Imagine the scene: a solo founder, perhaps fueled by caffeine, dictates ideas to an advanced AI. Code materializes like magic, coalescing into a polished product.
The user experience is seamless. Payments system, flawless. Support bots, perfectly empathetic. Customers appear as if summoned, flocking to sign up, use the service, and sing its praises. A unicorn born overnight, spun from prompts and processing power.
If only it were that easy.
Artificial intelligence is lowering the barrier to entry for new tech ventures, enabling smaller teams to achieve scale and giving rise to the ”Three-Person Unicorn” concept (shoutout to NFX). But the “instant AI-generated company” narrative misses crucial parts of the picture.
Founders who launched their startups before the generative AI boom may feel themselves falling behind as these AI-native competitors emerge. Are our handcrafted codebases and established processes suddenly obsolete?
Not necessarily. But standing still is not an option.
The good news? You likely have real advantages that newcomers don’t — an existing product, traction and product market fit, established sales and marketing engines, operational processes, unique market learnings. The challenge now is integrating AI’s power to augment — not replace — these strengths.
Beyond Artisanal Code: Pivoting Your Tech Team
AI won’t replace engineering, but it will reshape it.
As a founder, you need to encourage your technology teams to pivot from “artisanal” code creation to workflows that fully leverage AI. This is not about sacrificing quality or craftsmanship but unlocking a new paradigm. With the right AI tools, we can finally break the old “evil triangle” of speed, price, and quality. No more choosing two — we can now achieve all three.
Even if AI could produce perfect code instantly, that alone doesn’t build a company. At the heart of any business are human decisions: identifying real problems, crafting thoughtful solutions, and defining success in a way that aligns with the vision.
Think about the software development life cycle. As Eliyahu Goldratt highlighted in “The Goal,” optimizing one part of a system does not automatically optimize the whole. Your development process will then be gated by the speed of your next-slowest process.
Real work remains:
- Understanding the “why”: Is the feature genuinely valuable to the customer? Does it solve a real problem? AI can help synthesize feedback or research, but product vision and customer empathy remain key.
- Validation and testing: Does the generated code actually work as intended? Is it robust, secure, and performant in real-world pressure?
- Integration and deployment: Does this new piece play well with the existing system? How is it deployed reliably and scalably?
- Monitoring and iteration: How is its performance tracked? How do you measure success and gather data for the next iteration?
- The human element: Collaboration, architectural decisions, strategic trade-offs, and user experience nuances still require human judgment.
Efficiency can’t replace human judgment. As long as this remains true, companies will still be built by skilled people making smart decisions together.
The Reductio ad Absurdum AI Exercise
Here’s a practical exercise for your team.
Imagine that every line of software code is now free and instantaneous. Ask your team:
- What tasks still need doing?
- Where are the bottlenecks now?
- What processes are essential to ensure you’re building the right thing, deploying it effectively, and ensuring it performs and scales in its intended environment (web, mobile, hardware, etc.)?
Focus on those remaining tasks and how AI can streamline them.
- Can AI generate better test cases?
- Can AI analyze monitoring data more effectively to spot anomalies?
- Can AI help automate deployment pipelines?
- Can AI synthesize user feedback to prioritize features?
- Can AI assist in drafting documentation or initial UX flows?
Implement AI-driven best practices to improve these areas. This is not just about keeping up; it’s about giving your team superpowers.
Shopify’s Playbook: AI as a Baseline
Look at how established tech companies like Shopify are adapting. In a recent internal memo, CEO Tobi Lütke was explicit: “Reflexive AI usage is now a baseline expectation at Shopify.“
Key takeaways from their approach include:
- AI as a multiplier: It’s a tool that not only assists but multiplies capability, enabling teams to tackle previously implausible tasks.
- Learning by doing: Using AI effectively is a muscle that strengthens with use. Flexing it is not optional.
- Beyond code: AI is a “thought partner, deep researcher, critic, tutor, or pair programmer.” Its applications are broad.
- Prototype with AI: Faster iteration → faster learning.
- Justify not using AI: Before requesting more resources, prove that AI can’t do it first.
- Applies to everyone: This expectation permeates the entire organization, from engineers to executives.
Shopify’s stance highlights a crucial reality: Adapting to AI is not just a strategy for new entrants or a nice-to-have tool. It’s a table-stakes requirement for staying relevant.
Stagnation, as Lütke puts it, “is slow-motion failure.”
Your Edge: Don’t Just Defend, Augment
Established startups have the unique opportunity to blend their hard-won market knowledge, customer relationships, and operational infrastructure with the accelerating power of AI.
- Acknowledge the change: Recognize that the ground has shifted. Yes, AI-native companies have advantages in speed and cost. Don’t ignore it.
- One ETW founder announced their AI shift by having the team tear up their job descriptions. That’s the mindset shift this moment demands.
- Inventory your strengths: What advantages do you have? Existing customers, data, brand, operational processes, experienced team …?
- Identify AI opportunities (beyond code): Use the reductio ad absurdum exercise. Where can AI optimize what already works? Validation, deployment, monitoring, support, sales, marketing …?
- Empower your team: Provide access to tools (like GitHub Copilot, Claude, internal chatbots) and time to experiment and learn. Foster a culture of sharing AI wins and learnings, just as Shopify encourages.
Integrate strategically: Do not blindly chase AI hype. Solve real problems and enhance existing strengths.
Instant Code ≠ Instant Company
Building a successful business requires far more than just generating lines of code. It requires vision, validation, execution, and adaptation across all functions.
By strategically integrating AI not just into coding but into the entire fabric of your operations, established startups can leverage their existing advantages to compete effectively and build a sustainable future in this new, AI-augmented era.
The race is on, but you are not at the starting line. You can stay ahead — if you choose to move.