In the traditional SaaS world, the GTM approach followed a tried and tested path:

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1. Start with an unconventional idea and a big vision
   A strong product hypothesis was at the core. You began by defining a problem worth solving, paired with a vision for how your product would reshape the landscape.
2. Identify a small but critical customer segment
   You’d narrow down to a minimum viable segment (MVS)—a group of users who had a strong, shared need and would immediately see value from your product.
3. Iterate until your MVP finds first product-market fit
   The goal was to build a minimal product that addressed a small number of well-defined pain points. You’d keep improving until your segment couldn’t stop talking about you.
4. Launch publicly
   Once you felt confident in your product and had a fanbase that believed in your vision, you’d launch to the world.
5. Expand your segment gradually
   With a small but passionate user base in place, you’d grow by expanding the segment, using their advocacy to propel you to the next stage and higher level PMFs.

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But with AI, the rules have shifted.

Building in public isn't a new concept in traditional SaaS, but it was always optional. OpenAI, however, recognised that with the uncertainty of AI outputs, they could move faster by learning alongside the world. For AI companies, to launch early is a necessity, because they rely on massive user engagement to train and refine their models. Without substantial input from a large user base, this process would take longer and be far less effective. MidJourney, for instance, leveraged this approach, incorporating multiple feedback loops and offering free credits to users who graded outputs. 

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This approach also means that companies can’t to perfect a single magical use case or find a very specific product-market fit. They demonstrate what their AI does well, but are also transparent about known flaws, and pay attention to how users interact with their tech and adapt to AI. This openness created curiosity, excitement, and an iterative process visible to the world, accelerating their model's improvement and adaptation.

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So, what does this mean for the GTM of AI companies?

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The traditional SaaS GTM model—nail a niche, build an MVP, iterate to perfection, and launch to the masses—doesn’t fully apply to AI. With AI, the rules shift, and so must the go-to-market strategy.

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The new GTM playbook for AI companies

1. Define and test positioning before building
   AI companies need to define their positioning early, grounded in a strong vision. Your market needs to understand not just the product’s immediate value but its future potential, especially since AI technology evolves quickly.
2. Think about moats from day 1
   AI startups can’t rely on product features alone as a moat—incumbents can easily integrate AI features. Focus on building moats through team, vision, and strategic partnerships. Establish relationships, exclusive integrations, and collaborations that incumbents can’t replicate. Protect yourself from competition up and down the value chain—whether it’s new entrants carving out niche solutions or incumbents developing generalized technology to solve your problem faster and cheaper.
3. Lead with education and transparency
   AI often involves complex decision-making and automation. The GTM strategy must educate the market early on about what the AI does, where it excels, and even where it falls short. Being transparent about these nuances builds trust and primes customers to adopt and integrate AI solutions into their workflows.
4. Constant iteration in public view
   Rather than aiming for product perfection before launch, AI companies should iterate publicly. Customers and early adopters are more forgiving with AI, expecting it to evolve and improve. By launching early and gathering real-world feedback, your product can grow organically with user input.
5. Transform processes, not just add features
   AI doesn't just layer over existing processes—it can fundamentally unbundle them from humans and then bundle into new, AI-led workflows. As your product grows, focus your GTM on showing how AI reshapes workflows, reducing human involvement in favor of more automated, machine-to-machine processes. Seamless integration with existing platforms is key. Position AI as a transformative solution, not just a set of features, to drive greater perceived value.

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We’re in a period where there is little firm ground to stand on—everything is in flux. AI is poised to transform industries, but the landscape is constantly evolving. To navigate this uncertainty, companies must think strategically about positioning, vision, product, talent, and ecosystem from the very beginning. This foresight aims to provide the maneuverability needed to adapt and stay in the game.