A Category Theory-Inspired BBN

How the Topos Institute tackles problems in complex systems

As some readers know, I’ve recently joined Renaissance Philanthropy (RenPhil). While I hope to put many ideas from FreakTakes into practice at RenPhil, I’ll be particularly focused on one goal: building more BBNs.

For those who have not read or do not remember prior pieces on BBN-model orgs (BBNs), the biggest takeaway from my series of ARPA project histories is that, for many of ARPA’s all-time great projects, exceptional projects were the result of exceptional contractors. By “exceptional,” I mean contractor groups who were not just staffed with elite talent, but uniquely aligned with ARPA’s mission. Early ARPA success stories like stealth aircraft, early autonomous vehicles, and the ARPAnet had contractors that shared three distinct traits, rarely found together. Each:

1. Was novelty-seeking, with a strong preference for projects that pushed the technological frontier forward substantially.

2. Built useful technology for actual users. This entailed professional contract management and a willingness to focus on difficult systems engineering tasks.

3. Used more flexible team structures than academia. When compared to academia, they more effectively hired, organized, and incentivized researchers, engineers, and other experts to collaborate on applied projects in a common-sense fashion.

While recent pieces have explored how great BBNs from history mixed contracts, grants, and hybrid team structures to pursue an ambitious technical vision, today’s piece will kick off a series of pieces profiling BBNs and BBN-shaped opportunities that exist today.

The subject of today’s piece is the Topos Institute, a BBN-shaped organization staffed with PhDs from top math and computer science departments. The CEO, Brendan Fong, has a computer science PhD from Oxford, specializing in category theory and its applications. The unifying technical ambition that motivates the Topos team is the goal of building technologies to enable scientists and engineers from adjacent fields to more effectively combine and reason with overlapping models from distinct domains. Topos pursues this technical vision with a mix of carefully selected R&D contracts and traditional research grants. In today’s piece, I interview Brendan to learn more about:

• Topos’ technical vision.

• How Topos balances contracts and grants to fuel their work.

• Topos’ comparative advantages when compared to university departments, startups, etc.

• How Topos is/is not like an FRO.

• What differentiates Topos from other AI for science groups.

Enjoy!

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For those interested, Topos is looking to hire for a crucial position in its Oxford, UK office — discussed later in the piece. It’s an ideal position for someone with a nerdy fascination with the operational history of R&D and excited to contribute to managing a real applied metascience experiment. (I have also been meeting other BBN founders looking to fill similar roles. If that’s exciting to you, please see the end of the piece to learn more.)

In putting together this piece, Brendan and I had over three hours of messy, wide-ranging conversations and interviews. We’ve distilled them into this final, more readable, interview format. From here onward, the italic + bold text denotes my words and the standard text Brendan’s.

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Topos, In Short

Brendan, what is Topos from a very high level?

Topos is an experiment in a new structure for doing research for the public benefit.  In pursuit of this goal, we look to combine a focus on fundamental, long-term research similar to a university with a theory of change similar to a tech startup.

The technical area we work in is mathematics and computer science — particularly in collective modeling, collective inquiry, and cooperating in situations that are incredibly complex and multidisciplinary. We do this as a nonprofit whose work is supported by grants and contracts from groups like DARPA, ARIA, philanthropic foundations, technical firms, and gifts from high-net-worth individuals.

Topos’ North Star Technical Ambition

Can you talk more about collective modeling etc. and how that drives Topos’ grand technical vision?

Ultimately, the sort of technology we want to develop and produce is, in some sense, a new form of programming language. As we’ll talk about later in the interview, Topos works on projects in areas like climate modeling, the systems engineering of large airplanes, ensuring AI systems are resistant to catastrophic risks, etc. The communities contributing to each of these technical systems are made up of a variety of technical experts navigating different aspects of the system with their own overlapping models — which often have their own merits and their own limitations.

Topos works on projects with specific user communities. That keeps us grounded in the practicalities, ensuring we don’t get too caught up solving problems we find beautiful but are only modestly connected to practical needs. The grand goal the organization has when working on these specific problems with users is a General Theory of the Specific. We want to build a tool that, like the spreadsheet, helps a variety of users reason across situations with the richness and complexity that comes with combining and interpreting a mix of overlapping models of the world. The term I often use to describe the goal of this general programming language or spreadsheet to combine ideas from overlapping domains is “collective sense-making.”

One difference between this general modeling environment we hope to build and the spreadsheet is that we want to empower our users to represent the world in the conceptual language they use in their work, not force them to translate those representations into numbers and formal logic. A key difference between our modeling environment and even high-level programming languages is that we are more focused on declarative representations of the world, not procedural actions in the world.

As transformative as spreadsheets have been, I think we could make a similarly significant advance if this plays out right.

Do you intend for this to be a tool for mixing models in the worlds of science and engineering, in particular?

Scientists and engineers are the target user groups in the vast majority of our applied contracts. While we think that one day these tools can be helpful to someone running a coffee shop, planning a wedding, or simply just communicating carefully with friends, we see the ‘edge of the wedge’ as working in areas where people already have a rich tradition of articulating the conceptual foundation of their work. And that tends to be in the sciences.

Where Topos Fits in the R&D Ecosystem

Would you like to say anything about the distinct advantages Topos has in tackling certain technical problems — when compared to universities, startups, etc.?

We’ve chatted for a while outside this interview, and I find you very perceptive about these topics. Can you give me a quick rundown of what you think the strongest case is? Like what three things would you name? And I can see if I disagree or would like to add.

Yeah. When we had a discussion surrounding the question, “Do you consider Topos to be a ‘center of excellence outside the university?’” you pushed back. That wasn’t due to a lack of talent. You believe a huge proportion of your staff could have gone on to have successful academic careers. You pushed back because you felt the term ‘center of excellence’ was often associated with centers of excellence for ideas, not technology. You want to be a grounded org. University departments and places committed to ideas, first and foremost, tend to not respond to user needs very dynamically. So, in terms of differentiating yourself from an academic department, I’d say being staffed with similarly talented people dedicated to building technology for actual users with pressing needs is a huge differentiator.

In terms of what differentiates you from startups…VC-funded startups are often limited to areas with a market cap of ~$1 billion+. That’s a big constraint. And in most cases, they don’t really embrace hard technical risks. They often prefer engineering risks to scientific ones, etc. They embrace risks that they hope can be mostly mitigated within a couple of years…a lot of the time, at least. You recruit as ambitiously as a university department or a top startup. But you go where the hard problems are as long as you can find funding from a DARPA, ARIA, or some open-minded grant funder in Berkeley. So, to be clear, you also have an arbitrary constraint. But the arbitrary constraint is “What’s DARPA funding?” or “What’s ARIA funding?” But if you can find that funding, you don’t have to clip the wings of your ambition. It allows you to tackle all sorts of areas VC-funded firms can’t. You can let me know if you agree with all that.

Yeah, I think you pretty much got it. For groundbreaking, public good technologies, we need tight integration between research — the source of deep new ideas — and product — the source of deep new questions. Our core advantage over a university is that we can be user- and impact-driven. Our core advantage over a start-up is that we can tackle deep technical risk. Let me add a bit of color to both points though.

First, academia. One thing that cannot be done in a university setting is, I think, build and maintain technologies for the long term. And that is a core part of our theory of change and how we assess our success in basic research. We want to do basic inquiry around foundational questions — and we win enough grants that allow us to do that — but we don’t want to be measured by the publications we produce: the publications are instrumental. This contrasts with the known incentives against maintaining software and a user base in academia.

Relatedly, another way we differ from a university is being intensely collaborative and team-driven. To me, the incentive structures of academia are entirely individual. Postdocs, tenure, awards, all go to individuals for individual success. One of the reasons for starting Topos was that it was the only way we could find ourselves collaborating day-in-day-out with the top people in our field: university departments often want breadth, rather than aligned faculty well-suited to teaming up. With that system, it can be difficult to find and maintain generative collaborations that lead to core breakthroughs.

What separates us from start-ups? We’re driven by producing public goods — as long as we can find the funding. We don’t orient ourselves around what’s best for investors.

Since I know you want to move on, the last thing I’d like to add is that Topos is a lot of fun! We’re a collaborative, creative, and compassionate place with a great sense of collective purpose. I’m inspired by our team every day.

Specific Technical Projects

Topos’ technical agenda is driven, in no small part, by pursuing a General Theory of the Specific. Could you talk about some specific projects you’ve 1) already worked on or 2) would love to pursue?

One very illustrative example is a systems engineering example. Imagine you’re Boeing or Airbus and you’re constructing a passenger jet. There are thousands of people involved in the process of this construction. How do you ensure it all comes together into a vehicle that people can trust with their travel and lives for decades to come?

Some of that is scientific or engineering modeling. “What is the weight of the engine?” “What are the properties of the materials going into the airplane wing?” “How do the material properties relate to the aerodynamic properties?” Etc. But in bringing the parts together, very significant social processes are also going on. Initially, this group is responsible for this piece of the puzzle, specified in this way; that group is responsible for another piece specified in that way. There are also a lot of initial guesses that get refined over time, through new insights and changes in responsibility. This is the evolution of a process, a highly dynamic process of knowledge discovery.  It’s really important to keep track of that history and understand how different decisions have been made in response to different sorts of problems, insights, and constraints as the work of various groups converges into an interoperable design.

Passenger jets like the Boeing 737 have been flying for 50 years or more…

And, at this point, these firms often need to keep long-retired engineers on retainer, sometimes until they die, in case they’re needed to answer a question. This need often derives from this issue of organizational memory — not remembering why you did what all those years ago, how that learning should apply to the present situation, etc.

Right. Exactly. That sort of knowledge, that sort of understanding of the product, is very difficult to institutionalize with existing technologies. But if you do study the processes of collective sense-making carefully, there is reason to suspect — and it’s Topos’ ambition to build — knowledge management software, structures, techniques, and cultures so you don’t have to keep someone on retainer…unless they want to be on retainer and you want them to be on retainer.

The folks at ARIA connected us. I’d love to hear about your current AI Safety project with them.

What we’re doing with ARIA is a project for Davidad’s Safeguarded AI program. There is this idea that AI systems will increasingly drive actions in the world. This will sometimes extend to critical or complex situations. For example, it would be great if we could use AI systems to optimize the power grid of the UK — payload distribution, inform the construction of new power generation sites, and so on. But there are huge possibilities for catastrophic risks to a country. The power grid is literally the backbone of getting work, as in physical force, done in society. Power grid failures, whether accidental, due to natural disasters, or from cyber and physical attacks, could cause widespread economic and physical harm, and even deaths.

How do you negotiate over the constraints of what you want — in an accessible, high-level way — so your team can use AI tools to generate solutions that you know to be safe and respect those constraints? One thing you need in these situations, where you have all these social and physical factors, is an appropriately precise model of the world. This model should be transparent, interpretable, and verifiable in certain ways. By providing a way for people — in a collaborative and interdisciplinary way — to construct subtle, formal models of the world, you can enable them to run these AI systems inside sandboxes. In these sandboxes, you might have tools helping you to combine techniques that allow you to validate your model and constraints — e.g. automated reasoning, formal verification, theorem proving, etc. Doing this, a team can generate very strong pieces of evidence (almost guarantees) that the system will be safe enough for public deployment.

Do you have any examples of ARIA projects you’d jump at the opportunity to work on? This can either be from their existing program areas or areas you suspect they’ll pursue in the future. ARIA’s program areas are all scientifically-motivated and seem to have an AI for science component. And, as we discussed, there are difficulties that come with pursuing this sort of work entirely within academia. There’s a lot of room for exciting Topos collaborations.

What would we love to do with ARIA? Another program that has conceptual similarities to the Safeguarded AI program is Sarah and Gemma’s program on Forecasting Climate Tipping Points. Their program deals with an extremely complex system — understood only through integrating perspectives from multiple disciplines, including both the physical and social sciences — with deep impacts on society. Their goal is to produce an early warning system for climate catastrophes that is precise, trustworthy, and actionable.

As the program thesis outlines, “our best climate models are computationally expensive and do not capture all the physical processes we need.” Moreover, we want to avoid tipping points, so we don’t and hopefully won’t have too much data on them! And the complexity of the system requires the integration of information at a wide range of spatial and temporal scales. We’d love to undertake a project on mathematical and software development of climate modeling tools that respond to these challenges. Similarly to our ARIA project with Davidad, we could work with ARIA creators (contractors) to figure out things like, “What are the bounds of the system?” and “Under what circumstances will it go into a catastrophic region of behavior?” We’d try to develop tools that enabled domain scientists to construct a shared, interdisciplinary understanding of the climate system that is responsive to new information from sensing systems and research.¹

Of course, what that hypothetical project would look like specifically depends on many things — the needs of the program and other creators, etc. I’d be interested in exploring what you’ve mentioned, which is the idea of Topos being a sort of consulting AI for science shop that comes in and assists with tooling to solve problems in and between particular domain areas. Academia is not set up to support cross-disciplinary tooling and workflows, and we’d love to complement the academic system in this way. But…I’d like to be very clear and say that domains are very important. When talking about providing AI tools for other scientific disciplines, I want to do it from a place of humility…

If I can interrupt, do you mind talking about why climate science is such an interesting area for Topos first? And then we can re-visit your point on bringing a level of humility to AI for science work.

Yes, of course. Climate science is a great example of where our work can add value. We build computational tools for cooperating across the different conceptual frameworks and disciplines required to navigate complex systems. One basic, almost too-obvious-to-state idea underlying our work is that a good way of making concrete progress on difficult topics is writing things down. You want to write down models; you want these written models to precisely reflect your understanding of the world.

To adequately describe complex systems, we must use models from many different disciplines simultaneously. The problem, however, is that disciplines often create silos and it becomes difficult to collaborate across these different perspectives. So, we need a way of allowing conceptual-specific language that also enables interoperability between the various languages. A major focus of our tools is this process of translation, mapping between perspectives, identifying overlap, and identifying difference.

In tackling this work, you really reveal the complexity of the situation. It’s a challenge. Things become very messy, very fast. For some arbitrary climate science problem, an oceanographer might be trying to construct maps of the Pacific Ocean, how it works, the concepts involved, etc. The oceanographer will have a particular idea about what water is and what the important features of it are. This is very useful, but when building a climate model you’ll also want to have insights from an atmospheric physicist (etc.). Depending on the situation, an atmospheric physicist will have their own model with different primitives. Factors like solar radiation or windspeed might be more or less important, or interpreted in a different way, than in the oceanographer’s model.

There’s an overlap between these concepts: both fields are talking about water. But an atmospheric physicist is talking much less about ice and much more about water vapor, clouds, etc. But they’re still talking about water. How do you translate between what water means to an oceanographer and what water means to an atmospheric physicist?

One approach we use to tackle problems like this — drawing on our mathematics and computer science backgrounds — is, as you collect data, setting up database ontologies and more sophisticated logical tools to help specify what water is from various points of view. From one point of view, water is some combination of precipitation, cloud cover, snow, etc. But the relational aspects are also very important in forming a more full worldview. From this POV, water doesn’t mean anything in and of itself; it’s something like a database column heading. What reveals the quality of water could be how it relates to what the plankton need — or other parts of the ecosystem.

Having to translate what water means in one field vs. another can be incredibly complex. A single concept here can turn into a web of relationships over there. When dealing with problems like how water is witnessed through its relationships to animal life in the oceans, you might even have to translate between webs of relationships. In trying to create interoperability between frameworks, there are a whole lot of interdependencies that are incredibly difficult to track. You want sophisticated, formal computational tools to help you track that. That’s what Topos tries to build.

With our approach, the scope for ambition in climate science is immense. Consider the current state-of-the-art in the field. There are these gigantic climatic models that groups like the IPCC coordinate. With these models you’re talking about millions of lines of code, often in low-level languages like FORTRAN, written over the past half-century by thousands of scientists across many disciplines. There are good reasons for using FORTRAN, but there’s a recognized need for better tools too. They’re trying to weave all these models together, but they’re presented in ways that are so procedural — not conceptual — that you cannot get at the emergent behavior as these disciplines interact. To tackle this particular application would require succeeding on many smaller, shorter-term problems first — like the systems engineering problem above. But it’s the exact kind of problem we hope to build towards.

Humility, Sourcing Contracts, and the Prospect of Being an FRO

Now, if we can, I’d love to get back to the idea of pursuing this interdisciplinary work with humility. It’s very important to us. Our work relies on deep, respectful relationships with domain experts, our collaborators. To establish such relationships, it’s critical we acknowledge that it’s very rare for an outside group to come in and do things that are groundbreaking in the minds of people who have been working in that area…

If I can interject, it sounds to me like the Topos approach comes in with more humility than is typical from many AI for science groups. As I understand them, your proposals for applied projects are akin to what Blackboard would have been in the early CMU Robotics Institute’s autonomous vehicle work. You’re not coming in and saying, “Let’s build up more vision data and we’re going to run it through an existing set of models. Scale is going to solve it.” Your pitch, to me, sounds something like: “This is a really complex system with a lot of disciplines that have something useful to say about it. We’d like to come in and build you tools that can be something like a synthesized reasoning engine.” Your proposal attempts to take in all the existing models and output something very interpretable, while very overtly attempting to preserve what specialists would consider useful about their fields’ models. You’re not looking to supplant them with any kind of neural net or random forest.

Yes, exactly.

Now, there probably should be some contractor that comes in and tries to get people to build up more data, train neural nets, and things like that. But it sounds like Topos has a pretty differentiated offering — one that has a lot of humility.

Right. We’re not the kind of AI for science shop that seeks to combine clever algorithms, a wealth of data, and compute to yield breakthrough insights. That certainly has its place, and we’ve seen some stunning successes from this approach, including the Nobel Prize-winning work on AlphaFold. But that’s not our comparative advantage. What we want to offer is a much more bespoke product. We hope that through our applied projects we can assist users in eliciting the structures of and clarify the thinking in their domain. Over time, we hope to build up a software stack that helps reify some of that knowledge that has been clarified. If all goes well, our tools will enable them to more easily build up or utilize that knowledge.

If you could raise the funds, would becoming an FRO be an ideal scenario? That’s not easy. But let’s say someone offered you $50 million over 7 years to pursue a big FRO-style project, what would you do with it?

That’s a very interesting question. I guess the answer to your question is: we’d love a modest amount of FRO-style funding — say $10 million — but even with this funding we’d continue to seek out contracts too. It’s a bit too early for us to need something like $50 million over seven years. With the more modest $10 million, we could do a lot of research and engineering groundwork to develop fundamental aspects of our modeling environment. (See CatColab if curious.)

But for us to do good quality work, we need to remain grounded. General research funds are great, but another key resource for us is pressing problems from domain partners invested in the challenges of collaborating, articulating questions carefully, and working together toward solutions. Being resource-constrained, as we are, incentivizes us to maintain close engagements with contract partners with concrete needs.

We’re still figuring out the resource balance for Topos. I’ve found your work on BBNs as an alternative to FROs a very helpful framework for thinking this through. For example, it’s helped me find some questions that explore whether or not we’re on the right path. On one end, I ask questions like, “Are we becoming unmoored and doing too much research for research/philosophy’s sake?” or “Are we responding to actual problems in the world?” On the other end, I ask questions like, “Do we have the freedom to do really ambitious thinking and reset paradigms where we need to reset them?” or “Are we so constrained by the needs of particular contracts that we cannot, where we feel we have an edge on a new foundational approach, pursue that edge as necessary?”

We speak of Topos as vertically integrating foundational research, technology development, and public service. Navigating these questions, and thinking about the balance between FROs, BBNs, and other organizational models, is part of the process of making this idea reality.

Can you say a bit about how you obtain applied contracts?

Our most productive applied projects are driven by shared visions and strong relationships with program managers, whether at DARPA, ARIA, or in private industry. They’ll have a particular concrete problem space they’re exploring. Over the years, through our publications and through conversations, they develop an understanding of what we do and how we can help their programs. That often turns into them saying something like, “Okay, we want a proof of concept. My program has a need in [simulated multiphysics, systems biology, epidemiology, or something like that]. Can you propose a way to demonstrate your ideas in this particular domain?” We then go do a bunch of research on the problem area, often collaborating with domain experts, and construct a bunch of software around this problem — built on top of some basic core libraries we build and maintain.

How about undirected funds?

Undirected funds and, just as important, a strong network of visionary supporters, have been critical in getting Topos off the ground. We are an organizational experiment. Experimenting, exploring, and learning what does and doesn’t work for us: this takes real resources — it’s been around a third of our total funding so far.

Our founding board — particularly our founding chair, Ilyas Khan — provided not just resources but also mentorship, courage, and connections. I’ll be forever grateful to early major donors like Jaan Tallinn and Jed McCaleb for their trust, even despite our naivety and inexperience. Berkeley and Oxford have both been great communities for finding collaboration with open-minded funders who both appreciate our technical ambition and that breakthroughs in some of our approaches can have exciting spillovers for the wider science and technology ecosystem.

A Possible UK Expansion

You’re looking to make a big hire. As I understand it, you’re looking for someone to be your key operational person as you expand your Oxford, UK team. Finding the right person might even make the difference in whether or not you expand. It sounds like they would be both an operational hire and a strategy hire, interfacing between ARIA and your technical team as well as helping the team come up with new project areas. Can you talk more about what this person would do and why it’s such an exciting position?

I’ll preface this by emphasizing that Topos is an ongoing experiment and we’re still seeing it through. So it’s hard to speak on precisely what this person would do. But maybe that’s the point: the right person will find space for innovation exciting!

What is my best first guess? I think we need someone who sees the importance of Topos’ vision — humanity cooperating around issues of complexity. They should believe this technology can be produced by our team, which is (in my opinion) one of the most impressive technology teams around at the moment. And this person should want it to be produced as a public good.

This person is meant to help us continually find answers to practical questions like, “How do we organize this team of technical talent, fund them, and motivate them to ensure we have a sustainable approach to building technologies that move humanity forward?” A mix of philanthropic grants, contracts, and maybe even investments (for certain products) might be involved. Beyond helping us make these decisions, they’re meant to take on a lot of the day-to-day responsibilities necessary to ensure our collaborations run smoothly. This first guess will be refined in concert with the applicant, the wider team, and ultimately practical experience.

As far as the type of person we’re looking for? They don’t need to be good at mathematics. It might even be good if they weren’t. But they should appreciate mathematical ideas, be open-minded and willing to learn how researchers think, and be able to get along well with our technical staff and staff at partner organizations like ARIA.

Many backgrounds could be well-suited to the role. These include:

• An operator with the ability to pitch and manage — who is also driven by public service. This person should also be excited by the experimental nature of our organizational model and the prospect of shaping it.

• A mathematician/computer scientist who gets people and business…and is willing to not do the mathematics. While this person wouldn’t be doing research, having an appreciation for mathematics might give them a deep understanding of what this org is about.

• A domain scientist who, beyond being good with people and business, sees the particular problems of their field as a lens onto a general problem. We’ve had some collaborations with domain-specific scientists flounder a bit because they wanted to start over-tuning for that particular field. That is not a terrible instinct when working on a specific applied contract, but a bad one for someone helping steer our UK operation. We are looking for a General Theory of the Specific, after all.

• A software engineer whose first instinct is not to respond to things by building systems. Software engineers often respond to things by building rules-based systems; that is the nature of the craft. We’ve had only ten contracts throughout the history of the organization. The wrong software engineer might be tempted to immediately begin building databases, search tools, etc. around those learnings. Those approaches can be powerful, but I think what is needed is a bit of patience. This person needs to embrace that this is an ongoing organizational experiment in how to do science better; we’re still learning. We only have an inkling of what’s best. Formalizing too soon is not respecting the problem.

Lastly, I’d like this person to have a really creative spirit around how to build a team that does ambitious basic research that can also build. As we’ve discussed outside this interview, our early DARPA contracts taught us a lot of lessons we hope to incorporate moving forward. I’d estimate that at least half of the funds from those contracts directly funded work we’d have pursued anyway with an undirected NSF grant. This new hire should be excited about helping us figure out a revenue strategy that gets us as close to 100% as possible, and can find exciting partners/contracts to make it happen.

Conclusion

Any final words?

We’re something like a BBN-style org, right now. We have a clear technical vision that we pursue with a mix of contracts with carefully selected partners and grants. These bespoke projects are key to what we do and create deep insights into the nature of what the general problem actually is. Balancing vision and contracts enables us to build technologies that enable cooperation across different perspectives. Parts of our fundraising and governance approaches may change over time — there are many upsides to the FRO governance model, raising a basic research endowment, consulting using internal tools, etc. But our general goals of enabling cooperation in areas of complexity through a balance of basic research and applied projects will not.

Finally, thank you, Eric, so much for this conversation, and for the many leading up to it. It’s been a real privilege and, as always, you’ve helped push my understanding and articulation of Topos deeper.

It’s been a pleasure. I learned a ton through our conversations. I really hope one of my readers (or someone they know!) finds this piece and reaches out. It’s the kind of job I’d have dreamed of before FreakTakes, when I spent my nights reading about scientific history with no clue how to get hired and make a difference at any of the new, exciting organizational experiments I’d heard were being founded in places like Berkeley.

Thanks for reading:) If you’re interested in Brendan’s position, please reach out to him (first name@topos.institute) or me via email (gilliam@renphil.org) or Twitter. And if you’d love to fill a similar role at a BBN in a different technical area, please reach out! I might be able to connect you with a researcher in need of a systems engineer/operator. Some technical areas in which I already know a BBN founder open to hiring (at least part-time) for similar work include:

• Non-Model Organisms

• Economic Complexity

• Ocean Water Sensors

• Bespoke EE and Biotech Hardware

• Computational Chemistry

The Topos team, March 2022. Left to right: Tish Tanski, Juliet Szatko, David Spivak, Valeria de Paiva, Evan Patterson, Toby Smithe, Brandon Shapiro, Brendan Fong, Tim Hosgood, Sophie Libkind.

1. It would mean a lot to me personally to note that climate change, via John Baez’s Azimuth Project, was, in fact, the problem that motivated me to pursue graduate studies in the first place!