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Can your startup support a research-based workflow? – TechCrunch

João Graça is the CTO and co-founder of Unbabel, an AI-powered language operations platform enabling agents to communicate in any language. The President’s Council of Advisors on Science and Technology predicts that U.S. companies will spend $100 billion on AI R&D annually by 2025. Much of this spending today is done by six tech companies — Microsoft, Google, Amazon, IBM, Facebook, and Apple, according to a recent study from CSET at Georgetown University. But what if you’re a startup whose product relies on AI? Can early-stage companies support a research-based workflow? At a startup or scaleup, the focus is often more on concrete product development than research. For obvious reasons, companies want to make things that matter to their customers, investors, and stakeholders. Ideally, there’s a way to do both. Before investing in staffing an AI research lab, consider this advice to determine whether you’re ready to start.

Compile the right research team.

Assuming it’s your organization’s priority to do innovative AI research, the first step is to hire one or two researchers. Some researchers will build from scratch, and others will take your data and try to find a pre-existing model that fits your needs. At Unbabel, we did this early by hiring Ph.D.s and getting started quickly with research for a product that hadn’t been developed yet.

You’ll need to hire research engineers or machine learning operations professionals from there. Research is only a small part of using AI in production. Research engineers will then release your research into production, monitor your model’s results, and refine it if it stops predicting well (or otherwise is not operating as planned). Often, they’ll use automation to simplify monitoring and deployment procedures instead of doing everything manually.

None of this falls within the scope of a research scientist — they’re most used to working with the data sets and models in training. That said, researchers and engineers will need to work together in a continuous feedback loop to refine and retrain models based on actual performance in inference.

Choose the problems you want to solve

The CSET research cited above shows that 85% of North American and European AI labs do some form of basic AI research and less than 15% focus on development. The rest of the world is different: Most labs in other countries, such as India and Israel, focus on growth.


I have always enjoyed writing and reading other people's blogs. I started writing a journal as a teenager and have since written numerous books and articles. My blog is a place where I can write freely about my personal interests and those of others.

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