When I first heard of “tough tech”, I envisioned it as a realm filled with lofty, half-practical moonshot ideas, more similar to faraway dreams than tangible realities. This original perception took a 180-degree turn as soon as I stepped foot into the 2023 Tough Tech Summit. The minute I walked into the main networking area of the venue, I overheard conversations that completely shattered my preconceived notions– one company raising a staggering $1B in Series B funding, another busy constructing a 50-acre manufacturing site, and a third producing and over 15,000 units just one year after their launch.
I immediately wanted to dive deeper into this world. I’ll have to admit, though– it was a bit intimidating at first, wading into these discussions. PhD titles at Tough Tech seemed as common as high school diplomas in the outside world. Some attendees casually asked about my postdoc research, which led me to subtly hint I hadn’t quite reached that stage yet (truth be told, I hadn’t even finished my undergrad degree).
On top of all of this, topics like climate tech, biotech, fusion technology were all the rage– fields I knew very little about. Meanwhile, my main area of concentration and full-time job revolved around blockchain and crypto, which are unfortunately subjects that tend to raise skepticism. SBF’s trials right now are not helping much either (I’m kidding haha).
I came up with a few topics that I thought could potentially create synergies and bridge intersections between their expertise and my familiarity with different fields. I would’ve never expected to see so many intersections between seemingly unrelated areas. It was also incredible to see these intersections apply to other intersections to create entirely new disciplines. It turns out, fields are never isolated.
A deeper dive into the conversations about interesting intersections:
Quantum x Security — (Tim @ Atlantic Quantum)
Atlantic Quantum is a tough tech portfolio company within “advanced systems & infrastructure”. They are building scalable quantum computers with a new type of quantum circuit architecture that produces fewer errors and is significantly cheaper than the current industry standard.
The current industry standard leverages transmon qubits, which are a type of superconducting qubit with relatively decent coherence, less noise sensitivity and easy coupling. Google, IBM, Rigetti — the big quantum players — use transmon qubits. Atlantic Quantum, on the other hand, is using fluxonium qubits. They are a lot simpler to control, have higher coherence, lower error rates and can operate on lower frequencies.
As quantum computers are continuing to scale with the goal of becoming commercialized, there are some fears of a “quantum apocalypse”. This doomsday event describes the day that quantum computers will completely break our defenses by overpowering our conventional encryption methods. Secret files can be cracked open in an instant leaving all our personally identifiable information exposed, our savings emptied, etc.
There’s a worry that as quantum computers become more advanced, our current security systems become more weak. As a result of this possibility, there is development towards a new area: post-quantum cryptography. The goal is to develop cryptographic systems that are secure against quantum computers. How does this in turn affect the companies developing quantum computers? Would there be a divide between security companies and quantum computing developers where they are constantly trying to compete with each other?
It turns out that this future requires the collaboration between quantum and cryptography developers. I asked Tim if Atlantic Quantum was considering the quantum security landscape in the near future, especially as many companies building security solutions are considering scenarios requiring post-quantum cryptography. Tim suspects that the quantum era (commercial quantum computers) will likely arrive at least a decade from now. Even then, applying quantum computers to break RSA encryption will still require millions of strong qubits with high coherence. Currently it’s an afterthought, though he emphasized right now is the correct time to research these post-quantum cryptography implementations, as we’re dealing with the inevitable.
In order to build a truly secure future in tandem with continually advanced computing systems, we are going to have to focus on not just one, but multiple areas. Specifically, the collaboration amongst the fields of security, quantum computing and cryptography to create post-quantum cryptography approaches.
Quantum x Sensing — (Sanket @ Dirac Labs)
It’s currently pretty difficult to detect rare-earth materials in the mining industry (such as with gold, copper, nickel and cobalt). Ores are pretty complex, especially if they are abundant in more than one type of mineral. Each mineral requires a different extraction technology and mineral procesing technique. And they’re all quite costly.
That’s if you can even get to the mineral. Some elements are very difficult to detect in the first place, as they are usually found mixed with one another or with other radioactive elements.
Sometimes it comes down to a guessing game of where the mineral could be based on a scoped out area. Many minerals, such as rare earths, are mined by digging open pits in the ground. This does have negative environmental implications with the potential to produce polluted ponds filled with waste.
We’re currently faced with two problems: 1) inaccurate detection 2) inefficient and expensive extracting methods. Dirac Labs is addressing the first problem by developing quantum sensors for underground resource exploration.
Sanket mentioned for our current classical sensing technologies, only a tiny fraction of detections results in a valuable find. Adding quantum, however, adds more control with detecting desired target signals and rejecting other sources of interference. In that way, we can have more targeted detection, leading to less costly and less environmentally damaging explorations.
It’s interesting how different applications of quantum mechanical properties can have almost contrasting effects. Applying quantum computationally with qubits to build quantum algorithms currently has many headaches, with problems in errors and decoherence. On the other hand, applying quantum materially with quantum sensors actually helps to eliminate interference.
Climate Tech x Institutional Bond Markets x Blockchain (Steven @ City of Boston)
Steven is an investor and issuer in the institutional bond markets, as well as an early-stage and later stage (Series D and beyond) investor for climate tech companies. He is currently also an advisor for Michelle Wu’s Green New Deal.
Steven mentioned that despite the green bond markets becoming larger in size, verification and reporting measures aren’t keeping up. Finding the optimal balance between establishing market credibility and encouraging the markets is difficult for both investors and issuers. This applies to more specific applications, such as with climate finance.
There is some research of transitioning from asset-backed securities to blockchain, using its features of transparency, traceability and trust. In this way, we can potentially track and trace carbon savings through green lending into the institutional bond markets. The transparency aspects allow all participants in the carbon market to verify the carbon credits and track where they came from originally. We can apply this not only to investors and issuers in institutional bond markets, but also to sustainable initiatives that involve monitoring companies’ business practices.
Security x Policy — (Ariana @ Special Competitive Studies Project)
There are many regulatory constraints and policies that are in the security solution space. One example is having regulations on encryption technology has been an ongoing debate that emerged over 30 years ago. This specific debate has caused many tensions within security and privacy, law enforcement needs and individual rights.
Ariana is working on research to create proposals revising these types of policies, incorporating the viewpoints of all parties involved. There’s no easy fix to this ongoing issue. There are many stakeholders involved, including big tech companies, civil liberties organizations, cybersecurity professionals and policymakers. With all of these stakeholders, there are many differing opinions.
On one side, the majority of the public is wary of the government having access to their own data. On the other side, there’s the concern of the misues of encryption by malicious hackers that can lead to data breaches or terrorist atacks.
It was interesting to hear the perspective of those in favor of these regulatory constraints, mostly due to 1) criminals using encryption to conduct illegal activities 2) public safety. Both of these points require the involvement of law enforcement agencies, and in turn, these agencies accessing the encrypted information.
Policy especially requires the involvement of multiple fields with different perspectives. There’s no one-size fits all approach and we need the collaboration of as many people as possible to create fair regulations.
Early Stage Funding and Non-Saas Companies:
In addition to examining the technological intersections between these different areas, I was curious to hear more about the early-stage investing side. Specifically:
The “aha” moment leading to new funding that 1) clicked for investors while a company was pitching 2) clicked for investors while a company was pitching
Pitching to investors for tough tech companies is very different from that of SocialFi apps or software using SaaS models. For SaaS models, investors consider factors such as how big the user base is and projections based on current revenue data– metrics that many early-stage tough tech companies do not have yet. I wanted to ask investors what metrics or decision-making frameworks they used instead of user base size and revenue amounts.
Storytelling > Idea
At least three investors I spoke to mentioned that the founders they invested in connected all the points in their pitch into one clear story that succinctly told why they were doing this. Many companies that pitched to them all had similar value propositions and technological approaches. The idea itself, some data to back it up, and results from the lab are not enough because basically everyone has those three things. The only way to differentiate is to clearly convey to investors why it would be a mistake for them not to invest in them. At the end of the day, investors are not just investing in the company itself, they are also investing in the team.
Under Promising and Overdelivering (Not Becoming Theranos II)
This was a point emphasized by two representatives from Cambridge Consultants. You never want to do the opposite by overpromising and under-delivering. Not only does that decrease your credibility as a company, but it also decreases the trust that both clients and investors have in you. For their firm and for other investors (especially in this area) they’re still conscious about companies that sound like they could become another Theranos.
Building Team Credibility
An incubator manager from University of Wisconsin Madison emphasized that the companies in her university’s incubator with the most success were the ones who built a credible team. The founding team for all the companies she oversaw were mostly postgrads with not a significant amount of industry experience, let alone founding a company experience.
Those who acquired established advisors– technical, financial, legal– who helped with the team’s decision-making, added a significant amount of credibility to the founding team. This was also especially crucial when the company’s core tech was completed and a shift was made from R&D to actual product engineering. That’s when things “get real” and stop being conceptual and theoretical.
Not Building The Ideal Product
This piece of advice from another investor may seem counterintuitive at first, as the reason why many companies are founded are to build an improved version of a product in the market, with a vision to build an ideal version. In reality, this is a very common pitfall for many early-stage companies.
Start small, then build bigger. It’s important to pick the low-hanging fruit first to gather promising data and results early on. Data, especially collected from the company itself, either through partnered studies or pilots, is one of the biggest selling points. Investors also prefer if a company optimizes for simple architecture– the more simple, the better.
MIT Tough Tech was overall an incredible experience– I’d like to thank the Tough Tech team including Chloe, Ian and Rumi for organizing the conference and inviting TKS to help volunteer. I’d also like to give a shoutout to Michael for this opportunity- this was one of my favorite conferences I attended!
