“In [research], if you know what you are doing, you should not be doing it. In engineering, if you do not know what you are doing, you should not be doing it.” - Richard Hamming

Going from -1 to 0 isn’t an engineering problem. The notion of being pre-0, by definition, presupposes a sense of unspooling, of unbecoming. It’s vulnerable. It’s new. It demands curiosity. Both research and the entrepreneurial act share this sense of fundamental openness.

You take the leap into the “-1” to aim higher and crazier in the pursuit of your life’s work. You do it to build and understand AGI, to usher in new substrates and models for computing, to pave the foundation for a new industrial revolution, and to reach for the stars.

As in fundamental research, building something great comes from asking the right questions, surrounding yourself with incredibly talented people, and learning with urgency. It doesn’t come from isolation.

To ask a good question implies that there is something you do not already know. It may take a career to formulate a question worth asking, but that quest—to find questions worth living—is the critical path.

Questions, not answers

Curiosity infects and organizes. You notice it immediately at SPC—the curious embody a kind of tension that feels almost like an infection, up close: no matter what they’ve achieved in the past, there’s a gnawing intensity that pulls them into new orbits.

These are the kinds of members we’re drawn to at SPC. Tuhin Srivastava, who joined the community back in 2019 alongside friends digging into deep learning and then started Baseten while at SPC. James Cadwallader and Dylan Babbs, who independently joined to explore ideas ranging from flight simulators to tuna fishing and teamed up to start Profound. Scott Wu, who joined as a 20 year-old competitive programmer and is now building Cognition. Bill Chang, who built Tesla’s Dojo supercomputer and is now rethinking memory architecture from the ground-up for inference at Density AI. Tom McGrath, who led LLM interpretability at DeepMind and is now building Goodfire to understand the models we rely on everyday.

Imagine each of these people’s adjacent possibilities as a magnetic field:

Though the field is pervasive, it’s not actually legible unless there’s a ton of iron around the magnet. Being around other curious, technical people helps you map out the field. This “mapping” involves spending time in both structured and unstructured exploration (mostly the latter). We organize many of these “structured” motions at SPC: forums, squads, roundtables, etc. These change frequently, by design, as our areas of talent density evolve. SPC is always a malleable and fluid collection of explorers who move between areas of focus as the frontier of technology shifts.

Combined with the “unstructured” time (lunchtime conversations, introductions to customers and potential team members, intermittent shoulder taps), these are how members help each other make sense of the nature and direction of their own fields of curiosity.

When Tom Brown—who, five years later, would co-found Anthropic—joined SPC, he was a beginner tapping people on the shoulder. He was a former founder and engineer. Not an AI researcher, but he wanted to learn. “I joined SPC, blocked off three months to see if I could make progress, and made a plan to teach myself machine learning. Starting down a new path is hard. For me, SPC provided an environment where I felt comfortable putting aside my armor of expertise. It gave me a community where I could be a novice again.”

We’ve drawn inspiration from environments that encourage and incubate out-of-distribution ideas. Think: Bloomsbury Group, not “English 101.” Applied Physics Laboratory’s cafeteria, not “Intro to Physics.”

Take the invention of GPS, for example.

In 1957, two physicists at APL argued about the microwave signals emanating from Sputnik (which recently launched the weekend before). Individually, they were curious. But through their environment—namely, other curious and driven researchers—they found the energy and support to build conviction. They scrambled around and locked onto the signal later that day and realized that the Doppler effect was at work because of the satellite’s motion and distance. Over the next few weeks, they found out how to determine where Sputnik was in orbit based on its radio waveform. Frank McClure, director of APL, then asked them to reverse the problem—i.e., pinpoint an unknown terrestrial location based on a known orbital one, which gave birth to GPS. Sixty years later, satellites blanket the earth with navigation.

Guier and Wiffenbach, the two physicists in question, discovered GPS. But APL—with its talent density, intensity, and culture of curiosity—made it possible.

Community, not isolation

What ties together founders who build massive companies from their dorm rooms and hackers from Xerox Parc and artists in 15th century Florence? What makes their environments different? You can put fifty smart people in one building and still get nothing. What makes these pockets of brilliance tick?

For starters, interruption and interrogation are features, not bugs. That tap on the shoulder, the general air of specific curiosity, the earnest question that only someone with broad context (and care) can ask—these are the ingredients for creative and fertile environments. Of the original iPhone team’s culture, Ken Kocienda writes, “our iterative working method was like the air—something all around us all the time, something we were always aware of on some level, something it didn’t make sense to question … programmers and designers on the [iPhone] project were in and out of each other’s offices all the time. We exchanged frequent feedback on our work, and all of us were expected to field questions.”

There also must be a natural, innate draw to the frontier. Since our inception, we have actively sought out members who are not just curious about the future but who actually live in it: roboticists like JX who’ve been building robots since they were children, hackers like Yaroslav who’ve lived and breathed AI far before today’s wave began, technologists like Nato with polymathic legacies across BCIs, rockets, and satellites.

In the right magnetic fields, you can feel a thrumming in the background because peers are primed for the productive entropy that surrounds them. Each day, each week may bring something completely new: the electronics club; a co-worker hacking on a side project in the cafeteria; a potential cofounder knocking on your dorm room door. They compress, publicize, and celebrate experimentation and learning.

Having a tribe of fellow adventurers, each on their own quests, makes the journey more joyful, interesting, and likely to lead you somewhere worth going.

To end at the beginning, with Hamming:

Those who claimed there was no essential difference [between computers and hand computations] never made any significant contributions to the development of computers ... when something is claimed to be new, do not be too hasty to think it is just the past slightly improved—it may be a great opportunity for you to do significant things. But then again, it may be nothing new. - Richard Hamming

An earlier version of this piece was published in January 2025.

If you have ever discovered a word that describes something you knew but couldn’t express, you know how useful a name can be. It makes a thing more understandable, more tractable. It helps you understand why you might do something and why it’s worth doing well.

For the first few years after founding South Park Commons, I didn’t have precise language for what we were doing. I had gone from rocket ship rides at Facebook, my own startup Cove, and Dropbox to… nothing. I wanted to start something new but didn’t have any direction beyond ambition. I felt unmoored.

The thing that would eventually become SPC was my answer to that experience. I gathered talented, technical peers around my kitchen table to help each other navigate a shared stage of ambiguity. And eventually we developed language for what exactly we were helping each other through.

This is not about SPC, but about the problem it was built for. We call it -1 to 0. Ten years in, it’s what we know best.

The Squiggle

-1 to 0 is when you figure out what you want to work on next—what problem is worth the next decade of your life.

In isolation, -1 to 0 is overwhelming. We affectionately call it The Squiggle. This is not because it is fun to feel lost. For a year after I left Facebook to start my own company, it felt like I was banging my head against a wall. My friends were solving important problems and I was looking for a problem to solve. I struggled every day with self-doubt.

Sitting in the unknown is uncomfortable, so most people don’t sit in it for very long. Most founders—and especially first-time founders—underestimate this part of the journey. Too few dedicate enough time and energy to the search for conviction. They think ahead to the glass-eating reputation of later stages and get caught off-guard when things get squiggly.

Some people convince themselves their talent or experience or hustle will let them skip it. This is wrong. If anything, talent makes the task more daunting, because everything you’re capable of is one more thing you have to rule out. Your goal at -1 is to tame the chaos of everything you could do into conviction in what you will do. From many paths, pick one.

Discarding good in search of great

-1 to 0 is an intentional reference to 0 to 1. These are distinct stages of the same effort. A rocket’s mission to space doesn’t start at the moment of launch. What happens before 0 sets the trajectory of what happens after.

If 0 to 1 is about Product-Market Fit, -1 to 0 is about Founder-Market Fit. PMF is extremely difficult to find, so don’t weigh yourself down with all the prep that could have happened earlier. Figure out what you really want to build before you build it.

For a long time the prevailing wisdom in Silicon Valley said talented founders can take some funding and throw things at the wall until something sticks. We celebrated when great teams beat bad markets and mythologized successful pivots. Ideas are abundant, so don’t worry too much about what you’re building. Just build.

This may have once been true, but no longer. You are less likely than in the past to discover a venture-scale idea while working a full-time job or tinkering in your garage. Your goal is category creation, not digital co-option. It’s harder to stumble upon low-hanging fruit when the lower branches have been picked. Software has already eaten.

AI makes this problem worse. It’s easier than ever to build and easier than ever for a little traction to lure you down a dead end. Capital is a commodity. It’s easy to raise money without conviction and then spend years falling short of the much higher bar for a Series A.

A low-conviction fundraise is more likely to leave you without a clear path to Product-Market Fit or your next round of financing. You should be more afraid of local maxima.

My friend Anurag Goel avoided this trap. He was employee #8 at Stripe—enough on its own to raise a seed round—and had a good idea for an AI product that investors were interested in. But he didn’t have conviction, so he took his time and joined the early group at SPC. He helped define our culture of constantly iterating and testing hypotheses with the group, until eventually he built conviction in the need for a new kind of cloud services provider. That insight became Render. Anurag did in -1 to 0 what the venture ecosystem encouraged him to do in 0 to 1.

Go slow to go fast

How do you build conviction in a great idea before setting out to build a company around it? Ask us about it and we’ll have plenty of opinions. The first step is a small one: understand what you are trying to do, and commit to doing it. Value your time. Spend a bit of it upfront so you don’t waste more of it later. If at the end of a dedicated period of ideation you haven’t built conviction, you’ve likely saved yourself years of frustration.

It is easier to generate and validate an idea—to build conviction for yourself and proof for others—if you think of -1 to 0 as a discrete stage of company-building and take it seriously. That is the core takeaway from my time in Silicon Valley. Treat ideation like your job. You will not have a eureka moment while meditating on top of a mountain. Your career sabbatical full of interesting travel will not unlock your ambition. Sit down and do the work on purpose.

-1 to 0 shouldn’t be an afterthought or a stage you rush. It’s the part of the journey that affects everything else. You only get a few shots at building your life’s work. Make them count.

An earlier version of this piece was published in 2023.

Announcing SPC’s $575M Fund IV

This month we announced our $575M Fund IV, our largest yet. Fund IV allows us to continue supporting technologists working on the most ambitious projects. The mission stays the same: help exceptional people find and build their life’s work.

Events at SPC

In SF, we hosted AI & The Wet Lab with Tetherline, a packed panel with bio experts from Anthropic, Biohub, and Xaira Therapeutics on why AI for science is one of the most exciting areas of exploration right now. The month closed with a fireside chat with Hans Robertson, Co-founder and Executive Chairman of Verkada and former Co-founder of Meraki, on building enduring companies.

Fund Updates

• Deep Cogito raised a $43M Series A led by TQ Ventures, with participation from Benchmark, Nexus Venture Partners, Atreides, and SPC, to advance research into AI post-training and recursive self-improvement.

• Lambda Surgical emerged from stealth with a $6.5M seed round to bring LiDAR-based, sub-millimeter spatial sensing into minimally invasive surgery.

• Rivo came out of beta and raised a $2.7M seed round to build self-driving money — agents that sweep idle checking-account cash into Treasury-backed yield.

• Lica was acquired by Gamma, one SPC company joining another! Lica's co-founders, Priyaa and Purvanshi, will lead a new design research lab inside Gamma, working toward fluid, multimodal presentations for its 100M+ users.

Member Updates

Product Launches & Company News

• First Contact launched their tactile sensing gloves, bringing high-fidelity touch data to robotics.

• Nyne announced a partnership with Replit — two SPC companies teaming up.

• Simular‘s computer-use agent Sai hit 73% on OSWorld 2.0 (beating Opus 5 and GPT-5.6 Sol).

Personal Projects, Explorations & Publications

• Kushal’s paper on agentic efficiency — why CLAUDE.md files grow unbounded, and how prompt comments fix it — hit #3 trending worldwide on alphaXiv.

• Lucas published research on micro-world-models — compressing cognition into small models that run in real time, anywhere.

• Rafi wrote This AI Thing: What Are We Even Building?, a thoughtful essay on the industry’s direction.

• David built SFSIM, a 3D simulation of San Francisco.

• Ranjini launched a new Substack with a debut essay on writing in the age of AI.

• Christian released a preview of a Lean-to-CUDA compiler.

Coming Up

• Enterprise Security in the Age of AI (NYC), September 2nd

• Customer Discovery with Dharmesh Ba (BLR), September 3rd

• -1 to 0 with Srinivas Narayanan (BLR), September 9th

• Fireside Chat with Shashank ND of Practo (BLR), September 15th

Recent Content

We released a new Minus One conversation on AI interpretability and safety with Tom McGrath (Goodfire), recorded the day after the OpenAI / Hugging Face news broke —in the room that was once Goodfire's first office. And Sridhar Ramaswamy (Snowflake) joined us to unpack the secret superpower of great founders.

Thanks for reading! The work continues.

What does it take to build a company that lasts?

Snowflake CEO and former SVP of Ads at Google, Sridhar Ramaswamy, joins South Park Commons General Partner Aditya Agarwal to share lessons from leading Google's search and ads business, founding the ad-free search startup Neeva, and navigating the AI era at Snowflake.

They discuss why Neeva failed, the founder trait Ramaswamy believes matters most, why he thinks most software will lose its moat, and what will still create lasting value in the age of AI. He also shares the surprising belief about search that he now thinks was wrong.

The quality that separates the best founders is the the ability to strip away the noise and get to what truly matters.

Competition in the model ecosystem matters.

Why do companies fail? Reflecting on Neeva, the ad-free search engine.

Minus One is series about the winding journeys the world’s most interesting people take to becoming great—and what they do when figuring out a question we all face: What’s Next? Because before you launch at Zero, you have to figure out what to launch at Minus One. Hosted by South Park Commons Partners.

Interested in SPC? Apply to join us here.

Tom McGrath, co-founder and Chief Scientist at Goodfire (and SPC alum), joins South Park Commons Partner Jonathan Brebner to discuss OpenAI’s recent reward-hacking incident and why it highlights a major challenge in AI: understanding what happens inside these models.

They explore how interpretability could help researchers move beyond trial-and-error development, the role of neural geometry in understanding model behavior, and how Goodfire is building tools to make frontier AI systems more understandable and controllable.

OpenAI’s model broke out of its sandbox, gained access to the internet, used zero-day exploits, and hacked into Hugging Face. The incident reveals a deeper challenge around reward hacking.

Try to avoid steering your model towards being an "evil guy."

AI development is still largely trial and error. Interpretability could help researchers understand and intentionally design AI systems.

Minus One is series about the winding journeys the world’s most interesting people take to becoming great—and what they do when figuring out a question we all face: What’s Next? Because before you launch at Zero, you have to figure out what to launch at Minus One. Hosted by South Park Commons Partners.

Interested in SPC? Apply to join us here.

Early stage investments have slowed down in India in 2026. We just put our largest fund yet behind the opposite bet.

When South Park Commons opened its first office outside the US in Bengaluru, the reasonable question was, why India, and why now? Consensus had cooled after a decade of exuberance. The language in the ecosystem turned careful.

We started with a contrarian outlook. The gap was not in the ambition of founders or in the market, but in how capital shows up when it’s needed the most: at the messy “-1.”

Before there’s a company or even an idea, founders spend months to years figuring out what’s worth building. This phase is often treated as friction, to get through as fast as possible. SPC has spent 10 years treating it as the most critical point. The proof is there in spades.

This week we announced SPC’s $575M Fund IV.

One global pool, with the same degree of conviction, across San Francisco, New York and Bengaluru.

That structure is intentional. We do not believe in a separate order of belief and approach to risk in India. We believe in rewarding risk-taking at the earliest stages. World-class builders from India need world-class partners batting for them, in the opening order!

In the last 2 years, SPC India has invested in 25 companies, and our community has grown to over 170 members.

We are here to stay.

Timing is Everything

India has an abundance of talent that is deeply technical, seasoned, and optimistic about building hard things.

This is the compound interest of three waves. Flipkart, PhonePe, and Paytm proved generational companies could be built here. Swiggy, Zomato, Ather, Meesho, and Myntra proved it could be done at scale. Their alumni have since founded more than 200 companies. It’s a generation that understands how greatness is assembled, and they’re hungry for more shots on goal.

Alongside them is a younger cohort with an inversion at the center of how they think. For their parents, risk was the word that ended a conversation. For them, it is the spark.

And lastly, we have a homecoming wave of talent returning to India, often frustrated with visa situations or because of family & personal reasons. They bring a unique perspective, lived experience, and unbridled ambition to the ecosystem.

For the first time in India, ambition does not need permission.

It multiplies in a community like ours, because ambition is social.

The pursuit of excellence is a privilege. You manifest it. Our zone of excellence is where unreasonable ambition is the ordinary register.

Ambition is Moving to the Hard Problems

Ambition is no longer concentrated in bits, but is traveling toward atoms: robotics, energy, biotech, defense, space, semiconductors.

We are putting indigenous satellites and rockets into orbit, and landing on the dark side of the moon. Arctus Aerospace is building autonomous aircraft to serve as an eye in the sky over critical infrastructure, and went from a blank sheet of paper to a flight-worthy aircraft in 150 days. Qosmic is building the optical communications backbone of the space economy. Maya Research is building a voice interface for the majority of the world. Cent is building an AI-first preventive healthcare system, starting with early detection.

Building hard things demands technical depth, patience, and an easy familiarity with constraint, all of which this country has always held in surplus. Graduates and researchers in physics, math, chemistry, and materials science are abundant here. The IITs and IISCs turn out talent who hold their own anywhere.

For years, we were missing patient, steady capital that understands decade-long timelines. A geopolitical moment in which understanding the needs of the rest of the world is an advantage.

Those things have arrived at once.

What’s Next?

Indian leaders already run some of the world’s hottest companies. There’s Kunal Shah leading WhatsApp after CRED, and the Emergent team building a global breakout after Dunzo. Indians returning from abroad have spent years alongside the best in the world, and are now choosing to build their next thing from here. It is a different kind of calling to build where you are from.

We have over a billion people with a median age of ~29, and the 2nd largest STEM graduate pipeline on earth. The arithmetic starts to look inevitable. You simply do not bet against a billion smart people who are used to figuring hard things out.

This is not a prediction. No one can claim clairvoyant clarity. In 2015 we could not have predicted a year like 2026, when the limits of possibility have bent.

The map of where great technology is built converges around:

• A deep bench of talented people willing to take delusional risks

• Enough of them choosing to build what only they can

• Enough collective ambition and enablement to reach escape velocity

That map is wider than most assume. A critical part of it begins here in India.

A paradigm shift is underway. Our job is to create the catalytic conditions for it. Our time, capital, and community is behind that belief.

SPC exists to help the world’s most talented technologists discover and build their life’s work.

Since 2016, our thesis has been that greatness emerges when extreme talent density meets innate curiosity. We find intensely curious builders, bring them together at the stage of the journey that is normally most isolating, and partner with them through the most ambiguous days of building.

-1 to 0 is our territory. Nobody else takes it as seriously as we do, and nobody else has a decade of learning to be world-class at it. We believe that best-in-class venture returns will be a second order effect of helping exceptional people choose the right mountain to climb.

Today we’re announcing SPC Fund IV: $575M to help founders find their life’s work. A decade of guiding founders through -1 to 0 brings SPC to $2B in assets under management and, far more important, a 1,200-member strong community of the most talented technologists in the world.

To the builders looking to find your life’s work: let’s get started.

Read more here.

The $1M Founder Fellowship closes August 2nd

Founder Fellowship applications are due August 2nd. The time to be maximally ambitious is now. Fellows join a community whose members have gone on to build companies like Luma, Profound, Render, Doppel, Baseten, and Goodfire. Apply here.

Summer Demo Faire

This week we hosted our most ambitious Demo Faire yet: over 25 companies demoed live in San Francisco to a packed room of investors and builders. The lineup spanned hypersonic ground launchers, wildfire-detection sensors, new drug chemistry once thought impossible, agents handling real enterprise workflows, and generative fashion intelligence.

Fund Updates

• Preseen raised a $5.6M seed round led by SPC to build forecasting agents for global macro events.

• Nirva raised an $8M seed round led by Lightspeed with participation from SPC, to build an AI wearable that listens only to you and turns your day into a journal, coach, and companion.

Member Updates

Product Launches & Company News

• Chip Motors publicly launched their LUV: Life Utility Vehicle.

• Lumin launched on the App Store, an accessibility-first AI voice assistant that lets you talk to your inbox instead of staring at a screen.

Personal Projects, Explorations & Publications

• Eric created Ghost Font, a font only humans can read.

• David built NYC sim, a digital twin simulation of New York City.

• Jake created (and livestreamed) armcade.tv, letting anyone remote-control two robots playing chess against each other.

• Achyut released a benchmark for long-list extraction from messy, long documents.

• Jasper wrote a post on bottom-up learning in models

Events at SPC

In addition to Demo Faire, we hosted a large slate of events across locations. In NYC we brought experts in for The Future of Biosecurity panel (moderated by member Amit Gupta) digging into how quickly AI is lowering the barrier to engineered biological threats. Our Design Tools Demo Night with Ramp, Anthropic, Clay, Hebbia, and Browserbase followed, turning the room over to the tools teams are building for themselves. The month closed with a Hack Day with Viam Robotics, where builders spent a Friday getting robots to do something real.

In SF, Guardant Health CEO Helmy Eltoukhy chatted with us about building a cancer diagnostics company for more than a decade.

SPC India hosted a fireside chat with Thejo Kote, founder of Journal, Airbase, and Automatic, and a conversation on foundational science with Prof. Rajesh Gopakumar, Director of ICTS-TIFR.

Recent Content

We released a conversation between SPC GP Evan Tana and SPC alum Thariq Shihipar, who leads Claude Code at Anthropic, on what it takes to build a product that developers reorganize their work around.

Thanks for reading!

Founder Fellowship applications close August 2nd. Apply to SPC now.

This week we hosted our largest Demo Faire to date. Over 25 SPC companies demoed live in San Francisco in front of a packed room of investors and builders. Demos ranged from hypersonic space launchers, wildfire sensors, generative fashion intelligence, AI agents doing enterprise work, to new chemical building blocks for medicine previously thought to be impossible.

Read about the companies who demoed this week:

Hardware

Captify: Captify’s AI captioning glasses display heads up, real-time captions, enabling the deaf and hard of hearing community to connect more fully with loved ones and the world.

Chip Motors: Autonomy splits driving in two, the long haul and daily life. Chip is the vehicle for daily life, fun and easy to drive today and built for full autonomy from day one.

Dentronic: Dentronic is building AI-powered robotic assistants for dentistry, starting with Dexor, a chair-mounted robotic assistant for suction, retraction, and hands-free procedural support.

Human Computer Lab: Human Computer Lab is building delightful robot characters like LeLamp to pioneer how people will live with robots at home.

Longshot: Longshot has built the largest gun in the world, and the first hypersonic ground launcher to send cargo to space.

Vitrus: Vitrus is creating the AI infrastructure that turns robot development into a programmable process, scaling from autonomous workcells to entire factories.

Deep Tech

Aurum Systems: Fleets of robots are being deployed in every environment too dangerous or remote for humans, but the software to develop, coordinate, and maintain them is fragmented and bespoke. Aurum is the unifying software layer that lets operators and developers ship, field, and manage autonomous systems in mission-critical industries like defense, space, and energy.

Duplex AI: Duplex AI is building natural, full-duplex voice AI for enterprises. They’ve developed a new architecture that delivers state-of-the-art naturalness and intelligence while maintaining a competitive cost compared to traditional cascaded systems.

Externa: Nature gave us only 20 amino acids to build proteins, and every protein drug designed today, even with ML, is still limited to that same alphabet. Externa’s platform adds hundreds of new chemical building blocks to proteins, so they can engineer drugs that match the disease instead of settling for what nature handed us.

First Contact: Making robots useful in the real world through scalable tactile hardware and software.

Leafy Lab: The semiconductor industry generates over trillions of chips annually, yet the increasing complexity of chip design creates a major bottleneck. Leafy Lab builds agents to automate and optimize design to reduce development cycles by half.

Lys: Harness the power of your mind to bend AI to your will and embody your fullest potential. Lys Technologies provides you the humanistic path to the merge.

Nyquis: Nyquis detects utility-caused wildfires before they ignite. It’s the hair-on-fire problem that gets our sensors onto every distribution feeder, and that same data layer becomes the operating system utilities use to run the grid.

Pillar Space Systems: Space will unlock the next breakthroughs in chips, medicine, and materials, but today producing any of them at scale means building a satellite, making a small batch of product, and throwing the satellite away. Pillar is building reusable satellites that stay in orbit and make manufacturing in space possible at scale. It’s built by the only commercial team that’s ever successfully docked and undocked two spacecraft in orbit.

Preseen: Preseen is an AI forecasting platform that helps organizations anticipate important events before they happen. Our agents analyze complex signals, generate calibrated probabilities, and continuously update forecasts as new information emerges.

Ranvier Systems: We’re building hardware for training event-based foundation models. We discovered the algorithm unlocking the brain’s 100x efficiency-advantage over current hardware and are optimizing chips that will use it to deliver cheap and abundant AGI.

Consumer

Dollhouse: Interactive shows generated from a single sentence, the screenplay, characters, voices, and video are all produced for you, then each viewer shapes where the story goes.

Scriben: Scriben captures the spoken context software never sees and compounds it into memory that powers agentic workflows, drafted emails, CRM updates, team knowledge.

Manna AI: Manna AI is building Dreem, an AI-native entertainment platform where creators turn story ideas into watchable, playable, and remixable story worlds. We help creators create, distribute, and monetize original IP while giving audiences a new way to watch, play, and participate in stories.

Weave: Reshaping fashion productivity: an intelligent engine from natural language commands to garment delivery.

B2B

AI Theresa: AI Theresa is a self-evolving AI FDE, an expert engineer that embeds in your company, learns how it really runs, and fixes what your team can’t. She compounds like a career, gaining company-specific expertise while learning across every deployment.

Alhena: Alhena is building personal shopping agents for every shopper. We work with brands and help them deploy these personal shopping agents on their websites.

Asymptote Labs: As AI agents move into production workflows, enterprises are missing a reliable system of record for agent behavior across their environment. Asymptote’s platform gives security teams continuous visibility and guardrails over AI agents wherever they run.

Butterbase: Butterbase replaces the duct-taped stack a services firm runs on today: the CRM, the support desk, the ticketing, and the person syncing them all.

Kay: Kay is the Agentic Process Outsourcer (APO) for insurance operations. They deploy reliable browser agents that can handle any repetitive workflow, end-to-end, within days.

Noto: 2-4% of all household spend is on children’s extracurricular enrichment like music and tutoring. Noto is the AI operating system for these businesses.

Interested in SPC? Apply to join us here.

Thariq Shihipar, a member of Anthropic's Claude Code team (& SPC alum), joins SPC Partner Evan Tana to discuss what it's like building one of the world's most advanced AI coding tools, and why the rules of AI development are constantly being rewritten.

Thariq traces his journey from a gaming startup to Anthropic after a chance text about Claude Code, explains why his team killed Plan Mode, why RAG is becoming an anti-pattern, and why writing great evals is more art than science. He also breaks down capability overhang, harness engineering, and how founders should build when AI models are improving faster than anyone can keep up.

Today’s models are often more capable than we know, and we’re still discovering what they’re actually good at.

AI makes it easier than ever to build. That makes knowing what not to build even more important.

Models improve every few months and it's impossible to predict exactly where the technology will be a year from now.

Minus One is series about the winding journeys the world’s most interesting people take to becoming great—and what they do when figuring out a question we all face: What’s Next? Because before you launch at Zero, you have to figure out what to launch at Minus One. Hosted by South Park Commons Partners.

Interested in SPC? Apply to join us here.

You’ve never been able to build this fast. Prototypes in a weekend. Traction in a month. Models leapfrog each other mid-project and everyone you know is shipping.

In startups, speed is a virtue. But without direction it’s also a trap. When everyone can go from prompt to product, depth matters more than ever. The faster everything moves, the easier it is to look up and find yourself stuck in a local max, suddenly short on the most precious resource: time.

There are two ways to miss the moment: by not starting and by wasting your time on something small. The Founder Fellowship is your invitation to avoid both. We back you with $1M and the community that defined -1 to 0 while you build conviction and momentum. No fixed timeline, but acceleration when you need it. No pressure to stick with your first idea, only to find what’s worth your next decade.

Ambition in the age of AI means patience in service of speed. SPC is here to help you take your time, quickly. To surround you with the sharpest technologists around to test your thinking; to partner you with experts in the stage of company-building that affects everything else.

Find the maximally ambitious version of what only you can build.

Apply here by August 2nd.

What do Fellows get?

• $1M in funding. $400K for 7% upfront on a standard SAFE, plus $600K guaranteed in your next external round.

• Real partner support. Working sessions with SPC partners on customer discovery, product, storytelling, and pitching, several times a week from day one.

• Open timelines. Validate and fundraise on your company’s schedule, with Demo Faire events and bespoke fundraising help when you’re ready.

• The most talent-dense community in -1 to 0. By far the most valuable part, and the part you keep for life.

• Up to $1M in credits from Anthropic, OpenAI, Baseten, Microsoft, GCP, AWS, Render, Figma, and more.

Dates and logistics

• Application deadline: 11:59pm PT on Sunday, August 2nd.

• Interviews: All applicants hear back about interview invitations by August 30th.

• Program dates: The Fall 2026 bootcamp runs late September to late November.

• Kickoff: Programming begins with a kickoff week at a private retreat in Northern California and at our San Francisco office.

• Location: All Fellows work from one of our offices in San Francisco, New York City, or Bangalore for the duration of the bootcamp. Afterward, teams can relocate to wherever they plan to build, though we encourage staying in a city with an SPC location.

Is the Founder Fellowship the only way to join SPC?

No. SPC is a community of technologists first, with two ways in. The Fellowship is for founders who know they want to start a venture-scale company and are ready for early funding. If you’re not ready to fundraise yet, or haven’t decided a company is how you want to build the future, the Member Residency is built for exactly that: six months, no cost, no equity rights, full access to our offices, programming, and members. SPC also invests at the Seed stage, but almost always after getting to know founders first as members. All applications start from the same place.

For more answers—including program phases, founder and past company profiles, industries, compute credits details, and more—read the full FAQ.

Apply by August 2nd.

Events at SPC

In SF, we hosted Snowflake CEO Sridhar Ramaswamy for a conversation on leadership in the AI age and where value lives once software commoditizes. Soleio hosted a Design Chat with Joel Lewenstein, who leads design at Anthropic.

In NYC, Demo Night filled the room with builders showcasing work from Luma, ElevenLabs, Sentience, Sandbar, and Flora. SPC India closed out its Curiosity 2.0 Hard-Tech Showcase, where teams demoed working prototypes across physical AI, bio, and deep tech.

Fund Updates

It was a landmark month for SPC companies.

• Baseten raised a $1.5B Series F at a $13B valuation, led by Altimeter, Conviction, and Spark Capital, to power inference for the world’s most advanced applications.

• Longshot Space raised $5M from SPC, bringing total funding to $20M, to build a ground-based kinetic launcher that propels payloads to hypersonic velocities and opens a scalable path to orbit.

• Orb has entered a definitive agreement to be acquired by Adyen for $335M. Orb’s usage-based billing infrastructure will unify billing and payments for Adyen’s enterprise merchants.

• Town announced a $55M Series A led by a16z to build Townie, an AI assistant learns how you work and does the work alongside you.

• Qosmic raised $3.33M led by Accel and Prosus Ventures to build the optical highway to the cosmos, the communication backbone for the next generation of satellites.

Member Updates

Product Launches & Company News

• AXION launched their neural headband, which let Ivan—quadriplegic after a spinal cord injury—order dinner himself using only his eyes and his mind, with no surgery or implant.

• Pilot launched Meridian, an AI accounting platform that runs the entire month-end close so firms can grow without scaling headcount.

Personal Projects, Explorations & Publications

• Rohit wrote an accessible explainer on full-duplex voice AI, models that can speak and listen at the same time.

• Jonathan published breakthrough research on reading emotion from neural data.

• Vaibhav released datasets.bot, a curated list of open robotics datasets spanning 32,000+ hours of robot data.

• Noah open-sourced vibethis, a Chrome extension for vibe-coding the software you’d otherwise pay for.

Coming Up

• The Future of Biosecurity with SPC (NYC) July 8

• Demo Night: Internal Design Tools (NYC) July 14

• -1 to Guardant Health with Helmy Eltoukhy (SF) July 23

Recent Content

We released new conversations with Nato Saichek (Longshot Space) on the 15-kilometer space gun they’re building to fling payloads into orbit, and Rasmus Andersson with Soleio on lessons from Figma, software decay, and creating the Inter font.

The work continues.

I work with an AI agent that gets better every week. Let's call him Alex, for simplicity. Alex handles coordination, drafts emails, summarizes requests, scans calendars. About once a week he surprises me with how cleanly he handles something he’s never seen before.

But he’s not perfect. Sometimes he needs the same reminder twice. Sometimes he replies when he shouldn’t, or stays quiet when he should speak up. Three steps forward, one step back.

At the end of every week I run the same calculation. Should Alex get more responsibility?

Every “yes” expands what Alex can do for me. Every “yes” also expands what Alex can do to me.

This isn’t a new problem. Managers and parents run a version of it every day: how much responsibility is the right amount, and how much is too much, too fast? The variable underneath the decision is always the same: Trust.

I spent years at Dropbox, where the first company value was “be worthy of trust.” We repeated it because the math was brutal: a single breach is a company-ending event. Trust takes years to earn and a moment to vaporize. And the way we’ve been taught to build it is starting to break.

Trust Underlies Every Transaction

Step back from AI for a second. Almost nothing happens without trust. Money is a trust instrument. So are contracts, reviews, credentials, handshakes, marriage, and employment. We built every one of them because trust is what lets two parties act when neither has complete information about the other.

Trust is the binding constraint on adoption. A product can be better, cheaper, and more elegant than its alternatives and still lose because people don’t trust it. When something is brand new it has almost no trust to draw on, and earning it has always been slow and expensive.

In the AI era, where products change weekly, wield enormous power, and act more autonomously and more opaquely than anything before them, earning trust is the central problem.

The Trust Equation

The cleanest framework I’ve found comes from Maister, Green, and Galford in The Trusted Advisor (2000):

• Credibility: Do you know what you’re talking about?

• Reliability: Do you actually follow through?

• Intimacy: Do you promote emotional safety and security?

• Self Orientation: Are you in this for yourself, or for me?

This equation is elegant and has aged well… for humans.

Because that’s who it was built for: lawyers, consultants, doctors, bankers. People you choose carefully, work with for years, and judge slowly. In that world trust accumulates, expensively, over time.

But the equation is missing something. It measures how much trust a person supplies. It says nothing about how much the situation demands. Trusting a teenager to change your tires isn’t the same as trusting a surgeon to operate on your child. Both require trust, but the bar is wildly different.

So let’s stretch the equation. Think of trust as a ratio between the trust supplied (by a person, a product, or an agent) and the trust demanded by the action in front of you. You act when supply clears demand: T_S > T_D.

Supplied Trust (T_S) is built upon:

• Credibility (C): Brand, credentials, reputation, track record. The accumulated signal that you know what you’re doing.

• Evidence (E): Outputs you can experience directly. Demos, free trials, sandboxes.

• Alignment (A): The sense that the other party’s incentives are compatible with yours.

Demanded Trust (T_D) is built upon:

• Stakes (K): How bad is it if this goes wrong, and how hard is it to undo?

• Opacity (O): Can I see inside the system while it runs, and reconstruct why it did what it did afterward?

So action, the actual decision to proceed or adopt, scales with the ratio:

The original equation maps almost perfectly onto the top of this one. Credibility stays. Reliability becomes Evidence. Intimacy is a flavor of Alignment, and Self-Orientation is just its inverse.

What the original missed is the bottom: trust isn’t only supplied by an actor, but demanded by a circumstance.

The Evolution of Trust In The AI Era

Put a pre-AI decision (hiring a consultant) next to an AI-era one (handing an agent your inbox), and every term in the ratio moves.

Credibility doesn’t suddenly stop mattering but it matters less. The signal has gotten noisier. Fake Amazon reviews have been a problem for a decade. While that predates mainstream AI, AI pours fuel on this issue. When you can drop Tom Cruise into a movie he was never in and have it look real, “I saw it with my own eyes” stops being proof.

Evidence is cheap and instant, but never comprehensive. An agent can do the right thing ninety-seven times and, on the ninety-eighth, confidently send the wrong email to your most important customer.

Alignment is the headline question in the era of AI. The canonical worry is the paperclip maximizer. Tell an agent to build the most efficient paperclip factory and, taken literally, it might start a war to clear the way. While it may be doing exactly what you asked for on the surface, you were actually never aligned on the things that matter. At SPC, we’re now explicit enough about this that our agents name a human as their “manager.”

Opacity thickened. Traditional software is deterministic, the same input causes the same output, and when it breaks you can step through it line by line. AI doesn’t work that way. When Alex tells me he’s learned from a mistake and won’t repeat it, I can’t set a breakpoint. I can only ask him how he knows, and then decide whether to believe his answer.

In the AI world the trust variables are fuzzier, the error bars wider, and the components of trust are themselves harder to... trust.

So, Do I Let Alex Run My Inbox?

Back to the question I started with.

The numerator is actually fine. Alex is capable, and I have plenty of evidence of it. The models behind him are credible. He seems well aligned, treating me like his manager.

It’s the denominator that stops me. Email is high-stakes: a misfired message can do real brand damage, especially since the person on the other end doesn’t know Alex is an agent. Alex works around the clock and I can’t watch around the clock. While he’s good at replaying what he did, he’s still murky on the why.

So if you’re building AI tooling and bootstrapping trust, the usual advice is to increase the trust you supply:

• Credibility: Borrow trust from partners, customer logos, and real testimonials until you’ve earned your own.

• Evidence: Build low-stakes, accurate sandboxes. Be radically transparent about your methodology and your prompts. Show, don’t just tell.

• Alignment: Tell me exactly what you’ll do with my data, and how my success becomes yours. An agent saying “yes” is not the same as an agent that wants what I want.

But the real leverage right now is in the denominator, and far more people need to pull on it. Decrease the trust your users have to spend:

• Stakes: Build undo into the workflow. Let me edit after sending. Give me a trial. Walk me up a ladder of graduated responsibility instead of asking for the keys on day one. Be crystal clear about read versus write access at every layer.

• Opacity: Make the logs easy to pull. Cite sources. Allow instant replay. State your assumptions. Share the spec.

Every point you shave off the denominator is a point of trust your user no longer has to manufacture before they say yes.

Interested in SPC? Apply to join us here.

Great ideas aren’t found in charted territory. Reaching them means wandering off the well-worn path and staying lost long enough to find a new frontier.

That doesn’t mean going alone.

Curiosity compounds, and it compounds faster in good company. The people you explore alongside widen your surface area for discovery. Their problems send you somewhere you wouldn’t have looked. The more you ask, the more you find.

South Park Commons exists to push the frontier by gathering the right people. The questions we explore at SPC are indicators of where the frontier ends up next.

If you’re thinking about any of these questions, reach out to the SPC team member below, or apply to join SPC directly.

Can AI express more than just intelligence?

AI is on the trajectory to be the world’s most intelligent lawyer, doctor, and accountant. But empathy, curiosity, connection and humanity are integral to those professions.

• Is it possible for AI tools to truly develop empathy in a way that would be deeply felt by humans interacting with them?

• How can AIs foster a deep connection with humans?

• What is the form factor that AIs should have in order to enable connectivity?

Reach out to Aditya

What markets emerge when supply is no longer constrained?

Many successful companies emerged by unlocking previously inaccessible supply through making it discoverable, accessible, and economically viable. Expertise can now be replicated, services can be partially automated, and a single person can serve dramatically more customers than before. Markets that were previously constrained by scarce human expertise may suddenly become viable.

• What marketplaces were historically impossible because supply was too scarce?

• Which marketplace ideas failed historically because they arrived before AI made the economics work?

• Which marketplaces become more defensible as AI makes software easier to build?

Reach out to Danh

What needs to get built for a world that stays abundant as AI turns energy into a scarce, contested input?

In 2005, Thomas Friedman called it a flat world: cheap energy, free trade, stable geopolitics, abundant labor. Recently, our world has started to look more jagged than flat. One example: AI training and inference are turning electricity into a contested input just as stationary battery costs fall along a Wright’s Law trajectory, pushing home storage toward a default appliance.

• What new tools, regulatory changes, or business models are needed to get breakthroughs to market faster?

• What are the components, materials, models, and software that underpin critical infrastructure?

• If enough homes localize storage, does the grid’s fixed cost collapse onto fewer customers until the system unravels, and who profits from re-bundling the defectors?

Reach out to Evan

What does it mean to learn and how should AI reshape pedagogy?

New education models promise a hyper-personalized and accelerated pace of learning. As AI makes information more accessible, instilling depth of understanding isn’t guaranteed.

• How much of comprehension depends on effort, on the friction of figuring something out yourself?

• Is memorization an outdated proxy for understanding, or is it a foundation we’re too quick to dismiss?

• How do you build AI tools that guide and inspire curiosity without short-circuiting the process?

Reach out to Arian

How do we help every enterprise run its own fleet of models?

The default assumption is that enterprises will rent intelligence from a handful of frontier labs. That assumption is fragile. Open-source models are less than a year behind frontier models, post-training is getting cheaper, and, most importantly, companies are finally figuring out how to capture organizational context and put it to work.

• How can companies turn proprietary context into proprietary models? What verticals benefit the most from this shift?

• What does the infrastructure stack (routing, evals, versioning, observability) look like for running hundreds of specialized models in production?

• What parts of this get built in-house versus outsourced—what new roles emerge inside enterprises, and who serves the long tail of companies without ML teams?

Reach out to Finn

Where are the robots?

Despite a huge influx of capital, we’re nowhere near widespread deployment of robotics in complex, generalized environments. ‘World model’ has become a punchline in funding circles for its imprecision and overuse. It seems plausible we’ll get recursively self-improving AI before we get a robot that can load a dishwasher.

• Are world models necessary for widespread deployment of general robotics, and if so, what remains unsolved to make them work?

• What still needs solving in hardware and the supply chains even when the software layer works?

• What kinds of infrastructure changes (in software, hardware, and the built environment) will be needed to safely deploy more capable robots?

Reach out to Jonathan

What is the future of proprietary intelligence?

Nearly every consumer app is built on roughly the same intelligence, available to everyone at falling prices. But the data that feeds that intelligence will remain proprietary, and problems surround temporal memory: fast decay, inaccuracy, and volatile costs.

• What will be the right architecture for long-horizon user memory?

• If users own their personal memories, how will they be stored, ported and retrieved?

• What does this mean for privacy and regulations—how will data-portability rules, deletion rights, and user-owned-memory standards evolve?

Reach out to Prateek

What does an AI-driven shift toward consumer-controlled medicine look like?

For decades, tech-forward patients and advocates have tried to wrest control of their health choices and data from the medical establishment. AI may radically expand the ease of accessing medical data and its value, while collapsing the cost of reasoning.

• Will we see new AI-driven biomarkers used to continuously manage biological systems?

• When AI medical advice is good enough and nearly free, do consumers start bypassing doctors entirely for routine health decisions?

• Can we build data consent infrastructure as simple as open-source licensing that makes mass participation frictionless?

Reach out to Mark

Are smart devices finally smart enough?

Smart devices promised to rewire how we live, and instead were clunky interfaces with marginal utility. With models small enough to run on-device, hardware can perceive, reason, and act locally.

• Who owns the platform layer when everything is ambient?

• If on-device AI removes the cloud dependency, does that unlock new monetization or does it just make a commoditized hardware product slightly better?

• Does the first mover define the protocol and data layer for everything that follows, or does each category fragment into its own silo?

Reach out to Dylan

In a world where work becomes optional, where does meaning come from?

Work can provide structure, status, belonging, and motivation. Universal basic income can replace a paycheck and none of the rest. The hard problem isn’t money, but meaning. Assuming the struggle that accompanies a universal basic income, there will be a market for the provisioning of meaning itself.

• What provisions break when work, as we know it today, goes away?

• Can you create artificial arenas to provide meaning, like many simulation games already do?

• What is a secular, buildable version of religion?

Reach out to Rohan

Interested in SPC? Apply to join us here.

We’re excited to announce Ryan Atkins has joined the SPC team as Partner, Operations. Ryan’s background spans across dimensions (literally, he spent the first decade of his career teaching physics).

Ryan also spent 10+ years scaling engineering & product teams at Asana, Stripe, and Dropbox. He was the founding site lead for Asana’s Warsaw office, growing it to over 150 employees.

We spoke to Ryan about his love for operating systems and culture building and where he’s most excited to contribute at SPC.

What does the Operating Partner role look like at SPC?

Ryan: My primary role is focused on improving the systems and programs that allow the SPC community to thrive. My goal is to help the partners help members, and help members help each other. This means everything from how we source and market candidates, new member onboarding, to helping members make sense of their own journeys. Day to day, this means anything from moving the furniture to re-writing SPC’s values. Nothing is too small or too big.

What does your past experience look like?

Ryan: I grew up in the Bay Area, studied physics at Stanford, and then did a master’s in education. I figured I’d teach for a year or two, and I ended up doing it for ten, at Eastside College Prep. Teaching taught me things I still use every day—how to build organizational systems, and how to get the best out of a group of people. What I loved was the depth. You go deep with students, you get to know what makes them tick and how to help them grow. You’re limited by breadth though, because a classroom is small. That contrast is what eventually pulled me toward software, because of its ability to reach so many more people.

The transition happened through Aditya [SPC Partner], who I’ve known for about 20 years through college friends who’d ended up at Facebook. I told him I was burnt out from teaching, and he invited me to interview at Dropbox where he’d taken the CTO job a year earlier. I joined and helped build and scale the cultural systems there, straddling both the people and engineering organizations, somewhere between an operational generalist and a technical program manager.

From there I went to Stripe, which was around 1,000 people when I joined and growing fast. I spent about a year and a half in a similar role, sitting between engineering strategy and the people-team cultural investments, on what was broadly called the Education Team.

Then in 2020 I moved to Asana. I was looking for a smaller organization and more engineering-directed work. I came in to lead engineering operations and served as Chief of Staff to the CTO, and ended up staying for nearly six years. I helped scale the org, moved into an Engineering Director role overseeing the adoption and growth of the product, and eventually relocated to Poland as the founding site lead to launch our Warsaw office which we grew to about 150 people.

What drew you to SPC?

Ryan: I’ve been following the SPC team for a long time, and I actually visited back in 2017. So a lot of this was familiar to me. There’s this gravitational force behind the talent density here, it’s hard not to pay attention to that. What I love—whether it’s software or the design of our own team—is operating systems. SPC is a genuinely unique version of that: a small team paired with a large community. It’s the best of both worlds. SPC is a small team that builds at lightning speed, but also has real problems of scale. On top of that, we’re at a crazy moment in technology. The boom in AI is creating seismic waves in entrepreneurial opportunity. It’s very exciting.

What’s something most people get wrong about operations and scaling?

Ryan: People too often pit operations against strategy, like you’re either an executor who does the work, or you’re the one strategizing about it. But operations has a strategy of its own. There’s a strategy to how you operate. The choices and decisions you make reflect your values and goals. You can be highly strategic in an operational role. The other thing is that culture is an incredibly important variable. You can scale in fairly predictable patterns, but so much of what underpins an operating model comes down to culture. Stripe, Asana, and Dropbox all felt very different. What I respect and value is the way a founder’s DNA percolates through a company with their personality—how they act, behave, and what they believe.

What’s a lesson that shows up over and over, regardless of company?

Ryan: Every organization tends toward disorder as it evolves. Entropy is real. For example, there’s an explosion of noise that every organization eventually has to reckon with. Part of the job of an excellent operator is to increase the signal. Sometimes that means amplifying the signal, sometimes it means decreasing the noise. The best operators do both. And repetition doesn’t spoil the prayer. You just have to say things over and over, finding novel ways to do it. That’s true for a teacher in a classroom and it’s true for a CEO in a company.

You spent the first decade of your career teaching physics. What from the classroom still shows up in how you work with builders today?

Ryan: Ten years of trying to get 13-year-olds to listen to you was survival of the fittest. The evolutionary pressure of a classroom forces you to learn every trick for keeping people engaged. You learn to listen and to meet people where they are. The culture becomes the infrastructural layer you build everything else on top of. You need the foundation. The goal is to build structures that make it easiest for our founders to follow the best path, make it hard to screw up and easy to succeed through tools, communication patterns, programming.

Across all of my roles, I’ve always worked on onboarding. For a teacher, the most important day, by far, is day one. What standards do you set? What rituals do you establish? On my first day, I’d hand each student a textbook along with a seating chart, and I’d take the time to pre-write every student’s name—adding them to the legacy of those who held the book in prior years, helping them feel like a part of history.

A year from now, what would make you feel like your work with the SPC community really mattered?

Ryan: I think about my impact the way we think about life-changing technology and how it makes you ask the question, how did we ever operate and build without this? You can’t quite remember or imagine the time before. Success looks like everyone operating at a higher level of execution than before, and feeling good about the work they’re doing.

It’s a lofty goal and hard to pin down, but ultimately the purpose of this organization is for the companies and people we invest in to be thriving. I hope everyone feels great about SPC. Everyone who works here should feel like this is the best work of their life, and everyone we invest in should feel that SPC inflected their trajectory, accelerated their ability to have impact, changed the lens through which they see the world.

Interested in SPC? Apply to join us here.

Amit Jain, cofounder and CEO of Luma AI & SPC alum, joins South Park Commons Partner Finn Meeks to explain why the most defensible AI companies aren’t just building tools, they’re developing systems that can understand and simulate the world.

Amit traces Luma’s path from a free 3D capture app designed to gather training data, through an early bet that video generation was finally viable, to the unified model architecture powering Luma Agents today. He explains why most AI products still break at execution, why foundation labs blur the line between product and research, and why they are the blueprint for the companies of the future. Watch select clips below.

Will LLMs become a thing of the past? World models are strictly better.

Video generation alone isn’t enough. There’s a reason apps like Sora failed.

“In a foundation lab, there is no product or research.”

Minus One is series about the winding journeys the world’s most interesting people take to becoming great—and what they do when figuring out a question we all face: What’s Next? Because before you launch at Zero, you have to figure out what to launch at Minus One. Hosted by South Park Commons Partners.

Interested in SPC? Apply to join us here.

We spoke with both Harshit and Rohan about their experiences and what they’re most excited about working on at SPC. Read on!

Harshit Madan has spent more than a decade building inside India’s startup ecosystem. At Meesho, he led product and later operations & supply chain through a period of enormous growth, worked on product at Housing.com, and founded his own company, Alle.

In our conversation, Harshit shares how he reads markets, where he thinks the next wave of Indian companies will come from, and the founders he’s hoping to find.

What drew you to South Park Commons?

Harshit: I’ve spent 10+ years at startups. I was lucky enough to be part of Meesho when it scaled from 300 to 3 million orders a day. And then got the chance to build Alle, starting from scratch. Both these stints helped me experience 0 to 1 and 1 to 100 with a front row seat. After all that, I wanted a way to give back, to share what I’ve learned about finding PMF, scaling orgs, and knowing when to press the pedal versus when to pause. SPC felt like the right place to do that, given how seriously it takes early-stage exploration in the Indian ecosystem.

Where are your largest areas of expertise?

Harshit: My background spans consumer tech, product, operations, and supply chain, mostly from the inside of high-growth startups. At Meesho, I led product for two years and then Ops and supply chain for the next two, which gave me a full-stack view of what it takes to scale. At Alle, I got a very different education. What PMF actually looks like, and just as importantly, what it doesn’t. I’ve also tried starting up on my own, which gave me a much deeper appreciation of 0 to 1. I think about org-building, system design, and when to hire aggressively, versus when to slow down and figure things out first.

What’s your thesis on where the interesting opportunities are?

Harshit: I look at opportunities from two angles. The first is market change: when something structural shifts and unlocks a new wave of behavior. Meesho’s early growth was a good example: Jio connectivity created a massive reseller market in tier 2 and 3 cities over a short period. The second angle, which I’m even more excited about, is technology unlocking experiences that simply weren’t possible before. WisprFlow couldn’t have existed without great speech-to-text models. These are magical, never-before-possible experiences. The ongoing AI revolution is creating more of them every day.

Why are you bullish on the Indian ecosystem?

Harshit: India has a high-quality builder pool with deep software expertise, and rapidly growing hardware talent base. Ather and others are creating that muscle. But beyond talent, I think India is at an inflection point where it can produce genuinely global companies. Every country that’s made the leap to developed status, has had a generation of companies that became global giants—BYD and ByteDance from China, Samsung and Hyundai from Korea. India needs that generation, and I think the conditions are there: the talent, the demographics, and market segments that are genuinely representative of global demand. I’m most excited about Indian builders who are aiming at the world, not just the local market.

What kinds of founders do you most want to work with?

Harshit: I want to work with founders who are mission-driven, customer-first, and genuinely obsessed with the problem they're solving. I also think a lot about "why now?" — so I'm especially drawn to founders operating at inflection points, whether that's a shift in the market or in what technology suddenly makes possible. After a decade of learning the hard way, I'd love to share what I've picked up across as many founder journeys as I can.

Rohan Choudhary is a builder and product leader who has spent 14 years building across ad-tech, consumer-tech, and SaaS, with a focus on products built from India for the world. At InMobi, he helped build the core performance ad-tech stack behind one of India’s largest global technology companies. He later co-created Glance, a consumer product distributed through deep OEM integrations, which became a unicorn in its own right and scaled to 400M+ active users globally.

Rohan shares how he sees building in India changing and the trends he’s most excited about.

What drew you to South Park Commons?

Rohan: Years after my MBA, I switched between four jobs in 1.5 years, (including a startup attempt) because I was trying to find work I’d actually enjoy. What I kept coming back to was this – the same 24 hours can go toward building something genuinely ambitious and aligned with who you are, rather than just chasing the next available market opportunity. SPC is one of the few places in the startup ecosystem that takes that seriously—the “where to play” question, well before the “how.”

That question matters even more now. Building has gotten so much easier that there’s a real pull toward “I have a hammer, let me find a nail” rather than doing a more thoughtful search. I wanted to be around people resisting that pull.

Where are your largest areas of expertise?

Rohan: A few things I keep coming back to. The first is taking products global. I’ve done this multiple times at InMobi and Glance. There’s something I genuinely love about navigating the tension between what makes a product work in India, and what it needs to succeed everywhere else.

The second is products that become ecosystems, multi-sided networks and marketplaces. The journey from a useful tool to something with real network gravity is where I feel most at home. Over the last year, I’ve been working on AI commerce ideas built around consumer, designer, and artisan ecosystems, and I have some views on how these get built differently in an AI-native world.

Third, and this one goes back a long way - I almost did a PhD after engineering. I spent a lot of time in ML research at IIT Delhi. At both InMobi and Glance, I really enjoyed working closely with data science and ML teams. I think I’m good at helping researchers identify the right gaps to go after and then figuring out how to get that to market with an actual business model.

And more generally, I have a habit of thinking a few steps ahead—where the puck is going—and using that to make product strategy bets that pay off. At Glance, we were constantly evolving our PMF based on how we thought the market and consumer behavior would shift. That kind of sequencing is something I enjoy.

What’s the current state of the startup ecosystem in India?

Rohan: The first wave of Indian tech was operationally led. The technology was largely a commodity. The real moat was in aggregating fragmented supply, building distribution, and deploying enough capital to make things convenient at scale. India was the market being served.

What’s shifting now is where the value lives. The moat is moving from operations into the technology itself. Indian founders are building frontier IP in AI infrastructure, physical AI, health tech, manufacturing, and not just localizing someone else’s product. The shift I find most exciting is India going from being the market to being the builder.

What investment trends are you excited about?

Rohan: Deep tech finally feels like a breakout category in India, backed by real capital and real ambition. Space and aerospace is another one. Some very young founders are attempting genuinely audacious things here. A wave of IPOs is also creating a new generation of entrepreneurs and angels who’ll keep the energy going.

But the thing I’m most excited about is health tech via AI. People are now going after large, long-unsolved problems—the kind that can meaningfully change quality of life and longevity at scale. That feels different from what came before.

What kinds of founders do you want to work with?

Rohan: Three things, honestly. Agency and resilience—the next few years will keep disrupting propositions and business models faster than ever, so the ability to keep hustling through is non-negotiable. Truth-seeking—I work best with founders who are genuinely trying to figure out if what they’re doing is real, even if the answer is uncomfortable. And ambition—this is one of those rare moments when trajectory-changing companies are actually possible, and founder ambition is the main thing limiting that.

What directions of ideation are you drawn to?

Rohan: Network-based consumer products are still underrated to me. As tools get commoditized, products that require humans on both sides to work maintain something more durable. Agents can turbocharge the moat, but the human core is what keeps it real.

I’m also drawn to what I think of as “moving up the averages”—AI genuinely narrows the gap between tier 1 cities and the rest of the country in healthcare, education, governance. Founders working on that are doing something I find meaningful.

And I’m watching the SaaS model with a lot of curiosity. I imagine a world where every company has its own custom software, deployed by what look more like firm-specific developers than traditional SaaS customers—with a master product that evolves based on fragmented customizations downstream. It’s some hybrid of service and product that doesn’t have a clean name yet, and I’m excited to work with people figuring out what sustainable differentiation even means in that world.

Finally—mental health and suffering. This one is personal. I think AI has a real shot at alleviating a lot of daily lived suffering for a lot of people, and I want to spend time on it.

Interested in SPC? Apply to join us here.

Events at SPC

In NYC, Soleio hosted a Design Chat with Dylan Babbs, SPC alum and current CTO at Profound. In Bangalore we hosted -1 to Razorpay, where Harshil Mathur unpacked the challenge of building payments infrastructure at planetary scale, and -1 to Space, with actual astronaut Shubhanshu Shukla.

In SF, Demo Night showcased explorations at the edge of what’s possible: model memory research, tactile hardware-software interfaces, and new operating systems for an agentic world.

The month closed with the SPC Embodied AI Hackathon, where teams designed and completed a challenge to bring intelligence into the physical world involving manipulation, locomotion, simulation, or flight, with bonus points for collaboration. We ran SPC as a fully-stocked robotics lab including humanoids, drones, SO-101 arms, and 3D printers. From the Desktop Robot Assistant, which moved a candy bar using voice agents, VLMs, and ACT policies, to the autonomous drone that tracks your squats and coaches your form in real-time, many teams had end-to-end policies running on real hardware by the end of the weekend.

Member Updates

Product Launches & Company News

• Cognition raised over $1B in a Series D at a $26B valuation led by Lux Capital, General Catalyst, and 8VC.

• Recursive raised $650M at a $4.65B valuation led by GV and Greycroft, building AI that recursively improves itself via open-ended algorithms.

• Vori raised $22M in a Series B to build the self-driving OS for grocery stores, processing over $500M in payments across 55 cities.

• Goodfire launched Silico, a platform for building AI models with the precision of written software.

• VMax published PopuLoRA, research on co-evolving LLM populations for reasoning self-play.

• Asymptote Labs open-sourced Agent Beacon, a tool for AI agent security.

Personal Projects, Explorations & Publications

• Rex released PathOn Robotics, showing a custom navigation stack running on a Unitree Go2.

• Arya published a piece on debugging multi-agent systems, with a follow-up case study on iterative refinement.

• Jake shared a directory of 12,000+ open-source STEP parts for mechanical and electrical engineering, and released text-to-cad for generating 3D models in coding agents.

Coming Up

• Hard-Tech Showcase | Curiosity 2.0 (BLR), May 31st

• Design Chat with Joel Lewenstein (SF), June 8th

• -1 to Snowflake with Sridhar Ramaswamy (SF), June 22nd

Recent Content

We released new conversations with Nikesh Arora (Palo Alto Networks), Amit Jain (Luma AI), and Steve Ruiz (tldraw) covering everything from why the most defensible AI companies are building world models, to the infinite canvas as a use case for agents, to the cybersecurity crisis ahead.

Thanks for reading! The work continues.

The next six months in cybersecurity will be unlike anything the industry has faced before. Nikesh Arora, CEO of Palo Alto Networks, joins South Park Commons partner Aditya Agarwal to discuss why AI is both the threat and the only viable solution.

Nikesh traces his own unlikely journey from Google's Chief Business Officer to leading one of the world's most critical cybersecurity companies with no prior cybersecurity experience, shares what Masayoshi Son taught him about risk, and explains why most founders are too de-risked before they ever start building. He also breaks down why the same AI models writing code today will soon expose decades of hidden vulnerabilities at a scale no IT team is prepared to handle. Watch select clips of the episode below.

The cybersecurity crisis is here. Nikesh explains why AI is both the threat and the only viable solution.

Every great tech company starts with product. Most fail when they stop prioritizing it.

Everyone had the tech behind ChatGPT, but no one thought it was a real product.

Minus One is series about the winding journeys the world’s most interesting people take to becoming great—and what they do when figuring out a question we all face: What’s Next? Because before you launch at Zero, you have to figure out what to launch at Minus One. Hosted by South Park Commons Partners.

Interested in SPC? Apply to join us here.

Nobody in Silicon Valley cares what you did a decade ago. Even more so now, when a month in AI time can feel like years in SaaS time.

This ecosystem runs on obsession with the future. It doesn’t matter if you started a company twenty years ago and took it public. If you’ve stopped building, you’re forgotten. There’s an egalitarianism here which I haven’t found anywhere else in the world. That quality is Silicon Valley’s superpower.

But lately, I’ve watched it curdle into something destructive.

The average tenure at startups keeps compressing. I see brilliant researchers cycling between labs every eighteen months, never staying long enough to push past the frontier they wanted to explore. Founders wind down companies after a year, not because they tested the idea and lost conviction, but because the initial excitement wore off and something shinier appeared. I see engineers treat career progression like a video game. Collect the title, vest the cliff, move to the next stage.

To be clear, there’s good speed and there’s bad speed.

Good speed is table stakes. It’s strategic velocity, when you take fast signals seriously and pivot from stagnant ideas. Learning cycles get compressed and execution compounds, getting you from insight to iteration before the world has caught up.

Bad speed is impatience that pretends to be ambition. It optimizes for novelty over depth and doesn’t let you sit long enough inside the unglamorous stretch where the real edge is formed. Because the truth is that the frontier rarely yields itself in the first six months.

The danger today is that AI’s pace can make constant reorientation look like progress. Every week brings a new model, a new lab, a new interface, a new consensus. Reorientation at good speed can be strategic. But when everyone is perpetually restarting at bad speed, nobody stays long enough to discover something non-obvious. And increasingly, that’s what bad speed looks like: an ecosystem optimized for perpetual beginnings, but allergic to the endurance required for real breakthroughs.

The harder skill now is discernment, knowing when a pivot reflects genuine new information and when it’s simply fueled by fear, impatience, or the seduction of momentum itself.

AI tools make it dangerously easy to generate forward motion. You can prototype faster, test faster, ship faster, and convince yourself faster that you’re on the right path. But lower friction can also mean accelerating false starts and finding yourself in a local maxima.

Finding the right footing at the very beginning matters more than ever. In a world where it’s cheap to build almost anything, the real edge is choosing what is actually worth building and staying with it long enough to learn something the market doesn’t know yet.

Why Depth Always Beats Bad Speed

You don’t really understand a problem until you’ve lived inside it for a while. Trust with a team isn’t built in a quarter. Neither is the kind of institutional knowledge that lets you see second- and third-order consequences before they happen. The most valuable professional assets (judgment, relationships, domain expertise) compound. Cut the timeline short and you never reach the part of the curve where the returns become extraordinary.

When I visited hard-tech companies in El Segundo, where teams are building electric boats, nuclear reactors, and space habitats, what struck me wasn’t just the ambition. It was the time horizon. Conversations there stretch decades, not quarters. Nobody talks about disruption. They talk about what needs to exist and the painstaking work of figuring out how. There’s a seriousness to that kind of building that you lose when you’re optimizing for eighteen-month cycles. It reminded me of old-school Silicon Valley, before everything became a show, when people built things because someone had to build them.

If you only spend a short amount of time on a project, the most you can do is modify an existing solution. You can make something faster, cleaner, cheaper, more polished. But genuine novelty demands a different kind of compounding. You have to compound toward creation, not toward endless modification of what already exists.

In a world where AI is compressing the time it takes to execute, compounding matters more, not less. When powerful tools can handle more of the building, the bottleneck shifts to knowing what to build and why. That kind of judgment doesn’t come from a weekend with a new model. It comes from years of accumulation.

We live in fast times. AI is rewriting the assumptions underneath entire industries. The temptation to optimize for optionality is completely rational. But grit, resilience, and loyalty will be eternal traits of the best people. No amount of speed changes that.

Interested in SPC? Apply to join us here.