Seeing Through the Hype: A Four-Step Guide ToVetting Artificial Intelligence Firms.

Every day, multiple companies reach out to me on LinkedIn, making claims about their groundbreaking AI. In some sense, this is good — it shows the field is maturing into the mainstream, but it also creates a ton of noise, which results in executive confusion, lost time, and wasted budgets. All things I try to avoid as much as I can. Cutting to the point, the majority of these companies that claim to have an “AI” that does a task such as anti-money laundering, churn optimization, or anomaly detection products, etc., are generally no more than technical consulting services that use your data to train models using open-source algorithms/packages that are available to everyone e.g., H2o, TensorFlow, BERT, SK Learn, XGBoost et al. To sift through the hype, I’ve developed some mental hacks to quickly vet firms to increase my chances of hitting on my technology “draft picks,” while avoiding AI start-ups with marginal upside. I should also note that this guide is for companies that claim their core attribute is their “AI.” Not firms who leverage AI, but do not claim it as a fundamental competitive advantage.

In short, here they are. For more context continue reading.

1. Funding — Building novel AI takes more money than most think (tens if not hundreds of millions).
2. Technical Talent/People involved — there are a few people with the knowledge and skill to create something unique. Most of the legit people are connected by <2 degrees to the top VCs, founders, or thinkers in the space. It’s a small world. Red flags if there are zero or few connections.
3. Location — 95% of the time if you aren’t in one of a handful of cities you won’t have access to 1 and 2.
4. Documentation & Open Source Code — Firms that are genuinely building, especially small ones, will want to get their code in the hands of data scientists/developers. It shows confidence.

1.) Investors + Money: Look the company in question up using Crunch Base or a similar database to see who’s investing in them. Are investors such as Lux Capital, A16z, or DCVC involved? If you have a budget and want to dig deep use my long-time go-to augmented intelligence platform, Quid (graphs below), which uses Crunchbase data, network analysis, and natural language processing to surface how firms, capital, product domains, and investors connect to one another (more on using network science and alternative data to anchor strategy in another blog post).

Network graph of companies focused on GraphQl, Graph Databases, and Edge Computing.

Does the data show VCs investing in the firm have technical depth and a history of hitting on game-changing technologies and products? Or are they a government or corporate venture arm run by people with a sparse track record of being associated with technologies before they were mainstream. Could they be there for for “innovation PR” or “tech tourism” VS driving legitimately towards the bleeding edge? To my second point - how much funding is the firm in question getting? Many top A.I. start-ups are in “stealth mode” during seed funding and only work with selected partners. And legit AI firms will have raised $30–$60m at series B(!).

The amount of funding each sub-sector in machine learning has received over $100m. (Larger node equals more investment.). Note that it’s still early days for the semiconductor industry, and thus upside exists while investment in healthcare is highly saturated.

In London, I often see firms that have raised between $2–$5-$10m at Series B trying to pitch their AI to me. While I may love the passion, and those amounts of funding would be great for many business ventures, in machine learning that amount doesn’t provide enough runway to build anything novel. Key point? Machine intelligence is an arms race, and funding enables companies to hire the best and brightest that actually have the potential to build a product that improves the current in-state tech. The former is as limited as the number of people that have the raw athletic potential to run a sub-4.4-second 40-yard dash (not very many).

2.) Talent + Network: Do they have the brain trust/talent/investors that could outthink current companies like Element AI, Facebook, Google, Primer, H2o, Tencent, Baidu, or Microsoft? If they haven’t raised requisite money, which is a result of being connected to the right funds, they won’t be able to afford the opportunity. Does the founder have a past track record of innovation in start-ups or a corporate (potential clients & future funding to reach the critical mass needed)? Machine intelligence is a small world. Anyone who can legitimately build good products is generally connected to the top firms, people, corporates, and investors in some manner. If they lack these connections, be skeptical.

3.) Location: If it’s a pure A.I. company, they should have offices in San Francisco, Toronto (Research), Shanghai, or Beijing. If not, it’s unlikely the firm in review will be able to concentrate enough high-end technical talent and funding at scale to innovate on top of the current corpus of AI. While there are exceptions Hyper Science/X.AI — NYC, DeepMind — London ( acquired by Google five years ago, and was the last meaningful AI firm Europe produced), and perhaps some Israeli firms (typically more focused on specific tasks than general AI), they are few and far between. To anchor this point Graphcore — one of the UK’s high-profile ML Chip start-up just opened up shop in San Francisco (because there is no money in the UK). And most other UK companies I know in the space are plotting the same.

Cities where machine learning companies are receiving the most investment.

4.) Documentation + Open Source Code: Does their website have elegant documentation on their supposed AI? Are there technical white papers and or an open-sourced version of their algorithms or package? If not I can say with almost certainty they are using the same packages that everyone does, and there really isn’t anything novel about what they are doing. In short, you’re paying them to train machines for you, with little upside from a competitive sense. Basically, your firm would be missing out on the opportunity to build the ML muscles it needs to stay relevant in the future. In my case specifically, if I don’t know the AI company through my network, and you don’t have great documentation, 95% of the time it’s a non-starter. Alternatively, if you reach out with awesome documentation — or even better an API or open-sourced version of the kit ready to go, you’ll grab my attention almost every time.