The role of Tough Tech in Ensuring Shared, Sustainable Prosperity

By Katie Rae
CEO & Managing Partner,
The Engine

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Nearly four years ago, we founded The Engine alongside MIT because we recognized the need for new models to launch transformative breakthroughs out of the labs and on to the path of commercialization. Many of these breakthroughs hold the potential to impact our most fundamental global challenges such as climate change, human health, and the transition to a 21st-century economy that creates shared prosperity and sustainability for all.

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Katie serves as the CEO & Managing Partner of The Engine. She also serves as a Board Member at Commonwealth Fusion Systems, Form Energy, Via Separations, WoHo, and Lilac Solutions. Katie has advised hundreds of founders and invested in over 100 companies. Key investments include Pillpack (acquired by Amazon for $1 billion), Bevi, GrabCad, Synack, Zagster, NBD Nano, Neurala and Amino. She holds an MBA from Yale and BA in Biology from Oberlin College.

Addressing the world’s toughest challenges is a complex systems problem, and private capital is just one piece of the solution. Realizing the full impact of Tough Tech innovation requires public and private collaboration to go from breakthrough technology to commercialization to ethical, widespread impact on our economies and societies. …


What we learned from the collapse of investment in Cleantech 1.0 and how we can ensure the success of Cleantech 2.0.

By Michael Kearney, Senior Associate, The Engine

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Numerous accounts have documented the collapse of venture investment in the clean-technology sector during the first fifteen years of the 21st century. Retrospectively known as Cleantech 1.0, investors piled $25 billion into cleantech startups from 2006–2011, funds that resulted in little return on capital. [1]

The subsequent flight of capital from cleantech increased commercialization challenges for the struggling sector. In the latter part of the 2010s, however, the tide turned once again for cleantech startups. With $4 billion invested in the space since 2017, investors clearly have renewed interest in supporting cleantech companies.

So, what have we learned from Cleantech 1.0? What are investment firms doing differently to account for this newfound knowledge? What problems may still exist, and what can be done to solve them? In short, the investment community has moved to account for the deep technical risk, long development timelines, and capital intensity associated with cleantech investing. However, while energy markets, including electricity, fuels, and transportation infrastructure, seem large, the paths to market are arduous, and value capture in those markets is challenging. For Cleantech 2.0 to be a resounding success for venture investors, a series of structural reforms and government interventions are necessary. …


Creating world-changing energy storage tech is tough. Doing it during a pandemic is even tougher. Here’s how The Engine’s portfolio company Form Energy is pushing forward amidst global uncertainty.

By Ted Wiley, Co-Founder, President & COO of Form Energy

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We founded Form Energy with the assumption that the R&D of our core technology must be done by a team in the same space — shoulder to shoulder in the lab. In the beginning, none of us thought remote or distributed teams could deliver novel hardware that would rely on both materials science and engineering innovations. How would we integrate remote team members into the culture of our company? How would we communicate with the speed and freedom that startups require? …


Part V of our series, “Real Perspectives on Artificial Intelligence” features Rick Calle, AI business development lead for M12, Microsoft’s venture fund.

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How energy-intensive is the AI infrastructure today? And what does that mean for the future of discipline?

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Rick leads AI business development for M12, Microsoft’s venture fund. He works at the intersection of AI algorithms, hardware computing efficiency, and novel AI use cases. During his time with Qualcomm’s AI Research, he worked with the team that launched Qualcomm’s AI Engine into over 100 different models of AI-enabled mobile phones.

Today’s AI algorithms, software and hardware combined are 10X to 100X more energy-intensive than they should be. In light of Microsoft’s recent announcement of its carbon negative commitment, my challenge to the industry is clear: let’s improve AI hardware and software so that we don’t overheat our planet.

The computing industry is always optimizing for speed and innovation, but not necessarily considering the lifetime energy cost of that speed. I saw an inflection point around 2012 when the progression of AI hardware and algorithmic capabilities began to deviate from Moore’s law. Prior to that, most AI solutions were running on one, maybe two processors with workloads tracking to Moore’s law. …


Part IV of our series, “Real Perspectives on Artificial Intelligence” features Dan Huttenlocher, the inaugural dean of the MIT Schwarzman College of Computing.

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No matter how responsibly developed AI may be, its generality seems at once its greatest asset and its greatest danger. How do you reconcile this duality?

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Dan is the inaugural dean of the MIT Schwarzman College of Computing. Previously he helped found Cornell Tech, the digital technology oriented graduate school created by Cornell University in New York City, and served as its first Dean and Vice Provost.

I think it’s a great question. Much of this — and I don’t know if it’s fear of AI, exactly — but much of the sense of the potential dangers of AI comes from a misunderstanding of what the technology really is.

There’s often a tendency to anthropomorphize technology and with AI this tendency is much more extreme. In the end, these are still just algorithms. When we’re using machine learning, for example, they are algorithms that we can teach instead of algorithms that we have to code. But they’re still algorithms. They’re not going to become evil. There’s no rational basis for that worry at the present time (I’m not saying it’s impossible for that to be true in some future with technologies one cannot foresee today, but for the path we are on it is science fiction). …


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AI is redefining how we fight climate change and combat disease. It is even revolutionizing the systems responsible for its own evolution.

By Michael Blanding for The Engine
Illustrations by Harol Bustos

We have a romantic vision of the scientific discovery process. A white-coated chemist spends long hours in the lab, titrating, pipetting, centrifuging chemicals, until — by accident or design — they stumble upon a new molecule that might do something useful in the world. Then there are longer nights spent testing, refining, and optimizing the synthesis process, hoping that one day that chemical can be commercialized. In the luckiest of scenarios, that process might take 5 years — often it takes 10 or 20.

For Alan Aspuru-Guzik, that’s 20 years too long. “Every time a human is involved, you’re really wasting a lot of time,” says the University of Toronto chemistry professor, who wears big, grey-rimmed glasses and usually an impish grin. “You really want to have a machine doing as many things as possible.” Four years ago, while a professor at Harvard University, he succeeded in producing the first novel chemicals discovered by computer — materials for organic light-emitting diodes — selected out of 1.6 million possible candidates. From start to finish, the process just took a few months. …


Part III of our series, “Real Perspectives on Artificial Intelligence” features Rohit Prasad, the Head Scientist of Amazon’s Alexa AI.

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I recently read a note by President Reif of MIT regarding the school’s new College of Computing. He stated that its students will be “navigating the algorithmic future.” That is a provocative concept — how do you envision such a future?

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Rohit is the Vice President and Head Scientist for Alexa AI. He directs conversational AI R&D for Amazon Alexa, with a focus on natural language understanding, machine reasoning, and underlying machine learning capabilities.

It’s definitely a provocative statement — I agree with it. We are living in the early years of the golden age of AI. AI algorithms in hardware and software are becoming more and more indispensable and ubiquitous. They are part of the social fabric.

In some cases, I think algorithms will recede into the background where they assist with decision making. In other cases, I think algorithms will be embodied in physical form factors — self-driving cars, robots; these are the manifestation of AI in the physical world. Alexa, as it currently stands, is an example that blends these two schools of thought. …


Part II of our series, “Real Perspectives on Artificial Intelligence” features Colonel Randy “Laz” Gordon — an Air Force test pilot, HBS alum, AI leader, and overall badass.

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Should we remain optimistic about a future increasingly reliant on AI?

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Colonel Gordon is the Director of the USAF/MIT Artificial Intelligence Technology Accelerator. He is a Presidential Fellow, Harvard Business School Alumnus, DARPA Fellow, and MIT Fellow. He served the F-22 Combined Test Force as its commander.

When I came into this, I was very much focused on the notion of “AI as a savior” — whatever problem you have, whatever it is you’re trying to do, there’s an AI for that. But ultimately it’s a reflection of we as humans and the society that we come from. So rather than getting hyper-focused on the technology of AI, what I’ve learned is that it’s actually a behavioral science — it’s a reflection of the society that we come from.

So should we be optimistic? Absolutely. In the United States, we have a society built on some very fundamental values. During my time in the military, I traveled around to a lot of different places in the world where those fundamental values just aren’t there. In those nations in which AI technology develops, it will be a reflection of those times, those places. The U.S. is by far, in my view, is the place to be optimistic about such development. …


Part I of our series, “Real Perspectives on Artificial Intelligence” features Daniela Rus, one of the world’s leading robotics researchers.

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AI is polarizing — you see it referenced in pop culture as a savior (Star Trek) or a threat (Terminator). Or similarly, in the media — with positive news about drug discovery and health implications, and disturbing news regarding facial recognition and the potential of autonomous drones. Should we remain optimistic about a future increasingly reliant on AI? Why?

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Daniela Rus is an Electrical Engineering and Computer Science Professor at MIT, as well as the Director of its Computer Science and Artificial Intelligence Laboratory (CSAIL).

When I tell people that I am a robotics researcher, I tend to get one of two reactions. Some people get nervous. They make jokes about Skynet, and ask when the robots will take over their jobs. …


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The Synthetic Biology Revolution Is Here

By Deborah Halber for The Engine
Illustrations by
Andrés Rodríguez

Ajikumar Parayil reaches inside what looks like a black plastic wardrobe, tears a leaf from a bushy plant and sniffs it. A geranium, he guesses.

A chemical engineer and CEO, Parayil isn’t too concerned with exactly which species grow in a mini-hothouse in Manus Bio’s labs. His expertise lies in the molecular processes that let plants turn cheap, abundant resources into rare expensive chemicals such as essential oils.

Parayil’s MIT spin-off seeks to do what plants can’t — pump out large quantities of useful substances. By genetically programming fast-growing microbes to mimic the inner workings of plants, Manus Bio aims to mass-manufacture ingredients for new, cheaper, safer, more effective food and cosmetic ingredients, pharmaceuticals, and agricultural chemicals. …

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