A downloadable PDF of this proposal can be found at the Day One Project.
The history of the United States is replete with examples of how foundational new technologies can transform the economy and create jobs. From the automobile to the transistor to recombinant DNA, foundational technologies have enabled an expanding middle class and prosperity for millions of Americans. The U.S. federal government has played a vital role in providing and enabling early market development and applications for these technologies. …
By Katie Rae
CEO & Managing Partner, The Engine
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.
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. 
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. …
By Ted Wiley, Co-Founder, President & COO of Form Energy
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? …
How energy-intensive is the AI infrastructure today? And what does that mean for the future of discipline?
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?
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. …
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?
Should we remain optimistic about a future increasingly reliant on AI?
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?