertising, supply chain and logistics, etc. But I also think it makes the difference between those who are and aren't capable of leading a department in that niche. Generally, my definition of senior is you "(1) build a business case, then (2) make and execute a plan to address it". Imo it's hard, maybe even impossible to do (1) without subject matter expertise. For my team right now, that means: if you want to get to senior, it's important for you to understand not just *how* our pipelines are built, but *why*. What business problem are they addressing? Where are they consumed and who is consuming it? Do those people think the outputs are useful? How much money does it cost and make us? What future opportunities might exist to improve on our solution to these underlying problems? These tend to be the questions that upper management care about-- much more than the tooling used in the solution. For MLEs and MLOps engineers: start building domain expertise by answering those questions.

The Startup Org of the Future—More Thinkers, Fewer Coders Until recently, the startup narrative was almost formulaic: a visionary founder, a small product team, and an army of coders building everything from scratch. But that script is being flipped—fast. 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: 𝐎𝐥𝐝 𝐯𝐬. 𝐍𝐞𝐰 Old Way: You have five developers churning out code to integrate payments, user logins, and analytics. Each feature might take weeks or months, plus QA and iteration. New Reality: A product manager with a strong grasp of a foundational AI model—plus a no-code/low-code workflow tool like n8n—can spin up an end-to-end prototype in days. 𝐓𝐡𝐞 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 ‘𝐓𝐡𝐢𝐧𝐤𝐞𝐫-𝐃𝐨𝐞𝐫’ 𝐇𝐲𝐛𝐫𝐢𝐝 Shift in Skill Set: Instead of requiring advanced coding, future startups may prioritize domain expertise and problem-solving. The question becomes: “Can you design the right solution using AI blocks?” Data Point: No-code platforms are predicted to account for over 65% of app development activity by 2024 (Gartner). This indicates a seismic move away from pure coding to system orchestration. 𝐖𝐡𝐲 𝐅𝐞𝐰𝐞𝐫 𝐂𝐨𝐝𝐞𝐫𝐬? 1. AI Building Blocks: Large language models (LLMs) can generate boilerplate code in seconds. Repetitive tasks—like building CRUD apps or setting up common integrations—are now point-and-click. 2. Accelerated Prototyping: Tools like ChatGPT or GitHub Copilot can flesh out MVPs at breakneck speed. Meanwhile, workflow automation (n8n, Zapier, Make) eliminates repetitive tasks. 3. Resource Efficiency: Instead of a 10-person dev team, a startup can achieve similar output with 3 AI-savvy generalists plus 2 domain specialists. That’s big cost savings in an era where venture capital looks for lean, efficient teams. However, the new world is about coders who can efficiently use AI tools. 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐅𝐨𝐮𝐧𝐝𝐞𝐫𝐬 Founders: Startups can go from idea to MVP in a fraction of the time, reducing burn and generating product-market feedback early. The startup org of the future is less about armies of heads-down coders and more about agile, cross-functional teams that orchestrate AI-driven components. Execution will still matter, but the nature of “execution” evolves—fewer lines of raw code, more creative configuration and strategy. And that’s a transformation we’re only beginning to see. #AI #Startup #Founders NAKAD Sambhav Jain Avinash Uttav Akash Kejriwal Bikash Ranjan Mishra Chinmaya Gawde Raman S. Arun Yadav