Tau Ventures is an applied AI fund in Silicon Valley and food automation is one of our core focuses including an investment in Blendid. Our view in Nov 2019 was that capital is plentiful, that the automation won’t replace but augment humans, and that the real savings come from economies of scale.

Since then the industry has been shaken in huge ways. There have been some major entrants, for instance Uber founder Travis Kalanick’s CloudKitchens became more public (supposedly raising $400M). There have been some major failures, for instance, Zume pizza which had raised $375M from Softbank alone essentially imploded. And biggest of all, a global pandemic has ravaged the restaurant industry.

So will or won’t robots take over food? This article posits three of our core beliefs.

1) Robots = Production Yes, Distribution Yes, Service No

Robots today are great at specific tasks — chop vegetables, peel potatoes, flip burgers — and terrible at operating in unconstrained environments. You could even give them huge amounts of training data to expand their utility, but still within a tight structure. Being able to take care of a patron fully, like a human does, is still eons away. Rajat Bhageria from Chef Robotics has a provocative recent post in TechCrunch discussing in more depth the subject on narrow versus broad AI in food automation for those interested in exploring further.

At Tau we believe automation will increasingly help the production, especially the mass production, of food. Also distribution, whether it’s akin to a vending machine or getting the food fully into your home through point-to-point delivery. Production and distribution together often add up to more than half of the total cost, and the pandemic has highlighted the need for traceability and supply chain visibility.

What automation can help the least is service since a large part of the appeal of a sit-down restaurant is the overall experience, including the ambiance. We are talking specifically about mid-high end restaurants, not fast casual where we see ghost kitchens with office delivery becoming more prevalent. Our view is the sit-down model is largely on pause right now and will invariably make a comeback, even if automation can’t help it significantly.

2) Even More Startups, Going Beyond Cost Reduction

Per Pitchbook, in 2019 the food tech world broadly claimed $13.5B in venture capital across 440 deals worldwide. 2020 is surely going to be a year of significant downturn, with less deals and less capital, but at Tau we believe the relative proportion of food automation will increase and some of that will sustain much forward. In general we don’t see the pandemic as having overwhelmingly created new circumstances but more so accelerated secular trends. Reducing costs is always a driver behind automation and remains as such but add to it a desire for more

  • safety — contactless preparation reduces contamination risk, there is also the potential to improve safety in supply chains where risks have been particularly high
  • convenience — ghost kitchens facilitate delivery
  • innovation — for instance Minerva uses different raw materials and novel processes for making tortillas

A good signal for the momentum in a space is that there are multiple startups targeting similar markets even with similar go-to-markets per the framework below. Due caveat that frameworks, almost by definition, are guidelines to help analysis rather than black and white truth.

Item Directly Consumer Facing (predominantly plug and play solution) Human In the Loop (predominantly making production more efficient)
baking Le Bread XPress (croissant), Minerva (tortilla), Wilkinson Baking (bread)
coffee Briggo, Cafe X, Monty, Truebird
commercial Bear Robotics (robot waiters), Chef Robotics (mass production), Cloud Kitchens (shared kitchen space), Karakuri (mass production)
hamburger Creator, Flippy from Miso Robotics
pizza Basil Street, Pazzi, Picnic, Zume
smoothies Alberts, Blendid


3) Consumer Automation Is Very Far

Our view is that food automation is still years away from truly permeating our homes. In computing we had mainframes and minicomputers for decades before personal computers became significant enough. What partly drove that adoption was that PCs fundamentally made people far more productive, in fact allowed us to do tasks that weren’t even possible before. Not the same case with food automation, indeed consider the deep traditions embedded into cooking and how it relates to individuals, families and communities. Yes we have some automation, from fridges to ovens and microwaves to mixers, but they are a far cry from the level of automation in enterprise. In the home kitchen, it’s indeed not all about costs and chores.

Previously industrial robotics would reach out to factories, now they are starting to reach out to restaurants providing mostly white-labeled and cobranding models. A handful may succeed in creating their own brands but those will be very capital intensive. So when we look for investments in food automation we are asking immediate questions around CAC and LTV but also around how fundamentally the business scales, especially around unit economics.

Thank you to Cynthia Yeung and Juan Rosillo for their feedback. Originally published on “Data Driven Investor,” am happy to syndicate on other platforms. I am the Managing Partner and Cofounder of Tau Ventures with 20 years in Silicon Valley across corporates, own startup, and VC funds. These are purposely short articles focused on practical insights (I call it gl;dr — good length; did read). Many of my writings are at and I would be stoked if they get people interested enough in a topic to explore in further depth. If this article had useful insights for you comment away and/or give a like on the article and on the Tau Ventures’ LinkedIn page, with due thanks for supporting our work. All opinions expressed here are my own.

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Amit Garg
I have been in Silicon Valley for 20 years -- at Samsung NEXT Ventures, running my own startup (as of May 2019 a series D that has raised $120M and valued at $450M), at Norwest Ventures, and doing product and analytics at Google. My academic training is BS in computer science and MS in biomedical informatics, both from Stanford, and MBA from Harvard. I speak natively 3 languages, live carbon-neutral, am a 70.3 Ironman finisher, and have built a hospital in rural India serving 100,000 people.


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