Notes on a Holistic Model of Progress | 001

The Heart of the Question

What does progress mean to you?

Higher standard of living? New technologies? A sort of general sense of the future getting progressively better?

There is probably a thousand ways to define it. Local, global, specific to a vertical, personal impact… So many lenses, so many perspectives.

I have always been interested in change. It’s a mandate that is understood very differently in different sectors, and even org to org. A startup drives change in one way, via disruptive products. NGOs focus squarely on their mandate, optimising funding and ops to achieve the maximum impact. Gov agencies, accelerators, even the United Nations – you could map each on the impact methods they use.

Over the years, I had a chance to glimpse many of these worlds. There is much to be discovered at the boundaries of things.

Progress means different things to different people. In the context of what I’m about to write, I define it as 1) Expanding the boundaries of our understanding of reality, aka our collective scientific knowledge, 2) converting that knowledge into inventions and technology useful for humanity, and 3) distributing the benefits of technological advancement to the largest possible number of people simultaneously.

The prosperity of a society is, of course, not directly correlated with the level of technology it has access to. North Korea can access all of the world’s scientific knowledge next Tuesday if it wishes. It does not.

Setting aside the rightfully important issues of governance and institutions, however… it is science and technology that define what we are capable of accomplishing. We cannot solve a problem if we lack the tools to do so.

No skyscrapers without advanced material engineering. No life 80y+ life expectancy without modern medicine, drugs, vaccines, the healthcare system itself. No laptop, tablet or phone on which you are probably reading this without semiconductors, the Internet, OLED modules. On it goes.

Science is the First Horizon. Technology, the Second. ‘Distribution‘ – meaning, making the benefits of a new technology accessible – the Third (And in many ways the hardest to effectively advance).

The good news is that we have already created a system to accomplish this. It is what I would very broadly term the Global Progress Complex.

A colossal network of scientific institutions, aggregating the world’s knowledge from across the global. From Japan to Brazil, from Oxford to MIT, from a university research club to massive national programs.

A whole gamut of innovation-oriented companies. Deep Tech startups, established businesses pioneering new approaches internally, public companies, and many more.

And then the layers and layers of “Innovation Agencies“ of all types, from ARIA to UNIDO, from A*STAR to Y Combinator. Different mandates, different methods, different origins. Same result: accelerating innovation.

Combined, this monstrous latticework is the tip of the spear of global innovation. Interlinked, global, featuring a thousand methods and strategies.

Understanding, mapping, and accelerating this proverbial Progress Complex is the heart of my work. Call it a personal obsession. I believe that if we are able to view the entire system holistically, top-to-bottom, we could unlock entirely new models of ecosystem-level acceleration. Drive long-run progress across decades.

To begin with, little essay is my attempt to unify and convey my thinking. Each of the successive ‘Notes’ in this series will go deeper on specific components:

1. Flow of Change: scientific discovery > technological innovation > distribution of prosperity, and how we speed up this cycle.
2. Scope of Change: breaking down and studying all the different methods of innovation, from individual companies to local ecosystems and global agencies.
3. Factors of Change: understanding the ‘meta-factors‘ affecting innovation, from talent and policy to governance and capital. Further, I will also analyse how different organisations employ and boost these factors to speed up innovation ecosystems.

My take on the subject is a fusion of two dozen models, from innovation ecosystem theory to theories of change and more. I’ve spent a good deal of time combining methods to try and achieve a holistic lens that capture progress in its entirety, with every single inflection point and accelerant.

Because if we are able to understand that, perhaps we can speed up the whole machine. Shortcut to Abundance. Appealing, isn’t it?

Flow of Change

There are many ways to slice and dice the flow of innovation.

I want to preface by saying that it is, of course, a feedback loop. “Flow“ implies linearity, where in reality it is closer to having a single river stream that loops in on itself at many points, merges with other rivers along the way, and so on.

Nevertheless, I believe understanding the ‘phases‘ of innovation is relevant. We have many such models: the Valley of Death curve, the various business maturity lifecycles, etc.

Here, I tried to synthesise a high-level flow model with the specific intent of breaking the process down into phases that are as distinct as possible. If our goal is to identify and design effective support organisations at each step, then it’s good to have a clear mandate linked to a distinct stage of innovation the organisation or cluster of organisations is supporting.

Below is are my initial thoughts on the subject:

Expanding Boundaries of Knowledge

The limit of our understanding is the most fundamental barrier to progress. Hence, science represents the Possibility Horizon; the first of three.

I view science as opening the first door — or discovering there is a door there in the first place. It shows us what is possible, and gives us the understanding of how certain things can be achieved. To change, first you must understand.

Take something like fusion energy. At present, there can be no conversation about making fusion energy accessible to people, optimising it, etc… because we haven’t yet overcome the very first barrier of achieving net fusion. The first boundary has not yet been crossed.

Once we do crack this first barrier, however, the usual commercialisation cycle will kick in. Then it’s up to technological refinements, unit economics, and a plethora of other downstream problems to overcome.

The same logic applies to many of the most foundational barriers humanity faces. We cannot yet ”cure" aging, cannot make atomic manufacturing available en masse, and so on, because we simply lack the necessary scientific understanding to make any of the above possible.

Expanding Our Level of Technology

I think it’s fair to say that technology is fundamentally the collection of various tools, instruments and devices we have access to. Software, hardware, everything from materials to gene editing to modern rockets. The Sum Total of what we can do, to put in another way.

This in turn defines what is possible, and the kinds of problems we can solve. The Second Boundary. We both understand how to do something, and we have invented the technology to do so.

This is the innovation cluster under which you could write down deep tech startups (Among others), R&D labs, government research agencies, university labs, on the list continues.

This is the layer of progress which converts understanding into utility, into solutions for the problems we face and new ways of improving our lives.

Some of you may notice that the distinction between Science and Technology is a very fine line in the discussion above. Fair point, yes.

Sometimes saying “We lack the technology to do that“ in practice really means “We have the base scientific understanding of how to achieve X, but we haven’t yet figured out how to convert this into useful tech, and may need further development and additional scientific research to advance“.

The two are intimately connected. Still, I think this is a relevant distinction to make, especially when it comes to how we approach accelerating progress. Funding scientific research can directly result in new technology, but it is not synonymous with funding technological development or innovation. Sometimes technology actually leads to scientific discoveries. It’s a complex relationship, more so than I can capture in my deliberately simplified model.

The Meta-Factors

You could potentially end this section on just the above. Progress comes from science and technology. We have to speed those up. End of story.

If only.

The thing is, we already have both the science and the tech to solve all sorts of problems. World hunger is technologically solved, yet it persists. There is a long list of examples like this, from internet access to electricity coverage.

This is not exactly a novel statement. One could argue the contexts of the two are so different that it’s not worth bridging the two camps. Science and Tech here, all sorts of other barriers over there.

Well, I fundamentally disagree with this approach. If we want to have a serious conversation about ‘progress‘, what matters is the result and not the means. We can go on advancing technological progress all we like, but that won’t feed the hungry. Hence why the ‘distributing the benefits‘ part from the very beginning matters.

The reason I’ve titled this section ‘meta-factors‘ is because… well, here is where things get messy. International development has, conservatively speaking, the equivalent of seven Swiss army knives’ worth of tools. Education, financing, health campaigns… there are as many means as contributing factors.

This isn’t limited to ID either. The same set of problems arise in any country in the world: how to best design and maintain a healthcare system, what are the best policy to incentivise startup creation, what are the budget tradeoffs between redirecting more money to finance education or pay for new housing developments?

Welcome to the extremely murky waters of managing a society.

I will write a deep dive on this at some later point. For now, I’ll leave a question: how, then, do we design our society to best deploy and adopt available technology to benefit the most people? And by society, I really mean the entire world.

Scope of Change

Suppose you want to make an impact. Where do you look?

The tried and true startup advice is to solve what you know. A personal problem. Something in your company or industry. Something you’ve seen in your city.

For any startup to be viable, the problem has to be shared by a sufficiently large group of people. Even if you’re not shooting for the moon, it has to be something substantial enough that it is worth solving. Personal or very local challenges can be better overcome by other means entirely.

Some problems aren’t something any one organisation can overcome. Take cancer. It is fundamentally a problem of our scientific understanding, and a lack of sufficiently effective medical technology to combat it. Now, you could redefine the problem as solving a specific type of cancer. You’re much more likely to make headway here, isolating the problem that way. But cancer itself, as a type of disease, can only be truly overcome by the combined scientific-medical complex of the world, with dozens of research institutes and thousands of professionals chipping away at it.

Climate change for one is a great example of something that spans all scales. It is a fundamentally global problem, in the sense that it is 1) planetary in nature, and 2) affect everyone on Earth, even if not equally. Conversely, overcoming this challenge requires international cooperation. But there are also local, regional, industry level to it.

The through-line to all of the above is simple: there are scopes to problems. If you want to solve something, you have to understand where to look.

This is especially relevant for organisational mandates and model design.

Take UK’s ARIA. Per their website, it is “A UK R&D funding agency built to unlock scientific and technological breakthroughs that benefit everyone”. Modelled on US’s ARPA group of agencies, DARPA probably being the most famous of those, it is in effect a launchpad for high-risk science that can potentially unlock massive downstream opportunities. Their Opportunity Spaces are a treat for the imagination – would recommend everyone to have a look.

ARIA is what I would call a Phase One, Region-scope agency. The focus is on the very foundational stage of innovation (Breakthrough science that underpins potential technologies), and squarely on the UK as a country (ARIA being a UK non-department public body). Such agencies can have outsized impact by shaping the entire “funnel“ from the very start. Each new discovery can create the space for 5, 10, 20 companies on top of it.

Crucially, though, you need the corresponding ecosystem on top of it to truly take advantage of such work. That means the necessary capital willing to fund Deep Tech startups, an ecosystem to produce and/or attract capable founders, the necessary technical and other talent, and so on.

This, I suppose, raises a curious point about ecosystem lenses. For an ecosystem to be effective, you have to have all stages, from science to technology and the supporting factors such as talent, capital, and so on.

What happens when all components aren’t there? Well, you get things like the founder outflow from Europe to the US, owing to how good that ecosystem is at fuelling startups at later stages of scaling. The EU ecosystem is in fact incredibly strong across the initial stages, with many top-level universities and adjacent research hubs (ETH, Oxford, Cambridge, etc), as well as the corresponding talent. But it does peter out at that later phase – or I suppose some of the meta-factors.

I’ll explore the idea of scope in-depth in later journals, because it’s quite interesting. You have organisations such as the UNDP, spanning over 100 countries with their Accelerator Labs program. But the Labs themselves are nodes. So it’s a network approach layered over another philosophy which guides the operation of the accelerators themselves. Local-to-global impact flow. There are also hyper-local, city-specific programs.

All in due time.

Factors of Change

Factors are the hardest one of the three to define, for the simple reason that everything is a factor if you push the logic far enough.

Education impacts talent. Healthcare affects the life expectancy and life patterns of any given region or demographic. Hell, you could count housing policy as a factor. Everything has some kind of downstream impact, even if weak.

But that is not very useful.

My favourite definition of factors is the actor-factor pairing from The Startup Community Way by Brad Feld and Ian Hathaway. The two have a storied history between them, both deeply involved in the different layers of startup ecosystems. It’s one of the most interesting deep dive into startup ecosystems I have ever read. The concept of factors I use here is based on their work, partially modified by me. I am, to be clear, in no way affiliated with either of them – I just keep a massive library.

So, what are factors?

In the context of progress, these are the key elements you must have for it to occur. Exactly how each is relevant, and how relevant, depends on what precisely you are looking at. A university ecosystem can succeed through talent and research alone, ‘handing off‘ the resulting products to other part of the ecosystem to become companies, products, and so on.

Any ecosystem is a mesh. It’s connected, and it succeed by working together.

At higher scope levels, though, lacking even any one factor becomes a barrier to the success and growth of that ecosystem. This is especially true of countries and regions. Different pockets of an ecosystem can collaborate relatively easily within a single city, but across countries? That is a different story. And so, even if individual organisations or smaller ecosystems don’t per see need everything, those gaps become a serious challenge at higher levels of scale.

As to what the key factors are, I’ll copy some of my examples from a previous journal:

1. Talent Hubs — universities, research parks, popular founder hubs. Places that both produce and concentrate top talent. Founders, researchers, engineers, N1 employees.
2. Innovation Culture — risk-tolerant, forward-thinking, open to new solutions to old problems. Hardly a science but you know it when you see it.
3. Capital — research grants, VC money for ventures swinging big, fellowships, seed funding, the lot.
4. Infrastructure — overlaps with others. Co-working spaces, R&D labs, etc. Physical spaces that enable innovation & collaboration.
5. Market — and the % of early adopters it has. Not all countries can support all kinds of innovation just by virtue of scale or purchasing 2 power.
6. Setup Structure — regulation of all flavours. Quick incorporation, efficient taxes, R&D credits, talent visas. Startup-suitable structure.
7. Scaffolding — the ladder. Think mentorship, events, firesides. Ways for new innovators to break in and for experienced ones to give back.

Some are more important than others. Some require others. A strong mentorship culture without the requisite factors that help startups actually succeed doesn’t do much, for instance. Conversely, it can massively amplify an ecosystem – and even help fill the other gaps – if the core elements are in place.

I see factors as more of a fluid category. Even the exact buckets change ecosystem to ecosystem, especially since I’m working with progress as a broad church concept rather than just startups per se.

What Does It All Mean?

Flow. Scope. Factors.

How progress occurs, from its very initialisation to becoming useful technology and finding useful applications.

How it happens across different scales, and the kinds of organisations that support it at each.

And the many elements necessary for it to happen.

A matrix of progress in three dimensions.

This combined model is my experiment at capturing the entire picture in one group of dimensions. I’m playing very fast and loose with the many frameworks of innovation here, really, but alas I am neither a scholar nor academic. Still, I do believe there is something here.

The complete latticework of progress is enormously complex. Civilisation advances because of shared efforts of billions of people, all working on completely different things, on different problems, in different regions. Sometimes innovation crosses over. Some ideas fuel others; some do not truly shine until decades later. It’s not a very straightforward thing.

What I think we can do is try to see where the gaps are. If we are able to understand the overall flow, however inexactly, we can see where the leverage points are. We can see what is lacking. Where new efforts are needed, what kinds of organisations could fuel the next generation of progress, and which ecosystems could be nurtured or even created to make that happen.

My takeaway, thus far, is that progress is complex – but there are dozens of points at which any of us can contribute to it.

On Future Steps

I have written and rewritten this piece over a dozen times.

In truth, this work is way beyond my capabilities. There is so much to be done here. This piece is simply the beginning of a framework.

I have originally begun this project almost accidentally. I was searching for a way to solve problems at scale. Something about the idea of focusing on just one, going all in on a single startup… that really bothers me. Perhaps I am being too ambitious, but it feels insufficient, however endless my respect is for founders tackling any problem, no matter how small.

And so, this framework is the first step on the path to finding an answer to this question. How do you accelerate progress at scale?

Here are just some things I am working on next:

1. Further Notes: this is, naturally, just a draft. I will continue to refine my model. I am in fact working on an actual, proper paper for my dissertation research focused on this very topic. A Unified Model of Progress.
2. The Four Abundances: Deep Dives into Biotechnology, Manufacturing, Compute, and Energy. The four foundational areas which represent what we are able to do. I want to do a horizon scan on each area, looking at the opportunity spaces and the organisations working to create breakthroughs there. So, a DeepTech series.
3. The Most Successful Accelerants: DARPA, YC, EWOR, Bell Labs. The Factories of Innovation. I want to explore what makes them so successful, and what lessons we can learn to create more such hotspots for innovation that ends up shaping the next decades.

Until then.