What I took from this was that automation alone isn’t enough to win. At its core, automation just shifts costs from opex (e.g. labor) to capex (e.g. equipment/robots). What that automation enables (e.g. lower prices, more uptime, etc.) is far more important than the fact that something is, in fact, automated.

As the market gets more infatuated with startups doing “automation”, a nuanced understanding of the technology behind it and what it enables are critical. Is a proposed innovation a true automation breakthrough or something a legacy system integrator could design and implement? What is the measurable payback on NRE (non-recurring engineering cost)? Does the return from incurring that NRE cost allow a manufacturer to (durably) win on price? As a general rule, the more specific the value prop, the more valuable the company equity.

Go see more factories if you can. I think you’ll be surprised at what you’ll learn!

I got this question earlier on a call. It’s a good one. The instinct is to do a lot of diligence. Technical, market, financial. I don’t think this is right.

My advice? Find the five smartest people you know, and try to convince them to join your quest.

In the process of doing that, yes, you’ll be forced to answer the technical, market, and financial questions you face. You’ll also learn whether you have a natural ability to sell.

But most importantly, that group choosing to spend their scarcest resource (time...) with you should give you all the conviction you need to move forward.

Worst case, you’ll have a lot of fun with people you respect.

Best case, you’ll build something generational.

“Nothing is a line. Everything, everywhere is always moving. Forever.”

- Logan Roy, in Succession

To be successful, you have to embrace the dynamism and uncertainty of building and operating. You’re measured on outcomes, not inputs.

Said differently, you have to play to win.

“In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.”

Why is this important? To keep it simple, it comes down to the operating expense of running these assets, specifically depreciation. Let’s use an example:

If a GPU costs $100, with a 5-year useful life and is depreciated to zero, the annual depreciation expense is $20. If I generate $100 of revenue per year with those GPUs against $30 of additional COGS (beyond depreciation), then my gross margin is $50, or 50%.

Now take an example where the GPU has $25 of residual value (e.g. I assume I can sell the GPU to someone for $25 after 5 years of use). In this case, my annual depreciation is $15 and total annual COGS are $45. All else equal, establishing a clear residual value increased my gross margins from 50% to 55%. Multiply this effect across trillions in projected AI infrastructure Capex, and residual value has a very meaningful role to play in future industry profitability.

We can apply the same logic to robotics. Right now, the dominant use case for robotics is in the automotive industry. This mass adoption has established clear and demonstrable residual value (e.g. a used market for 6-DOF robotic arms), which reduces depreciation expense for the asset owners. As a result, we’ve seen the industry drive mass adoption of these systems.

When we look at frontier robotics (humanoids, vertical solutions, etc.), we are still far from the ability to accurately forecast residual value. Do we know how much a farmer will pay for a used weed-killing robot? What about a humanoid that restocks inventory? Because of this lack of agreed-upon residual value, companies must either expense these items in-year, or depreciate them to zero.

For these reasons, simply reducing the Capex required for robotics adoption is insufficient to spur adoption. Because of depreciation, the (non-cash) operating cost of these assets remains high, even when accounting for labor replacement and increased utilization rates. Raising the residual value is likely a much longer lever and a far simpler fix than an industry-wide cost down or performance enhancement exercise. We think there’s a big opportunity for any company that can unblock this!

We lead ~10 Formation-stage investments per fund.

Since 2020, we’ve backed founders building bigger satellites when everyone thought we should go smaller. We’ve backed companies making drugs in space when consensus was that comms was the only business case. And we’ve backed companies commercializing nuclear power before Executive Orders were even a thought.

We’re doing it again with Atlas Motion, which today announced their $11.5M seed, with Greycroft, Sunflower, Ravelin, and angels Jai Malik (AMCA) and Scott Sanders (Forterra) participating.

Atlas convinced us of two things we historically hadn’t been willing to believe:

1. You can generate venture returns building at the sub-system level; and

2. The ability to scale manufacturing is a defensible moat with potential for high returns on capital over time.

The authenticity, credibility, and clarity of thought from Christian, Tom, and Carlo forced us to evolve our thinking. And from what I’ve watched them build and deliver over the past 6 months, I’m glad we did.

They’ve gone from emptying their bank accounts to fund the initial days of Atlas to production capacity of 10,000 motors/mo, scaling to 40,000/mo by year end, representing revenue capacity in the tens of millions, and with real customer contracts already in hand.

They’ve built a world class team of nearly 20 across two continents, with more expansion on the horizon.

Most importantly, they’ve articulated a clear view of the true bottleneck in Physical AI:

“Intelligence is improving monthly. The motion systems are not. Physical intelligence is a manufacturing problem.”

What a fun journey ahead.

Visit them to learn more at https://atlasmotion.com/

This has been 7+ years in the making, dating back to Doug doing (self-funded) power modeling in his garage. It’s a special thing to see something that was a spreadsheet when we first invested in summer 2020 become a real, tangible thing over 6 years later, with the built version matching the 2020 specs nearly perfectly. While the hardware is impressive, it’s what the hardware represents that is most moving. You don’t build hard things without the right blend of mission alignment, seriousness, and fun. Radiant has all of them in spades. Kaleidos is the physical manifestation of a team with complete mission alignment that takes the promises they make seriously, and has a lot of fun along the way.

This idea that physical hardware represents company cultural strength is something that runs through many of our portfolio companies. K2’s massively successful recent Gravitas mission, Varda’s 6th (!!) capsule re-entry, and Northwood’s globally deployed ground sites are just a few examples of physical manifestations of strong underlying company culture. ARR numbers can be faked, but it’s harder to do that with hardware.

If you’re trying to figure out which companies are likely to make it and which won’t, focus on the ones that Build The Thing They Promised. That’s the leading indicator!

Some of these things could be learned in a textbook, but when you see it in person it’s clear that they’re more likely from experience and instinct possessed by someone that’s been obsessed with their craft for decades. They’ve made the mistakes, earned insight from long feedback loops, and have an intuitive sense of when something will just work. This is what makes them an artist.

We see this in the best entrepreneurs. Often they do things that defy logic and physics, but as a result create things that are only possible with enough experience and instinct. It’s this very specific (but intangible) thing that makes what they build defensible. There’s no set of steps someone can take to replicate the output, just a series of judgement calls that compound (positively or negatively) over a long period of time.

Understanding this is one of the main drivers of why the best early stage investors index so heavily on team and track record. What the “film” is about matters less than the unique experience the “director” delivers to viewers. A great movie isn’t made by a great script, or a great lead actor alone. It’s the output of the million small decisions made every day on set.

All that to say…if anyone has a 70mm IMAX Odyssey ticket they’d be willing to spare, please reach out :)

Though companies still make their best attempt to call their contracted revenue ARR, what they really have is backlog. Backlog is revenue that is contracted but hasn’t yet made its way through the production queue into realized revenue. This can be AI implementation service revenue, hardware sales, or government testing data collection, as a few examples. In this world, pipeline is king.

For investors, this new normal also changes how we diligence companies as they grow. Instead of doing SaaS cohort analysis and applying revenue multiples to companies, investors now need to prioritize a more rigorous pipeline analysis. They need to do customer references, analyze contract termination clauses, evaluate cash conversion cycles, and really understand where the marginal revenue will come from when they can’t safely assume that every year there will be a 10% price increase per seat license.

So if we’re headed into a world where backlog is the key metric, what should we look for at each stage of a company’s lifecycle to determine company progress? We look to answer four (often qualitative) questions:

1. Why are customers choosing this company (defensibility, product differentiation, GTM effectiveness)?

2. How big is the pipeline (TAM, “surface area for luck”)?

3. What is the near-term and long-run margin structure for the products and services sold to this pipeline? (e.g. what are the substitutes, how urgent is the need, how unique is this product, etc.)

4. How repeatable is the current sales motion? Has the team distilled GTM down into a core capability or is it scatter shot?

Depending on the category a company is operating in, the answers to these questions vary.

In hardware, product differentiation and defensibility are two of the easiest things to see. Relative to the digital world, building in the physical world is slower and has a more limited universe of alternatives to assess. The risk in hardware land more often tends to be TAM, margin structure, and sales repeatability.

In software today, long-run defensibility is the challenge. Short-run numbers may be great, but avoiding margin erosion and slowing top-line growth feel increasingly difficult to avoid. Without a hardware or network effect moat, unique GTM motions/unfair advantages and extreme talent advantages on the path to economies of scale are more important here than ever.

TLDR; Fun times and lots of opportunity for those willing to forego heuristic-based investing and go back to business valuation basics!

One of the most common are money problems.

There’s two types of money problems:

1. “We don’t have enough money”; and

2. Problems you can throw money at to make go away.

Number 1 is a separate blog post. Today I’m focused on number two. Call them “Type 2” Money Problems. Here’s some examples:

• Your supplier can’t ship for 4 weeks, but your customer needs the product in 3. Solution? Pay 2x to expedite it.

• An engineer you needed yesterday doesn’t want to leave their stock options on the table? Give them a signing bonus to exercise.

• Going to be late to a key meeting? Take an Uber. Unless you’re in NYC, then always just take the subway. It’s faster.

For startups that raise large rounds, it’s almost implied that an increasing proportion of the problems they face are Type 2 Money Problems. The assumption is that speed to scale and market position win, and that money solves that.

The challenge is being sure that’s the case and you’re not papering over another core deficiency of the company. No amount of money can paper over bad culture or leadership. No amount of money will fix hiring if the mission is uninspiring. And no amount of money can will materially accelerate the delivery of truly innovative technical solutions. Only talent and a culture of high iteration speed can do that.

• The largest IPO ever, funding a mission to explore the stars (and minting over 4,000 millionaires…)

• Father-son duos winning NBA championships after 50+ year droughts; and

• The beginning of the World Cup, an event that has historically brought the world together and created moments many citizens cherish forever.

For the romantics, it’s hard to experience these events and not feel something. Feeling something (inspiration, opportunity, potential) is what motivates people to take big risks. And people taking big risks is what creates the potential for change.

Contrast this romantic worldview with the more data-driven and measured view, the “classical.” In “Zen and The Art of Motorcycle Maintenance”, author Robert Pirsig explains the distinction well:

“To a romantic, classic mode often appears dull, awkward, and ugly, like mechanical maintenance itself. Everything is in terms of pieces and parts and components and relationships. Nothing is figured out until it’s run through the computer a dozen times. Everything’s got to be measured and proved. Oppressive. Heavy. Endlessly grey. The death force.”

Both views, romantic and classic, have a role to play in effecting change. But I’m excited that it feels like we’re entering a world where people are beginning to believe ambitious things are possible again.

As unapologetic romantics, we’re always looking for the reasons something might just work, not just the 10,000 reasons it won’t. It’s also a way more fun way to live!

Anyway!

There’s one metric I watch to predict what will happen in venture markets.

It’s not dollars deployed.

It’s not average valuation.

It’s the NUMBER and YoY GROWTH RATE of startups funded in a given year.

Case in point…from ‘95-‘99, number of startups funded nearly 3x’d.

We know how that ended…

Why is this so highly predictive? Because a greater absolute numbers of startups creates an unsustainable dispersion of talent and growth capital that bottlenecks companies getting to large outcomes.

So where are we today? Round count is actually DOWN from ‘24-25. Below even 2019 levels.

Makes you think…🤔

This could be true. But the reality is that any new technology deployment cycle will always oscillate between the euphoria that drives massive amounts of capital behind it and the disappointment when it fails to achieve the massive expectations behind it.

The remedy, however, is not to ban bubbles. In fact, there’s good evidence that bubbles are a feature of the innovation economy. My friend Tobias Huber wrote an entire book on the idea, titled “Boom: Bubbles and the End of Stagnation.” Highly recommend a read!

To quote Tobias’ book preview:

“In a series of case studies tracking some of the most significant breakthroughs of the past 100 years—from the Manhattan Project and the Apollo program to fracking and Bitcoin—they reverse-engineer how transformative progress arises from small groups with a unified vision, vast funding, and surprisingly poor accountability. They conclude that financial bubbles, while often maligned as destructive and destabilizing forces, have in fact been the engine of past breakthroughs and will drive future advances.”

In other words: Bubbles aren’t all bad. :)

A VC saying this is blasphemy, I am aware. But it feels great to get my view out in public.

The last two decades of software made investors lazy. 80% gross margins meant “this is investable.” There was no need to do the harder underwriting of balance sheets, return on capital, and how nascent markets would evolve.

But in this new venture era where investing in hardware is back in vogue and balance sheets are getting bigger, we need to primarily care about two things:

1) Aggregate profit DOLLARS (“Contribution Margin”); and

2) Total capital invested to get there

Profit dollars fuel continued R&D, which deepens the moat and increases enterprise value. And the more profit dollars we generate, the fewer equity dollars we need.

The optimization should always be for the most DURABLE profit dollars generated as QUICKLY as possible with the LEAST about of capital required.

High gross margin (%) is just a proxy for doing that well, but focusing just on that will cause one to miss great opportunities.

1. Building before planning. You ship sub-optimally, then you can’t hire because no one wants to own the mess.

2. Raising while building. You come to market with an MVP and no talent moat. The story doesn’t close/lacks credibility.

3. Building because it’s comfortable. Technical founders are good at it. So they do it. Then they can’t let go and the company stalls.

4. Getting pulled into product minutiae. Small fires feel enormous when you’re the only one fighting them. They don’t when you have a real team around you.

This is why we feel so strongly about founders focusing on hiring early (“First Five”). It’s the difference between a founder running a company and a founder running a product indefinitely.

Instead of building early, hire for the roles that unlock everything else. Do it earlier than feels comfortable.

A quick refresher on how it works: The scout team spends the summer in person with us at our SoHo office, running their own pool of capital like a fund. They have full autonomy over deal sourcing, diligence, pipeline management, portfolio construction, investment committee presentations, and “LP” reporting. All of the carry on the pool goes to the team. Along the way, they also pitch in on a few special projects for the core Also Capital fund and our portfolio companies.

In what’s become kind of a new tradition, we have a scout from the summer prior returning as Managing Partner of this year’s team. We couldn’t be more excited to have Esha back, and to add some new faces to the mix. Without further ado, here are the 2026 partners. As always, reach out to them directly to say hello or chat about what you’re building.

Esha Cyril, Managing Partner

Raised by two founders, her mom in fashion and dad in tech, Esha Cyril grew up on a healthy dose of delusion and the instinct that the hardest problems reward people who think across disciplines. She came to Johns Hopkins as a premed but quickly learned her generalist wiring wasn’t something to narrow to solve the problems she cared about most.

About to graduate with degrees in public health and economics, Esha has spent years researching rare disease drug development, previously authoring and proposing California’s now-passed hepatitis screening bill, and now building an AI tool for n-of-1 trials, a bespoke but slow process usually run by parents of children with rare diseases. Her work alongside founders spans clinical trials at a Series B immuno-oncology company, GTM at an early-stage AI startup tackling substance abuse, and sourcing deals at Soma Capital. Through college, she has mentored over 50 lab spinouts and companies from Parkinson’s therapies to low-wind-speed energy capture. As a scout at Also Capital last summer, she had the chance to learn fast about a lot, from what hiring great early talent looks like to the hands-on work of building conviction, whether on the future of railroads or biotech supply chains. While the sheer learning velocity was great, the people were better, and she’s thrilled to be back investing in outlier founders alongside them.

Outside of this, she believes in always keeping the mind and body moving. She’s a philosophy lover and has recently been working on maternal care and, relatedly, solar-powered water system projects for the mountainous regions of Bangladesh. She has also played racquetball for a decade, representing Team USA with 15 national medals and four international titles, now training for the U.S. Open with her doubles partner (and twin sister).

Rushil Kukreja, Partner

Rushil is an incoming Physics student at Princeton University with a deep passion for space technology, building systems for extreme environments and defense applications. While only in high school, that drive has pushed him to research with an MIT AeroAstro professor to prototype autonomous satellites, launch a payload 400,000 feet into space from NASA Wallops Flight Facility, lead the world’s first HS team building a liquid-fuel rocket, and even work on Lockheed Martin’s THAAD missile defense system. Along the way, he also won a Gold Medal in the USA Astronomy and Astrophysics Olympiad and spoke at the United Nations, drawn by the same desire to understand the universe he’s trying to build for.

His path into startups came through a different kind of extreme environment: disaster zones. After watching how chaotic and slow search-and-rescue could be, he built WiFind, an aerial device that uses Wi-Fi signal analysis to locate survivors trapped under rubble. After winning 1st Place internationally in the Conrad Challenge, WiFind earned $45,000+ in grant funding and landed him at the Founders, Inc. accelerator in San Francisco. Building WiFind under real constraints, with the first prototype hacked together from a salvaged home router, gave him a visceral sense of what it actually takes to push something difficult into existence.

In his free time, Rushil writes poetry, which turns out to be more compatible with physics than most people expect (turns out even Schrödinger wrote poems!). At Also Capital, Rushil hopes to source and back the outlier founders operating in the same territory he’s drawn to: too deep or too early in the frontier for most to even take seriously yet.

Trevor Xing-Xie, Partner

After years of volunteering in his local neurodivergent community, Trevor first ventured into the lab to build neuromodulation devices for locomotor dysfunction. Recruited to write infrastructure software at Google after his first year at Vanderbilt, Trevor now brings seriously intense grit and tact to all affairs; while the nervous system remains a raison d’etre, he’s since extended into affective computing, energy landscapes, intrinsically disordered proteins, and naturally, deep brain stimulation.

A Computer Science and Neuroscience student, he believes computation should celebrate the complexity of being human, using technology to shift our Overton window instead of medicating those who don’t conform.

Trevor has worked with startups across noninvasive cognitive load quantification and emotionally expressive robots, and brings a rigorously technical lens to pricing meaningful ideas. Outside of startups and science, Trevor is a marathon runner, lackluster ex-athlete, and enjoyer of Calvin and Hobbes, The Kekulé Problem, and Adlerian Psychology (courtesy of Mike).

I think the ones that stay relevant consistently do two things: love the process of pursuing greatness, and continuously evolve their edge.

LeBron James, now 41, talks about his love for the process of playing basketball at a high level keeping him relevant.

Jamie Moyer played professional baseball until he was 49 and at the time of his final game had the most wins, losses, and strikeouts of any active MLB pitcher.

In Moyer’s case, staying relevant for so long meant finding an edge any way he could, even when his physical talent had diminished. From the Society for American Baseball Research:

“Moyer’s changeup could make his low-80s fastball look like a high-80s fastball. Besides changing speeds, he kept hitters off balance by constantly moving the ball up, down, in, and out. The crafty lefty also used psychology to his advantage. He often approached the batter’s box in between pitches and talked to the hitter. “As a hitter, that would drive me crazy,” said Boone, “but as a teammate I loved it.”24 The unusual tactic was one example of Moyer getting an edge any way he could.”

The similarities between athletics and entrepreneurship abound. Markets change. Talents evolve. Networks grow and shift. But if you want to stay relevant, one needs to love the process and consistently evolve their edge.

And of course, have fun doing it!

With this approach, the small wins along the way end up becoming the markers of success. You’re helping your friends win! Things like Radiant Nuclear moving into the DOME after nearly a decade of hard work and planning, Harry O’Connor being awarded the Thiel Fellowship, and Grounded shipping Colgate’s Bright Smiles, Bright Futures fleet that increase access to pediatric dentistry services.

Selfishly, this also makes the day to day grind of entrepreneurship and investing more enjoyable. Early stage investing has a long feedback loop, so strictly optimizing for outcomes is a way to either make short term-oriented decisions or burn out.

Choose hard problems. Back good people. Celebrate the wins with them along the way.

A good vision is “A thousand songs in your pocket.”

A vision is NOT “Everyone in the world will have free electricity.”

A good vision is “shooting stars daily that return critical materials that improve life on earth.”

A vision is NOT “a future where we have abundant resources for all.”

A good vision is simple, tangible, and exciting. If a vision is obvious, it’s likely not that visionary. If it’s intangible, it loses meaning.

Founders that set a clear vision well hire better, raise capital more easily, and ultimately find success at a higher rate.

Iterate. Find your message. Then tell it on repeat!

As VisiCalc (and eventually Excel) proliferated, what remained as unfair advantages were relationships, insights, information (collectively, “Intangible Assets”). If you had better information on a business’ growth prospects, your Excel model would prove more accurate. If you had unique relationship, your purchase price in the model might be advantaged.

The same thing is happening now with AI and knowledge work. If we trust the analogy, then we know the volume of knowledge work is about to explode (I recognize the irony as I write a weekly blog, full of words). As a result, insights, information, and relationships are more valuable than ever.

In this new paradigm, it’s a company or investor’s Intangible Assets that will create an unfair advantage—unique information that feeds the tool, the way one uses the tool to move faster, and the relationships and reputation one builds that AI cannot automate away.

• The future will be better than the present;

• Technology will take us there; AND

• The craziest (sounding) ideas tend to be the most impactful.

The challenge on point three is striking the line between optimism and delusion. Is Company X a camera company or a “distributed global intelligence network”? Is Company Y “elevating global consciousness” or are they just a real estate sublessor? I could go on…

The bright line test is the specificity with which a founder can articulate what must go right along the way for them to win. “But what if it works?” is fine. “Here’s the playing field and the calculated risks we’re taking along the way” is much stronger. Hyperfluency on the messy middle of a company tends to distinguish the dreamers from the winners.

Happy hunting!