While I was a Product Manager at Google AI, I launched several AI-first features such as the first internal version of LaMDA platform, Smart Replies in Google Play, and the Gaze detection platform for UX Research use cases.

All of these technologies were very nascent and had more kinks than anyone could possibly imagine.

The system and technology misfired A LOT during the early stages and the trick was always to find the tiles that were safe and not step on the ones with landmines to determine the Go-to-Market for the product feature. We’d consider which features to work on now vs later, which set of users to target based on the value they’d get from the product, and more.

I left Google earlier this year to explore and build my AI-first company, and got curious about what the GTM for AI-first startups looks like. So, I did some preliminary research on three AI-first startups. Here’s a brief overview of their GTM:

1. Midjourney (image generation, started in 2021)

Midjourney is an independent research lab that’s self-funded. Midjourney did something very clever for their GTM. Instead of creating a web app (like Dalle), they launched a Discord server and gave early adopters access to the technology through that.

David Holz shared the following on why they launched the offering through Discord:

“We found very quickly that most people don’t know what they want. You say: “Here’s a machine you can imagine anything with — what do you want?”

And they go: “dog.”

And you go “really?”

And they go “pink dog.”

So you give them a picture of a dog, and they go “okay,” and then go do something else.

Whereas if you put them in a group, they’ll go “dog,” and someone else will go “space dog,” and someone else will go “Aztec space dog,” and then all of a sudden, people understand the possibilities, and you’re creating this augmented imagination — an environment where people can learn and play with this new capacity.”

Dalle played a big role in Midjourney’s initial success. While Dalle was raking headlines, it wasn’t available to all. Midjourney, with its comparatively sub-par offering, gave access to early adopters including me who were itching to try out the tech for themselves.

Given that the community had moderators on Discord (another advantage of the platform), they could control the stuff being generated and didn’t worry as much about sensitive content being generated as much as OpenAI did, and were able to open up the offering quickly.

Key Takeaways from Midjourney Go-to-Market:

  • There are creative ways for GTM beyond just launching a web or mobile app.
  • A community of early adopters can be powerful to help assess the possibilities of a significantly new offering.
  • Communities enable extremely fast feedback loops and Midjourney repeatedly polled people for features, likes, dislikes, events, and more.

2. Jasper AI (text generation, started in early 2021)

Jasper is amongst the top startups that popped up after the release of GPT-3.

The first version was called conversion.ai (which now redirects you to jasper.ai). Jasper’s founder and CEO ran a marketing agency for several years and then created marketing courses with $10K MRR. So they were well-versed in the space and what was needed.

Their ideal customer profile was pretty clear and because of their past agency business, they likely had relatively easy access to the marketing teams and other agencies. They also got testimonials from the initial users and that likely helped build credibility and word of mouth.

Learnings for Go-to-Market from Jasper:

  • Having a strong background in the area helps with problem-area understanding and domain knowledge as well as GTM with the initial set of customers (cliche but very true).
  • Get early users to write testimonials; build credibility and word-of-mouth publicity through that.

3. Synthesia (spokesperson video generation, 2016)

Synthesia started as a deep-tech project as well, where the founders tried to customize lip movement on an existing clip with the help of AI-based synthesis.

The initial version of the website boasted the technology and potential uses but didn’t target any specific segment. They did have a long-term vision because in 2017, lip movement synthesis was an extremely challenging problem, and to build a deep-tech company that would likely take one to three years was a solid forward-looking vision from the founders.

While they realized that movies, news, YouTube videos, etc would be eventual use cases, my opinion is they didn’t know what the ideal use cases should be at the start.

They found product-market fit in 2021 when they started focusing on corporate training videos and presentations, and then the offering blew up. They’ve since been focusing on competing with text-based presentations by offering companies a way to generate video presentations and training just from text.

Key Takeaways from Synthesia Go-to-Market

  • Finding an ideal use case and customer profile can be challenging for a tech-first startup.
  • Synthesia was a solution in search of a problem, which is an idea that YC scoffs at. So, it is possible to build startups in that bucket and have a successful Go-to-Market. On the flip side, this approach does take longer.

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