Building an AI-powered company

Market

The DL-powered AI adoption started with tasks with a narrower scope and higher tolerance for mistakes, like recommenders (showing “people also buy” widgets, or choosing the next post in the social network feed), dictation, OCR, and basic image recognition. Though such tasks used to be performed by hard-coded algorithms, the DL approach demonstrated much better results.

Use cases

When thinking about a specific product to focus on, it’s helpful to consider what use cases can benefit from

  • Search and recommendations: text, video, audio, products;
  • pattern recognition: human pose estimation, visual objects recognition, anomaly detection, content moderation, noise reduction, churn risk estimation, text <-> speech, question-answer matching;
  • structuring: summarization, entity extraction, intent;
  • translation: hi -> fr, en -> computer code;
  • assessment of unstructured content: fraud risk, grammatical correctness;
  • narrow repetitive tasks: taking notes during a meeting, filling out CRM, proof-reading, patrolling, painting, sanding;
  • content generation/rapid prototyping: images, sounds, music, text, or even car parts’ shapes in CAD systems;
  • content modification: removing an object from the photo, removing “uhms” or replacing a word in the podcast recording;
  • highly complex simulations: earth atmosphere, protein folding;
  • operating with large amounts of data that are impossible for traditional software to handle.

What makes a great market

Best markets are characterized by the combination of “high value” and either “rapid adoption” or “formidable barrier”.

  • High value — your AI system is enabling a massive improvement that matters to the customer. If AI can increase my LinkedIn headshot’s resolution by 10x — I don’t care. If it can half my electricity bill — I’m listening.
  • Rapid adoption — the faster you can get the product to the customers the better. Markets that win here have no regulation, don’t require physical world operations, or complex buying decisions. Social media is an example of such a market and TikTok [14] is one of the biggest successes of AI-powered startups to date.
  • Formidable barrier — if the market doesn’t allow for rapid adoption, there should be significant barriers to entry to keep competitors away from entering while your company is capturing it. Palantir is a good example of building a data-centric company in a slow market. The lower the structural barriers in your market the more you should think about how to accelerate adoption (see “Distribution” section below).
  • Look for situations where mediocre AI-powered products are growing suspiciously well. It’s a sure sign of the “market pull”
  • Look for a physical reaction from your customer, when you show them the prototype. “When can I have it, how can I get it sooner?” are the questions you’re looking for.

Product

Hit the market as fast as possible. Although this idea is as old as entrepreneurship itself and isn’t specific to AI businesses, it can be particularly hard for AI founders to follow it. Building powerful AI systems is hard, and sharing something not particularly well functioning with the customer seems like a waste of time. The key here is understanding the goal of “hitting the market”. It isn’t to sell your product or to demonstrate that it reliably works in production. The goals are — to validate the need, the key set of capabilities, the data availability, and the purchasing process. Look at your readiness through this set of goals. Often you can achieve all four goals with just mockups.

Distribution

For a startup to succeed it must solve distribution faster than the incumbent solves innovation. E.g. Microsoft came up with Teams to stop Slack from dominating the market. Had Slack grown faster MS might not have had time to respond.

Business model

The primary factor differentiating an AI-powered product business model is the compute-hungry nature of the DL systems which lends itself best to the tiered consumption model, freemium or trial-based with an option for wholesale enterprise deals with the largest customers (see e.g. OpenAI pricing structure). However, there are a few nuances.

Moats

One thing to capture a piece of the market, another is to hold it against an onslaught of challengers. This section is structured along the lines of the 7 Powers framework, focusing on dynamics introduced by the AI (“7 Powers” by H.Hemler is one of the essential readings on the general company building [1]).

Team

As in any rapidly evolving and fast-changing fundamental technology field the AI company will disproportionally benefit from the best-in-class engineering talent. Think about Apple and Wozniak. Zuckerberg coded himself and brought the best people on board, eventually overcoming buggy and slow Friendster. Paypal and Square winning against multiple competitors not in small part due to their ability to efficiently and automatically fight fraud. The list goes on. In the case of an AI company, it means making sure you brought in great teams working with the AI system (Data Science/ML-engineering, MLOps, etc) and general engineering (frontend, backend, infra, DevOps, etc.). You can’t build a production-level AI system without either of the groups and their intense collaboration. At the very early stages, it makes sense to aim for people who can work across the stack. These folks are rare but are well worth the effort of finding them.

Pitfalls

Technical side

There are several options for implementing AI capabilities in your product depending on the importance of each function and its availability elsewhere:

  • Core AI capabilities. These will likely have to be built in-house. It is a formidable task, even using all the wealth of open source, but it gives you maximum flexibility. E.g. this is how One AI’s team approaches building core NLP models, not only implementing SOTA but pushing its boundaries.
  • Not core, but isn’t readily available from 3rd parties. Build this in-house by applying AI shortcuts and advanced training techniques: embeddings, pre-trained foundation models, active learning [9] E.g. Meta uses this approach for most AI needs, excluding the core feed recommender algorithm, that it designs and trains independently.
  • Not core, and can be bought as a product or API — consider buying this capability at least at the beginning to speed up the development.

Conclusion

We’ll be updating this document based on your feedback. Please let us know what you think about it and what important lessons we missed here.

Notes

[1] These are some very good sources of general advice. The list just scratches the surface of course, but here we go:

  • Zero to one by Peter Thiel
  • Books from the “Business” sections here
  • Paul Graham’s blog
  • YC startup school
  • Content produced by NFX, Sequoia, A16z, and other VC firms.
  • If you don’t know where to get a good source of advice on some specific topic — ping me in the comments to this post on Twitter

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Artem Burachenok

Artem Burachenok

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Builder & venture/angel investor in tech | AI, Energy, Startups, Investing | Posts include investments | https://twitter.com/Burachenok