Using data models to grade assets for investment isn’t new. Both private investors and government funding agencies use analytics to make investment decisions. Research Bridge Partners’ data analytics program has taken these best practices and applied them to principal investigators (PIs) and their labs. The result is a data model and set of algorithms that gives us unique insight on not only the research but the personality, productivity, and commercial potential of universities, PIs and their labs.
Academic research is roughly a $72 billion per year industry, and the Administration recently proposed an exponential uptick in funding. We bring data analytics to it, and we share this analysis with academic researchers and their institutions.
Our unit of analysis is the PI, not the university, but we grow the model by “island hopping” from university to university. We started with 14 universities in 2017 and now have over 100. As of Spring 2021, we had about 75,000 PIs in our model, had used the model to develop approximately 400 targets, and have interviewed about half of those to determine our first set of fellows.
We receive two questions about our data analytics program often enough that it makes sense to address them here.
The first question: “Why are we using a proprietary search method instead of the conventional university channels?” In addition to their education and research programs, American universities have built licensing offices, startup incubators and accelerators, and corporate partnering offices. The staff who support these programs work hard to find and adequately resource academic inventors.
We use these channels, both algorithmically and on the ground. Research Bridge Partners’ algorithms weight participation in these programs, and we make sure to talk with the translation professionals and academic leadership wherever we go. In addition, we use our model to independently search for faculty entrepreneurs. Research Bridge Partners’ principals used to run “access point” programs, and none of us were immune from blind spots caused by institutional politics, personal interest, personality fit, limited time, or whatever.
The second question: “What are the underlying features collected on each lab, and how do you assemble your algorithms?”
What we can share are some insights, including the following:
We cannot be successful without Research Bridge Partners’ proprietary analytics, but our success does not depend on them alone. Analytics are essential because they enable us to identify individual researchers who have the rare potential to be the scientific co-founders of a success biomedical startup. However, understanding that potential, let alone realizing it, can only be achieved through a deep, collaborative relationship. In other words, our people pick up where our data leave off.
by Max Rosett, Dr. Lydia McClure
What matters most in our approach isn’t money; it’s people. People are our core bet – they are what we “bridge,” at Research Bridge Partners.
Researchers at universities across the country generate innovations that have the potential to benefit society in significant ways. Research leaders at mid-continent universities are generally as productive as their counterparts on the coasts. But when it comes to commercialization, Massachusetts and California produce spin-outs at a much higher rate than the rest of the country. For example, the University of California System creates spin-outs at about twice the rate per dollar of research as the University of Texas System.
This delta gets talked about using dollars, because dollars are easy to measure and obviously important. We often point out that the Bay Area has $15 of investible venture capital for every $1 of research done by Bay Area universities, but Minneapolis only has $0.09 of investible VC for every dollar of research done by Twin Cities universities. We talk about this investment capital gap because it illustrates the larger capability and performance gap between the tech hubs and the rest of the country.
The problem is that such data points lead people to conclude that dollars are the problem. “If only we could get more VC in our local community, that would close the gap between us and Palo Alto.” This gets it backwards. The dollar gap is a symptom; the people gap is the problem.
Commercializing technology is much, much more than matching promising patents with venture capital or industry dollars. Successful startups require tight collaboration between world-leading innovators and savvy, well-connected business leaders. In turn, startups need the additional support of a talented and knowledgeable ecosystem of business talent, professional service providers, investors, and others. They also need a deep enough reservoir of these resources so that a lot of people can say “no” to the deal before the right people say “yes.” Still, many institutions and states nevertheless assume that the problem can be solved with a new building or a far-reaching marketing campaign. Or by launching a $100 million fund that covers a million square miles and most of the industry sectors in the SIC index.
The right resources are hard to build and require scale. But once the necessary scale is reached, it’s reinforcing. The scientists whose innovations can change the world are working at universities all over the country. The minimum efficient scale for their innovation is low, and so the U.S. innovation infrastructure is diffuse. But their commercialization counterparts, the rare business people capable of turning that innovation into a billion dollars’ worth of impact, overwhelmingly operate in the Bay Area or Boston. Given the velocity and depth of both markets, good opportunities and good payoffs are far more plentiful there.
It’s those men and women – the people who can turn scientific innovation into scaling startups – who are what most heartland university communities are missing. The cost to move dollars is zero. It’s all electrons now. What’s expensive to move, and non-fungible across geographies, is people with options who could be doing something else with their time.
The most powerful economic force on the planet is the search for yield. Money follows opportunities. If money isn’t flowing to local deals, it’s because the local deals often aren’t shaped to attract that capital. And if the local deals aren’t good enough, the reason is either that the science isn’t good enough – which the data doesn’t support – or that the way the mid-continent innovation has been formed into an investible opportunity isn’t good enough. We bet on the latter, and we built Research Bridge Partners to bridge those great innovations and that great talent.
by Dr. Lydia McClure, Isaac Barchas
[dropcap]U[/dropcap]niversities around the country are launching initiatives to promote “commercialization” and “entrepreneurship”. But those terms cover a lot of ground. Initiatives that fail to tease out the differences between them are likely to fail, disappointing critical constituencies and leaving important opportunities fallow.
At Research Bridge Partners, we developed a framework that captures the topography of university commercialization and entrepreneurship. It is useful in making commercialization decisions, especially early in the process when path dependencies and lock-in occur. You need to know where you are starting to know where you are going.
We look at the commercial activity at a university along two axes: who creates an innovation at a university, and who owns that innovation. Here is our 2x2 university entrepreneurship topography framework:
Who innovates: as a university leader, you have different duties to students and faculty, duties that contextualize your relationship with them regarding commercial matters. In our framework, the left two examples were launched by students and the right two examples by faculty.
Who owns: the university has different claims on th[dropcap][/dropcap]e innovation that gets commercialized on its campus depending on a number of factors – most importantly who funded the innovation, where the innovation happened, and what contractual relationship(s) the university has with the innovator. In our framework, the bottom two examples are owned by the inventor and the top two examples are owned by the University.
The stakes for you, as a university leader, are higher in some boxes than others. In particular, faculty-generated/university-owned innovation can be especially high stakes. Commercialization activities in this box have outsized potential to:
(Despite the institutional criticality of the upper-right box, it seems that a disproportionate amount of university effort goes towards supporting the lower-left box: student innovators/no university ownership. We are puzzled by this … but then again, we were not early investors in Microsoft, Dell, Facebook, or Snap.)
Let’s go more deeply into each box of the university commercialization landscape framework. As examples, because they have been well publicized, we will use company examples from Stanford’s entrepreneurship experience.
Lower-left box: Student inventors, non-university-owned IP
When it comes to students, most of us involved in commercialization and entrepreneurship seem to agree: the university’s education obligations are paramount. It is wonderful that Evan Spiegel and Bobby Murphy could come up with and evolve the idea for Snapchat while at Stanford, but Stanford’s core duty to them was pedagogical, not commercial – to the students, not to their innovation.
This box is filled with good news. Your campus and your local ecosystem probably offer an abundance of solutions and support. National partners, such as Blackstone and MassChallenge, can provide best practices, links to mentors, and potentially institutional startup capital. Also, alumni are waiting to be asked to mentor your students. There is a pretty good playbook here, including both curricular and co-curricular initiatives: entrepreneurship classes, incubators, business plan competitions, etc. Our advice is to implement as much of that playbook as possible, package these initiatives with success stories from your campus, and send the resulting student startups special delivery to the Development office for fundraising wins. (Be careful, though: this playbook doesn’t work very well for the other segments in the commercialization landscape.)
Lower-right box: Faculty inventors, non-university-owned IP
In the 1990s, Paul Romer, an economist, had an idea for an education tools company. Although this idea grew out of his work as a Stanford professor, the university did not have a claim on the underlying intellectual property. Romer formed the company, Aplia, in 2000 and raised $10 million of launch capital, which allowed Aplia to hire a strong team. Aplia grew quickly and was eventually bought by Thompson Learning.
Aplia is a great story – not only was it successful, it was successful doing something that advances the mission of universities (education), and the faculty member who started it went on to win a Nobel Prize!
However, this box can be dangerous. Here is why:
We love this box for its opportunity and flexibility, but as a university administrator, you need to make sure you gate it properly. Administrative conflict issues should be narrowed down early, and this won’t always be collaborative. When it comes to commercial work, most conflicts between a university and its faculty are due to ownership and use of IP. Calling out these conflicts is important. But when you do this, reach out as a colleague rather than with a reprimand.
When faculty who we work with seem to be roaming down this path, our message is: “Engage the university, because if you don’t launch this company well from the start, you’re killing your options. The world of autonomy you are picturing probably does not exist.”
Upper-right box: Faculty inventors, university-owned IP
One of us, Isaac, grew up on the Stanford campus, and he remembers dinner table discussions when Norm Cohen (Stanford) and Herb Boyer (Berkeley) were launching, based on their recombinant DNA and restriction enzyme technologies, what became Genentech … and with Genentech, the entire biotechnology sector. At the time, patenting the IP from basic research, not to mention putting that IP into a startup and spinning it out, was extremely unusual and not universally approved. Fast forward to now, and Genentech is the poster child for university spin-outs.
The university – not the inventor – owns innovations in the top row. This ownership creates hard obligations formalized in the Bayh-Dole Act and soft (but real!) expectations from other key stakeholders that the university will move innovation rapidly and effectively into the public markets. It also creates compliance, conflicts management, and other oversight obligations.
Unfortunately, this ownership position can put the university into an adversarial relationship with the faculty inventor. Although there is no way around these difficult conversations, some universities seem to have them more productively. These universities emphasize support over compliance in their relationships with faculty – acting as colleagues rather than cops.
This box is where Research Bridge Partners does most of our work. We have developed some counterintuitive perspectives. For example, we think that the trend is for universities to create too many spin-out companies, that many EIR programs are wastes of money, and that fast-track licenses can be a disaster. We will cover these perspectives more deeply in other blog posts.
In general, though, our core view of this segment of commercial activity: if done right, it should be a powerful tool to attract and retain outstanding faculty and directly advance the university’s mission.
Upper-left box: Student inventors, university-owned IP
Alphabet, Inc. (Google)
Alphabet, Inc., is one of the world’s most valuable companies, with a market cap pushing $1 trillion. Famously, though, when Brin and Page disclosed their search ranking algorithms to Stanford’s TLO, they were greeted with no fanfare and (if anecdote is to be believed) did not even make the office’s “top 10 list” for the year.
This box is hard:
A lot of value gets left on the table, here. Since, in our experience, few university programs are tailored to these inventors, and few administrators understand how to value the importance of these co-founders in licensing deals, undervalued or lost opportunities are common.
We handle this box by making the lab – not the IP – our unit of analysis. We look for IP-plus-post-doc combinations as the core of the spin-outs that we will help catalyze and invest in. This is not a natural act for most universities; in fact, it’s part of the value that good VCs with deep experience in academic spin-outs bring to the table in the markets where they operate.
One last point, about people. Our university entrepreneurship topography framework emphasizes that the most important action that a university can take to support entrepreneurship and commercialization, is to curate the university community. The common action that Stanford University took in all four of the example cases discussed above? It got those people onto its campus. It takes Cohens to make Genentechs. If you want Snapchats, admit Spiegels. For any university administrator, that’s job one.
[dropcap]W[/dropcap]hen I was a sophomore in college, I was diagnosed with melanoma, an aggressive form of cancer. Within 24 hours, I was on a plane home, and 24 hours after that I had surgery to remove the tumor.
That initial whirlwind of activity stood in stark contrast to what happened next: nothing. As my doctors explained, melanoma doesn't respond to chemotherapy, radiation, or any other therapy they had to offer. After surgery, my only option was to wait and hope the cancer didn't come back.
But it did. Three months later I found a lump in my neck. The disease had returned and progressed to stage IIIc. At age 20, survival statistics gave me a 40% chance of making it past my 25th birthday.
However, those statistics couldn't account for a groundbreaking development during the brief window before my cancer returned. Just weeks before I found the new tumor in my neck, a team of researchers published a paper revealing that a drug called ipilimumab improved survival in patients with advanced melanoma. For the first time, oncologists could offer melanoma patients a pharmaceutical therapy.
Although ipilimumab (which is now branded as Yervoy) wasn't approved until the next year, I was able to access the drug through a clinical trial - a development that very well may have saved my life. Had I not received the drug, there's a one-in-five chance that I wouldn't be here writing these words today, according to the study's results.
The movie-like appearance of ipilimumab in my life was the culmination of a multi-decade process that started when I was a toddler. The drug's development is rooted in university research James Allison performed in the early 1990s. (Allison shared The Nobel Prize for Physiology and Medicine 2018 for that work.)
Most accounts of ipilimumab's development fast forward to the successful large-scale trial that led to its FDA approval, overlooking the decade and a half of heavy lifting that is typically required to develop a life-saving drug. Researchers like Allison need to find a corporate home for their breakthroughs (or build one themselves.) They also need to raise capital and negotiate licenses before they can even begin to solve manufacturing challenges and run clinical trials.
These are formidable challenges. Allison told NPR that he was "depressed" by his initial failure to find a company willing to commercialize his research. Fortunately, he worked at Berkeley, in the middle of a major technology startup hub, where he was surrounded by the resources required to overcome those challenges.
Because I survived cancer, I was able to finish my degree in math at Yale, and I went on to work at the Boston Consulting Group and then Google. I'm married now, and I'm able to support my wife as she trains to be an oncologist. (This frequently takes the form of sending her pictures of our dog and cats while she's working in the hospital.)
Unlike my wife, I don't expect to work directly with cancer patients. But as the lead data scientist for Research Bridge Partners, I'm doing my part to identify and commercialize breakthrough treatments. Our non-profit has built a one-of-a-kind data model that lets us search for future James Allisons - only those who don't happen to be next door to Silicon Valley. We use data to find the researchers whose science could save lives, but who might not get the chance to do so because they work in the wrong part of the country.
We use a lot of math and a lot of domain expertise to find these men and women. Then we do our best to ensure that the science behind the next ipilimumab gets bridged to the resources and people who can turn it into Yervoy-calibre impact in the lives of patients - like me - throughout the world.
by Max Rosett
[dropcap]A[/dropcap]s a university leader or a department head, you manage and serve creative PIs making world-leading discoveries. The coastal startup clusters have the capabilities - risk capital and (more importantly) commercialization talent - to drive those innovations to market. You need to bridge your most innovative PIs to the people and networks and capital sources that are as good at business as they are at science
The issue is that the tools that you have in your toolkit, as a heartland university or departmental leader, do not give you enough leverage on the problem. The key is to put your university in position to exploit the massive investment that the American economy makes in startups and entrepreneurship, nationally.
The university commercialization toolkit is limited and not very effective. Demo days (taking opportunities to Silicon Valley and inviting investors to view them) have started to look generic and now attract mostly low ranking investment professionals. Entrepreneur-in-residence programs (hiring business people to help filter opportunities on your campus) can produce a great result if this particular EIR happens to be awesome and happens to hit an innovation where she is a market expert … but the odds of that are pretty low.
Stanford and Harvard and the other schools that are most commonly held up as being "good at" commercialization are not good at commercialization because they do better demo days or have better EIR programs. They are good at commercialization because they are located in the densest concentrations of entrepreneurial capital and translational talent on the planet. Stanford's demo day is every day of every week. Its EIR program is Silicon Valley.
The risk capital available to turn innovation into a successful startup shows the scale of the challenge:
You could have Harvard's endowment, and you still wouldn't be able to fill those gaps.
But capital is mobile, right? Theoretically, Bay Area money can find opportunities anywhere in the country. And sometimes it does ... but not very often, because venture money is tied to people, and those people value their time highly, and burning two days in transit to and from your Big 10 or Big 12 or SEC campus is an expensive proposition for them, especially given the opportunities that they have at home.
OK, so can you hire translational professionals to do that work locally for your university? You absolutely should invest in great people in your TLO and commercialization and entrepreneurial support programs. But this is table stakes, not a bet.
The market rewards the best people with these skills really, really well. As in off-the-university-pay-scale and with-comp-structured-in-a-way-that-would-be-impossible-at-a-public-school well. So you are looking for people with rare skills who are willing to work below market. That's a tough ask.
Also, it's a question of scale. Think of the diversity of research at your university. Is one person or even one team going to be omnicompetent against that entire spectrum of technologies and market applications? Partners' Healthcare Innovations, the translational group associated with Harvard's affiliated hospitals, employs about 80 people in their innovation group, and that doesn't count their TLO or Harvard's other commercialization programs. Are you going to make that kind of investment?
Even if you do, it won't get you to parity with Partners'. It's geography again. Partners' is in Boston, with its at-scale networks of well-compensated people who can turn health innovation into billion-dollar companies. You cannot build that ecosystem from whole cloth.
As a result, although we have met fantastic and dedicated people in university commercialization programs, they alone will not bridge your commercialization gap. Some of us used to be university-employed translational professionals. We had some good wins. But we did not, in any structural sense, close the gap.
Why doesn't your university "do commercialization as well as MIT?" We have heard this question asked by more than one alumnus and state-level policy maker. A big part of the reason is that you're not in Boston with open access to its industry-specific talent and capital pools. And there is nothing you can do about that.
So you can't do it all yourself. Here is what we think you can, and should, do:
First, and above everything else, recruit and retain and resource great faculty.
The other thing about the commercialization success of the Bay Area and Boston schools, is that their PIs are really, really good at creating new knowledge. Stanford alone has 156 members of the National Academy of Sciences on its faculty. Not surprisingly, those PIs attract the most outstanding and ambitious students.
When partnered with strong translational professionals and capital, the innovation from those labs nucleates companies. The students from those labs graduate to help drive innovation into commercial products and services that create economic prosperity and make people's lives better.
If your main initiatives focus on faculty quality, you won't go wrong. Only a few percent of your faculty will end up as scientific co-founders of scaling companies, but that's all you need.
Second, remove barriers to commercialization
High-impact, scaling spinouts based on deep science from research labs: that's the objective.
University leadership can make this objective easier to obtain by breaking down barriers to commercialization. Two areas where you may have opportunities, are these:
We are not advocating using commercial metrics in lieu of academic metrics for hiring or promotion decisions. First, the schools that are "good at" commercialization don't do this, so why should you? Second, it's not clear that it adds much. In our experience, if an assistant professor has created a field-defining innovation that she instantiates in a patent, this will be brought up as part of her tenure review regardless of whether "patents" are a formal review category or not. Third, how good is your P&T committee at evaluating patent quality?
Third, create an atmosphere that celebrates and re-enforces entrepreneurial success among your faculty
Entrepreneurial activity at a university is contagious. To encourage this kind of virtuous contagion, here are some tactics that you should consider:
Many schools now have translational grant programs - catalyst funds and startup grants and the like - that provide $25,000 - $250,000 of non-dilutive grant capital to pre-commercial projects, as proof-of-concept or prototype funding. These seem to be expected now, and so you probably have to do it. On the margin, we would guess that they are positive investments. However, if you have $1,000,000 per year for this purpose, we would bet that you would get more payoff from three no-strings-attached awards to your three most commercially minded, high productivity PIs, than 20 awards intended to specifically drive towards commercialization. A tiny fraction of your faculty will generate almost all of the commercial value created at your university. Double down on them.
Fourth - and this is where RPB comes in - get leverage on scale
RBP is a 501(c)(3) not-for-profit that works with you to identify the faculty - usually about 1% of a university's PIs - who "look like" prospective serial academic entrepreneurs based on our proprietary model. Sometimes our model gives us a bogus target, and sometimes we miss somebody great, so we like to run our results past you. Usually, we end up with targets that have about 50% overlap with the PIs that deans and chairs would pick on their own, but much less overlap with the PIs whom the TLO would pick. The reason for this is that most of the innovation in most of the labs on most campuses is dark (undisclosed) and not visible to the TLO.
We can't replicate the access to startup networks, talent, and capital that those faculty would have if they worked in the Bay Area, but we can approximate parts of it:
Our goal is to get your best startups ready to compete successfully for the national-quality talent and capital that they will need to be successful. Yes, we do write checks through our program-supporting venture fund, and we offer other development in-kind. The way to think about our work, though, is that we bridge your best PIs to the people and networks and capital sources that are as good at business as they are at science.
[dropcap]L[/dropcap]ast Monday, 8 October 2018, Paul Romer won the Nobel Prize in Economics for his contributions to our understanding of economic growth. The theory he developed helps explain why we do what we do.
Economic growth is really, really important. From the beginning of human history up until the Industrial Revolution, there pretty much wasn't any. You could take the average person from the Year 1 and put her in 1740, and she would find it in many ways familiar … familiarly grim and hard.
But if you took a person from 1740 and dropped her here, today, it would blow her mind.
The reason is economic growth. Growth is pure oxygen. It's what has given us longer and healthier lives and enhanced opportunities for the flourishing of the human soul without hunger or terror or bondage.
In the 1950s and 1960s, economists like Robert Solow (Nobel Prize, 1987) worked out the theory of economic growth: growth is a function of labor, capital, and ideas. Labor is people, you and me. Capital is stuff like the machine that I am typing this on, tools that make us more productive than we would be on our own. Ideas are ways to do things differently - new ways to organize people, new machines, etc.. Solow and others took these simple insights and rigorously and elegantly modeled them, laying a theoretical foundation for the analysis of economic growth.
This was incredibly powerful. One of the insights of these theories is that growth stalls when we run out of people and out of productive uses of capital. The growth curve inevitably flattens as an economy matures. The way to keep the growth rate up - the only way - is for new innovation to keep being introduced into the economy, increasing productivity.
But although growth theory highlighted the importance of innovation, it did not have much to say about it. Innovation, knowledge, ideas … for the economists of the 1950s and 1960s, they were just sort of out there, floating around, for anyone to use. So even though ideas are the drivers of productivity, in a weird way they were treated as outside of the economic system by the architects of growth theory, rather than as part of it.
Intuitively, that's not right. Inventions don't just happen. Inventions are invented by inventors. We are the authors of ideas, not just their beneficiaries.
The legal and cultural context also matters. Ideas are often owned or private. Patents and trade secrets and know-how - those ideas are not just "out there" and available for anyone to use. Other ideas are not proprietary, but they are built into systems that are really hard to copy, like social customs or legal regimes. Growth theory didn't have much to say about this.
This was a problem for growth theory. Romer solved it.
Romer brought innovation into the longstanding models for economic growth. He described how ideas are both products of the economic system and influencers of that system. Patent rights, for example, increase the incentives for certain kinds of innovation.
Knowledge is cumulative. We can, and do, make more. And we can structure society so that we get more and more knowledge, more and more innovation, more and more growth. We can do things and set up systems and make investments that are likely to encourage people to invent things or systems that make our lives better … and that themselves lead to more innovation.
To me, this is one of the most optimistic findings in all of social science.
It means that our future doesn't have to be a dystopia where all the growth has drained out of our society, with our grandchildren's lives governed by zero-sum competition for a fixed pool of resources. Our future doesn't have to be the human version of the heat death of the universe, whose energy will gradually dissipate until … nothing.
The future can be better. People behave differently when the pie is growing than when it is static or shrinking. In this way, growth enables generosity. We all have to fight parts of our natures in order to be virtuous, otherwise virtue would not be praiseworthy. Growth helps to align virtue a little bit more with self-interest.
Economic growth also builds hope. Hope for the alleviation of poverty. Hope for cures for diseases. Hope for solutions to environmental problems. Hope for more people to be able to freely follow their spiritual course and calling.
Before Romer, economics viewed growth as kind of like a lottery or a cargo cult where society waits passively for a new box of ideas to wash up on shore to fuel the growth machine. What Romer said was, "We can build those ideas ourselves; we can fuel our own growth machine; it's hard work, but with deliberate effort, we can do it." A virtuous cycle of growth driving the creation of new ideas driving more growth … that, not heat death, can be our future.
We heard you, Paul. That's why we do what we do!
The world has never seen a system that produces innovation at the rate of America's research universities. Romer writes about their uniqueness: the combination of the Morrill Act in the 19th century (the Morrill Act created America's distinctive land grant university network) and the post-World War II federal research grant system (which institutionalized federal support for mission-oriented basic research) were two of the great meta-innovations of human history. The land grant schools and federal research funding are innovations that create more innovation.
But that alone doesn't generate growth. We also need tools to drive that university innovation into the economy, or else the innovation will lie fallow. The nation's startup clusters, especially Silicon Valley and Boston, do that amazingly well. These clusters metabolize about 4 out of every 5 dollars of early-stage funding. They are another important, American-made, meta-innovation: a new way to scale ideas rapidly through an economy.
The challenge is that most of America's research doesn't happen in those clusters. 74% of American university research, by dollars invested, happens in states other than California, Massachusetts, and New York. As a result, the rate of startup creation out of most heartland universities is a lot lower than at the universities in the states with technology hubs, and the startups that do get formed are often significantly undercapitalized compared to startups in the hubs, which probably contributes to lower success rates and lower impact of heartland science on the national economy.
This seems like a waste. It's like a whole cargo container of ideas drifting past our island.
So we built Research Bridge Partners as a bridge out to that big container of ideas, to get them into our economy for the benefit of all of us.
Hat tips and further reading: Paul Romer, Tyler Cowen, Alex Tabarrok, Joshua Gans, Ross DeVol
by Isaac Barchas