Chapter 5: Strategy
Purpose, landscape, climate, doctrine, leadership
Carbon Mapper
This is the story of how we created and sold the Carbon Mapper mission to measure the methane leaks responsible for a significant fraction of global warming.
Agenda
Strategy
The next big thing
Something cool I found on the internet
Tales from the soccer pitch
Metrics
Shameless plug
Strategy
In 2014, I was working at JPL, and lamenting the demise of OpTIIX. This was to be an astronomical telescope, robotically assembled on the space station, to demonstrate that with better design tools and active feedback, we could actually make something work in space that was too big to test on the ground. I was in charge of the laser metrology system, which measured all the mirror positions and then put them back where they were supposed to go, compensating for thermal expansion, manufacturing defects, and assembly errors. It would have been a pathfinder to a future ginormous space telescope, and a step up for me as well, managing a $15M mission system. JPL, Goddard, and Johnson space centers collaborated on it, and in only a few months, the team built an extremely stable line-locked laser prototype, simulated the closed-loop performance of the entire telescope under a variety of perturbations, and even demonstrated a backlash-free gimbal arm to point the telescope. We had a plan to build it, and the hardware and data to validate the assumptions. The line managers and project leadership assured us that we knocked it out of the park at the Preliminary Design Review, whose board included NASA’s director of science. And then, the mission shut down and everybody had to find a new job in a hurry.
This wasn’t the first time that I’d been assigned to a project that was canceled for reasons that were not the fault of the engineering team. And, in retrospect, those reasons should have been obvious from the outset. The problems were political, economic, and completely predictable. We should have addressed those first, instead of racing ahead to design the telescope. We should have thought of the mission in terms of total enterprise value, not just as cool space hardware. You might think that’s obvious, but some of these guys have enjoyed lifetime employment since the 1960s. It would never occur to many of them that the game theory could be upside-down (see Chapter 3, Incentives).
Of course, I’d gotten the OpTIIX job in part because nobody else wanted it. That should have been a warning sign. Much of my career so far had been as a firefighter, taking over and rescuing otherwise doomed deliverables. The well-funded, adequately resourced jobs tended to go to more senior people. That’s when I, being almost 40 years old, and the youngest person in the cafeteria at noon on a random weekday, realized that if I wanted a good assignment, I’d have to create it myself.
Now, my mom, president of our family business, Raosoft, says, “Selling is about making a product that people want to buy, at a price that they can afford, and at a price that you can afford to product it.” That sounds simple enough, but we all know scientists with really cool tech that nobody actually wants, or engineers excited about an idea that only needs $10M of investment to access a $5M market. So I set out to find an existing product line that didn’t already have an empire using it in space, and figure out how to derive massive value from it.
Step one was to write a playbook for myself, and step two was to find some like-minded co-conspirators. Skulking off-lab for brunch, we quickly concluded that the technology that JPL had that was the most special, affordable, and desirable was the imaging spectrometers. Radar, for which JPL had a storied history, you could buy anywhere. LIDAR from space sounds nice, but there aren’t enough photons reflected from the surface of the planet to measure anything other than billable hours. Laser communication from space is a feature, not a product. Infrared imaging was the Defense Department’s business, not NASA’s. In fact, for many of the instruments in orbit and looking back at our planet, a good fraction of the scientific papers published about them are about how to turn the data into useful information, years and years after launch. That left hyperspectral imaging, or, as its practitioners prefer to call it, imaging spectroscopy, as the sensor worth investing in. Most importantly, the people who had been working on it for decades, and held the essential tacit knowledge, didn’t throw me out of their offices when I offered to help them sell a space mission.
JPL had been building imaging spectrometers for a long time, notably the Airborne Visible - Infrared Imaging Spectrometer, or AVIRIS. Variants fly around in airplanes, one orbits Mars, and another had been sent to the Moon. Thanks to decades of investment in intellectual capital, tools, methods, and models, they worked really well. Yet, none orbited the Earth. That’s an opportunity we can drive a flaming bus through!
Great, now how does one go about doing that?
Wardley maps
That’s around when I stumbled upon Wardley Maps. What’s that? So glad you asked. Grab a warm beverage and settle in. We’ll start by reviewing the landscape.
Simon Wardley is a tech exec and philosopher, with a method for visually describing business strategy. I think it’s the most significant insight about strategy since the OODA loop. I’ll give you a teaser, and you can read his 19-chapter e-book later.
To make a map, start by drawing a vertical axis representing the value chain. It goes from invisible, at the bottom, to visible, at the top. Then you draw a horizontal axis, which goes from Invention on the left to Utility on the right. The customer will be an anchor, somewhere at the top. Let’s call NASA the anchor, and put it in the middle, being a fairly sophisticated customer who prefers not to worry about the details.
Here’s my map of earth-observation-from-space. The customers, the parts, and the processes are islands, connected by lines. The lines reflect flows of information, of capital, of risk, … whatever. The placement is subjective. Don’t overthink it. All models are wrong; some models are useful.
So, what’s that good for? Well, Wardley realized that progress means not having to think about complexity. We used to know the speed of our modems, the wattage of our microwave oven, how many MHz our computer could do. I don’t even know how many processors are in this computer, and I built it myself. I don’t care. There was once a time when cars had toolboxes and spare parts in the trunk. Now, many don’t even have spare tires. Progresses is when cool turns boring. Therefore, imagine an invisible force that wants everything to move to the right, from a new invention, to custom engineering, to an industrial product, to a commercial product, to a utility.
This is a familiar progression: at Raosoft, we tried to come up with 10 new product ideas a year, each in cooperation with a customer. Making it work for that one customer was easy. Well, quasi-easy. Making it work for the next handful of early adopters meant documentation, input validation, edge cases, etc. That was 10x more effort. Making something we could sell to sophisticated customers, another 10x. The general public … you get the picture. Selling and supporting retail-grade software is a big deal. I think of the horizontal scale as logarithmic with respect to investment.
The map’s purpose is to show motion and obstacles: the inevitable forces of progress move every blob on the map from the left to the right, from from invention to utility.
Let’s zoom in at the the bottom left corner: invention of the invisible. What we had at the time was the ability to make an scientific instrument. Each one was custom-made.
JPL is a research organization, not a production shop. At the time, it could not build just the first, but the only. The management system is set up to build things that the solar system would only want one of. For the imaging spectrometer, we’d need more than one for the planet. The mission architecture itself is a story for another day, but suffice it to say that there are good reasons that the sensors only have so many pixels, so if you want to look the whole planet every week, you need a lot more than just one.
Yoram Bauman, the Stand-Up Economist, has a joke that goes like this: Humans are irrational. I can prove this, because economics teaches us that rational decisions happen at the margin. But nobody goes to the supermarket and says, “I want an orange. I want another orange. I want another orange.” Similarly, we would need to get away from the decision cycle of, “I want a spacecraft. I want another spacecraft. I want another spacecraft.”
This was when we realized that NASA couldn’t be the anchor anymore. To get more than one spectrometer, we would need a commercial partner, with a long-term business plan. A commercial partner would want to create value with the data, not just earn fees on the construction. Maybe we could raise funding for the initial capital expense, but we’d still need to get the data to pay for ongoing sustainment.
The map also helps us identify obstacles. For instance, there’s no line between the scientists and the customers. What is the process, exactly, for turning NASA science into applications? NASA’s mission is research, not operation. That may explain why the Earth Science Directorate had no way to fund a constellation of earth-observing satellites. This tells us that not only do we need a commercial partner, but we need to transfer the technology out of the government entirely.
There are also blocks between the data and the scientists. Back then, it was fairly time-consuming to download and use spectral data, and only a handful of people around the world could anything useful with it. One can sympathize — if you’re an assistant professor, with 3 classes to teach and 2 committees to meet with, is it really worth your while to spend 4 hours a week finding and downloading a spectral data cube, in the hopes that it might contain something useful? We’re also looking for a data platform that makes the spectral imagery more accessible to a wider audience. This will also inform the decision to raise money for a first spacecraft, so customers will have something to chew on while the rest of the constellation is built.
Ignoring that third obstacle for now (if you show someone that their gas pipeline is leaking, how do you get them to do anything about it?), the map gave us about a half-dozen things that would need to change if we were to commercialize the imaging spectrometer. Not easy, but at least it’s straightforward.
To recap, in addition to a spaceworthy spectrometer, telescope, and the platform to take them around the earth (a longer, ITAR-controlled discussion), we need
a team that can make multiple instruments
an iterative design process to make them better/faster/cheaper over time
a production line for multiple spacecraft
a cost-effective way to launch and operate multiple spacecraft
a way to teach customers how to use the data
multiple ways for the data consumers to create value for their customers
That’s only 6 impossible tasks. Easy peasy!
One of Simon Wardley’s repeated maxims is, “Use appropriate methods.” To design a cryogenic imaging spectrometer takes experts in optics, structures, cryocoolers, analog electronics, digital electronics, power systems, heat pipes, thermal simulation, … not to mention a few systems engineers to manage the chaos. To launch and operate a spacecraft takes an entirely different organization, so we went looking for a partner who could take over all the blue-shaded parts of this map. Spoiler alert: it’s Planet1. Not just because they were the most innovative, fastest-moving, largest spacecraft fleet operator focused on societal good, but also the only ones who said, “yes.”
The lines represent flows of information, so it’s convenient to draw the blobs to minimize the number of lines that cross the boundaries. That gives us a chance at having less-messy interfaces.2 We can use the map to further divide up the work between the doing teams, by grouping functions by how we want to manage them. You’ve probably heard arguments over whether Scrum, Lean startup, or Agile is better. “There is no magic one size fits all method for project management”.
As we divide up the work, we also choose how to manage it. Developing algorithms is a research project, whereas preparing a spacecraft for launch should be a regimented (and boring) process. One encourages creativity, the other encourages discipline. So it makes sense to house those functions in different teams, managed in different ways.3 The final solution was to stand up a nonprofit corporation, Carbon Mapper, Inc., which houses a science team to turn spectral data into actionable intelligence, and manage the overall program, while Planet handles the space segment, operations, downlink, and also develops commercially profitable applications for the data.
Value proposition
The remaining obstacle on the map is that even if you show someone that their gas pipeline is leaking, how do you get them to do anything about it? As I pitched the idea of an earth-observing spectrometer constellation to just about anyone who would listen, erstwhile gatekeepers would object, “Why bother? Even if you see something, you won’t get anyone to care. And you have no way of forcing China to take action.”
Contemporaneously, the California Air Resources Board sponsored the California Methane Survey, organized by Riley Duren, Andrew Thorpe, and Ian McCubbin at JPL from 2016-2018, which ended up resolving that block. The survey was an airborne campaign with AVIRIS-NG, an airborne spectrometer to observe the cities, farms, and oilfields of California. It was motivated in part by the 2015 Aliso Canyon blowout — a giant leak in an underground storage facility that effectively doubled LA County’s greenhouse gas emissions. While invisible to you and me, thanks to some computational wizardry, especially by David Thompson and Christian Frankenberg, it was possible to see the signature in shortwave infrared spectra. Mind you, the methane absorption feature is only a few percent of the total light reflected at those wavelengths, so you absolutely need a sophisticated sensor.
The survey identified thousands of natural gas leaks, from oil wells, landfills, generators, refineries, livestock pens, etc. The survey also demonstrated that when the gas company could see a picture of a road with a color-enhanced methane plume coming out of it, the gas company would send a team out the same day to repair it. Likewise for generators, refineries, and landfills. This story would turn out to be critical in overcoming gatekeepers’ objections when it came time to sell the mission, and showed us that CARB would be an essential partner, going forward.
Around the same time, my friend Casey Heeg asked me to look at the European EDGAR data set, which includes global maps of estimated greenhouse gas emissions. Doing what physicists usually do with a shiny new pile of data, I ran some standard statistical tests, and what popped out was this power-law distribution.
Now, the data itself was pretty primitive. It’s just a 0.1º gridded map, and the inputs were fairly suspect, but power laws are a big deal. They don’t happen by accident, and usually mean there’s something meaningful going on. To a physicist, a straight line on a log-log plot is a “eureka!” moment. In fact, chart means that we can address the methane leak problem from space.
Take a look at the right edge. It suggests that there are around 7 places in the world that emit a billion kg of CH4 annually. If you could find and seal all of them, you’d save 10 billion kg of CH4 per year. Is that a lot? Sort of. Depending on who you ask, us humans put on the order of 300 billion tons of CH4 into the atmosphere each year. So these 7 places are responsible for about 3% of the total (if you believe the input data).
The fun of power laws is when you walk up the curve. The product of number of places, and emission rate, is constant over 4 orders of magnitude! The graph shows there are 100 places that produce about 100 million kg/yr. That’s another 3%. Moving down to a mere 10 million kg/yr, there are 1,000 places. That’s yet another 3% of the global total. See The Black Swan by Nassim Taleb for an eloquent explanation of why extreme events happen more often, and more extremely, than most people expect.
That last paragraph was a tad abstract. Management will want better pictures. 5% inspiration, 95% perspiration, and then another 95% bringing it to market.
If a problem is in just one place, it makes sense to go there and measure it. If it’s just over a few cities you have to worry about, it probably makes sense to use airborne sensors, which cost a few $M/year. If it’s distributed all over the world, then it is most cost-effective to build a spacecraft, especially as the cost of space is coming down.
The California Methane Survey emphatically validated the “heavy tail” effect, which had been seen before only in the Permian Basin (because that was the only place that anyone had looked). The point of the Carbon Mapper project is to extend those measurements to the whole world because, however big you think the methane problem is, it’s probably worse. The heavy tail means that cow burps are a distraction, as most of the emissions come from a small number of sites.
This urgent message motivated the State of California into action. Gov. Brown invited the Grantham and High Tide Foundations to get involved. Once we showed how a single satellite could, in 90 days, capture spectra of 90% of the list of the 28,080 sites most likely to be super-emitters, those few visionary donors funding concept studies and long-lead procurements for what would eventually become Carbon Mapper.
Ask for the sale
The only required class for Caltech Physics PhDs used to be Lunch With Robbie (Rochus Vogt, former provost). One time, Robbie told us about the trustee meeting that he was told to attend, where, during lunch, he was seated next to a lady who seemed to not want to be there, either.
She wasn’t interested in any of the biology, medicine, computers, neuroscience, chemistry, or other chatter at the table. Never had been. Came with a dour expression. But then, someone mentioned black holes, and she perked up. “Black holes? I’ve heard of those. What are they, really?”
Ninety minutes later, she and Robbie were still talking about black holes and other weird things that go bump in the universe. As the group dispersed, Robbie recalled that one of the Trustees came up to him and said something like, “The woman you were talking to — she has never been interested in anything we’ve talked about before. If she likes black holes, I bet a lot of other people do, too.”
Robbie was, at the time, trying to raise money for a large segmented telescope to go atop Mauna Kea. The Trustee was W. M. Keck. Robbie said that Keck recognized instantly the allure and popular appeal that astronomy could have. Keck said he was ready to fund the telescope. Robbie, in a characteristic stroke of brilliance and chutzpah, posited, “How about we build two telescopes, at twice the price?”
There are two Keck telescopes.
I remembered that story. When I got into the room with the sponsor of Carbon Mapper, I suggested, “How about we build two satellites?”
There will be two Carbon Mapper spacecraft.
Narrative
The internet always delivers! Jasmine Bina’s Concept Bureau published the Emergent Story Arc framework, or “how to win the brand war (in any industry)”. Her points:
When stories change, so does our reality.
Brands that matter place bets on the future.4
How does that apply to a space mission? What systems were changing, and how do you hitch a narrative to that? Let’s take a look at the available patterns. Applied psychology company Protagonist.io uses AI to discover master narratives — the stories we tell ourselves that affect our behavior.
“Master narratives are historically grounded stories, based on a community’s identity and experience. As a contemporary interpretation of historical facts, they frame the hopes, aspirations, and concerns of a society.”
Example: The American Dream features rugged individuals in a land of opportunity, free from their past or rigid social convention. Stories feature the Pilgrims, pioneers of the Wild West, and modern immigrants.
Protagonist.io further found that, across world societies, a community may subscribe to more than one narrative, but there tend to be ways of grouping them. And there are only three independent variables
Axis 1: Aspiration, grievances, control
Axis 2: Relationships, defining what is “good”, style of political organization, role of church/state/family
Common themes that may span axes: pride, exceptionalism, power, control (autonomy/conformity), anger, frustration, nostalgia, peace, kinship, trust/distrust, love, survival
If you combine master narratives with the emergent story arc, you get a spellbook. The point is that you can ride along on positive narratives, while avoiding cues that would activate opposition
There are 5 basic categories of magic spells — I mean, stories
Change story
Empowerment story
Guilt story
Nostalgia story
Greed/control story
Consider a salient story from 2017:
The empowerment story: Space becomes a personal experience. You can start a space company, do citizen science, or debate Tory Bruno on Twitter. Richard Branson and Elon Musk have spaceships, and they are your personal friends. If Krgystan has a satellite, so should every other country.
This is control + relationships, with a bit of FOMO, evoking kinship and trust. It gives you a warm fuzzy. The phrase “democratizing space” was often seen.
Alex Macdonald explains in The Long Space Age that space-as-a-personal-experience has been around since the early 1800’s, always in tension with the greed story that space belongs to the scientists, so would the mere mortals should stop asking to look through the big telescopes? To avoid opposition, we want to choose a narrative that completely avoids both empowerment and control stories. Nostalgia would be “the 60’s called and they want their space program back” and guilt would be “save the planet, or else.” In Protagonist’s case studies and webinars, we learn that guilt tends to be received negatively, and another project had already claimed it, anyhow.5 That leaves us with a change story.
The change story: new technology makes cutting-edge measurements attainable.
Information is expensive, and getting it to the right place at the right time makes it worth buying. All the colors of light, sensed everywhere. Better information for a better world.
Change stories imply that progress happens when the difficult becomes easy, and Wardley maps are a method for finding where a single innovation can have outsized results. In our case, rockets, satellite buses, downlink, cloud computing, etc. are all becoming commodities, while imaging spectrometers are one of the few things left to make cheaper. What stops the customer from going out and measuring methane plumes? It takes too much labor to get in a truck and drive there. So, we invest in making the knowledge of leaks be a utility service. No sensors to install, no stuff to maintain. Is it worth spending $100m to mitigate 10% or so of the total greenhouse effect? You bet!
There were other options. Try to identify the master narrative variables!
Information revolution: Everything is connected. Pollution as a negative externality. Your business became my business.
Guilt story: Global warming will doom us all. Also, fuel leaks are bad.
Nostalgia story: Prestige coalition of the willing, city on the hill. Build a consortium and invest consciously.
Greed story: Better data to attain an information advantage. Right information + right time = value.
Governor Brown’s announcement of “Our own damn satellite” was a nostalgia story, perhaps intended more for his internal audience of supporters and to reassure donors that they had made the correct decision. His PR people didn’t ask my opinion.
Story
The story we tell affects what we do. Will Carbon Mapper be the Carbon Police? Will refineries try to hide their emissions when it’s overhead? How will regulators and facility owners interact with it?
Did you know that JPL has classes on how to write persuasive proposals? They draw on Pixar’s story structure, which we now apply to the Carbon Mapper narrative.
Once upon a time: Energy creates prosperity, making life better for billions of people.
Every day: In our rush to use energy, a lot of fuel spills, which, ironically, is bad for those people in the long run.
Until one day: We realized that a lot of the fuel leaks come from a very few places, and we know the general area (within 10 km from forecasts and fuel efficiency models).
Because of that: Somebody (California) decided to find exactly where the most fuel is leaking (within 100’).
Because of that: We realized that while any one leak is small, together, these fixable leaks cause a substantial fraction of global warming.
Until finally: The operators of factories ask us to help them find methane leaks.
Pixar stories always have happy endings.
The next big thing
Precursor SPC (Social Purpose Corporation) is a startup company conducting a series of experiments to discover methods for forecasting earthquakes. Yes, I know the USGS is deeply committed to the impossibility of that. But my scientific career started with a job in the machine shop, cutting metal tubes that would later be used to discover the Higgs Boson. In graduate school, I helped build LIGO, the gravitational wave observatory that discovered neutron star and black hole collisions. At JPL, I built instruments to observe extrasolar planets, Bose-Einstein condensates in microgravity, and methane leaks. These observations were all impossible, until they weren’t. Predicting earthquakes is next.
Precursor was started by Friedmann Freund, a scientist at NASA Ames, who started personally funding exploration of different ways we might learn about what’s going on underground. I got interested in Precursor through their work on measuring space weather with ionospheric tomography, as I’m a fan of all Bayesian inference. I invested in the company and have gotten involved as a science advisor.
What are you trying to do? Precursor will instrument Southern California, measuring multiple phenomena associated with electrochemical changes beneath the ground, to identify distinct and predictable (to an AI) patterns that precede earthquakes. It also measures space weather in near-real-time.
How is it done today, and what are the limits of current practice? Companies attempting to predict earthquakes use electric fields and seismicity. The best claim is to observe the buildup of energy that suggests an earthquake in the next 4-6 weeks. Ionosphere maps are made by other companies. These are all model-based, which means they are inherently biased against observing high-resolution details that would occur because of underground stress concentrations.
What's new in your approach and why do you think it will be successful? Precursor uses an unbiased Bayesian algorithm to compute the tomography of the ionosphere in real time, at high resolution. The ionosphere has been observed to react to large-scale electric field changes in several historical earthquakes. Precursor will also measure underground field gradients, atmospheric ions, and magnetic pulses. Any of these on their own are insufficient to forecast the location, time, and severity of an earthquake. Past field tests of these measurements individually, and in subsets, suggest that all the information, taken together, will contain sufficient information to make a prediction.
What difference will it make? A warning of geological stress buildup, suggesting an earthquake in the next 1-2 months, could influence business decisions and prepare, such as staging supplies or making sure backup generators have plenty of fresh fuel. A 2-3 hour advance notice, as the fault line starts to tear, could give infrastructure operators time to prepare and factories to safely park equipment.
What are the risks and the payoffs? It all hangs on quantifying the uncertainty in the prediction. If the false positive and negative rates are validated in an operational system, then public confidence can begin to be established. If enterprises and insurance companies subscribe at global scale, Precursor could become a ubiquitous global utility. If it doesn’t work, or if the uncertainties are too great for public trust, then measuring the space weather for satellite operators will be the fallback plan.
Something cool I found on the internet
Across my portfolio of space, climate, and robotics companies, I’ve found that the #1 best thing you can do as a founder is to send a regular update newsletter. Ideally, with a graph, above the scroll, that goes up and to the right.
NFX, the folks who wrote the Network Effects Bible, have published a guide to writing newsletters: Investor Updates in Tough Times: A Guide for Founders.
Positive Coaching FTW
“See, you do like to win!”, exclaimed one of my coaching mentors, who happens to coach one of the US national teams. As a coach, I’ve always made a conspicuous point about how having fun and going your best was all that mattered. I ask kids to play in positions that challenge them, not necessarily the ones they’re best at, fully well knowing that it could lead to a loss or a tie. In the long run, it should make for stronger players, right?
Turns out, I do also like to win. Well, I’m actually playing a bigger game, and we totally crushed it this time. For the first time in as long as anyone can remember, all 4 of the girls’ and boys’ 10-and-under and 12-and-under teams all won our area All-Star championships, then came in 2nd or 3rd in every division at the LA+Riverside playoffs. Watch out, Manhattan Beach, we’re coming for you.
Anybody can win by recruiting the best players to join your club. Where’s the honor in that? Turning a general population into the best players is something to be proud of.
What changed? I believe it was all the work the other volunteers and I put into changing the culture, so that we have a large, cooperative community of parent volunteers who are committed to positive coaching. And I can tell that positive coaching really works, because I can see the kids on the other teams shoulders slump when their parents and coaches yell at them. Creativity and cooperation outperform.
We have written hundreds of pages of documents to plan what culture we want, and how. Here’s the short version:
make culture a priority
choose a master narrative that reinforces the culture you want
measure everything
make decisions from data
For 2022, we adopted an aspiration/virtue narrative — playing, coaching, or refereeing soccer would make you a better person. We chose the theme, “Be the change you want to see in the world.” The annual onboarding briefings included a review of the league’s DEI policy, along with role-specific tasks. For instance, team managers were charged with identifying the parents who tended to sidle off away from the group, getting to know them, and introducing them to the rest of the spectators. Coaches were briefed on key points of the Keep Girls In Sport best practices for social-emotional learning. Parents were all asked to read The Ride Home Is Not A Teachable Moment.
And the core messages were updated with a change story. Before, we’d use guilt (“your child needs you to …”) or greed (“if you coach, you’ll get …”). Now, those are replaced with messages like “Start your soccer coaching adventure” and “You can do it! Comfort zones are made to expand.”
Following Lenny’s advice, in 2022 started community engagement (using Slack), and monitored product quality with a formal process for monitoring coach and referee reports. There’s more to do for next year, and more volunteers excited to organize it.
Startup scorecard
Recession? What recession? Deep tech investing is going great. There is a weakness, however, in growth capital for Series B/C deep tech companies. Please reach out if you have any ideas.
Space 16 active 3 exits
Robots 4 active
Earth environment information 3 active
Other 3 active 1 exit
If you want in on my dealflow, just ask. I can hype my book all day!
Shameless plug
If you’ve made it this far, here’s my request of you: change the world. Do something meaningful to help people, because you can.
You can hire me to consult on strategy, aerospace engineering, or deep tech investing. Buy books through the Amazon links, and I’ll get a small cut, too. Thanks! More about me at http://shantirao.com.
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If you liked this and want to catch up, check out previous episodes:
Chapter 0: Recap
Chapter 1: Rethink Everything
Chapter 2: Power Laws and Disruption
Chapter 3: Incentives
Chapter 4: Failure
Switching to Planet’s standard typeface, Montserrat, makes every sentence appear smarter.
Sercel’s Law: there’s a tradeoff between duplication of effort and complexity of interface. Simplicity isn’t necessarily better, and neither is efficiency. It’s just a choice to make. Consider, for example, a USB-C port vs a 3-prong outlet. Each has its place.
More details in the slides, based on David Snowden’s Cynefin framework, shows how the various organizations in the supply chain evolve.
Many climate action initiatives that want your time, money, and food scraps don’t seem to have a vision for the future. The contrapositive of Bina’s second point seems to hold: brands that don’t have an opinion about the future, don’t matter.
Guilt narratives go well with zero-sum games. Many climate action projects use guilt narratives to tell a savior story, which can work well for raising money, provided you have a small group of constituents (see the Dictator’s Handbook by Bruce Bueno de Mesquita and Alastair Smith. I believe that we need as many resources as we can get, as soon as possible, so check your ego at the door and let’s grow the pie together.