Tag: startups

Categories
Business Creativity Innovation Inspiration

The Idea That Won’t Let Go

“There are three elements to every great idea: 1. It solves for ‘why.’ Long before you figure out what a product will do, you need to understand why people will want it. The ‘why’ drives the ‘what.’ 2. It solves a problem that a lot of people have in their daily lives. 3. It follows you around. Even after you research and learn about it and try it out and realize how hard it’ll be to get it right, you can’t stop thinking about it.”
— Tony Fadell, Build

The third element is the only one that isn’t optional. You can fake the why — retrofit it, hire a consultant to write it on a slide in a font called Montserrat. You can borrow the what; most products are just other products in a better jacket. You cannot fake the third thing, because the whole test of it is that it happens without your permission.

Call it a visitation. It shows up uninvited at the stoplight, in the shower, at 2:40 a.m. when the ceiling becomes a screen for it. You know the unit economics don’t work. You know the regulatory path is nine years long. You know — you know — that fourteen better-funded people already tried this and left with nothing but a Delaware C-corp and a grudge. None of it helps. The idea has already filed its paperwork. It lives here now.

You cannot bullet-point a visitation. There is no OKR for thinking about landing gear hydraulics while your wife is telling you about her sister’s wedding. Everyone who’s built something that mattered will tell you this, late enough at a dinner and honest enough on the wine: not conviction, not passion — that laminated word — but the visitor, still standing in the doorway of every other thought you’re supposed to be having.

It doesn’t check your calendar. It doesn’t care that you’ve moved on to point two, the sensible one, the one venture capitalists nod along to with their flat whites going warm. It was never about the market. The market is the alibi you build afterward, so you don’t have to say the true thing: I didn’t choose it. It moved in while I was asleep, and my life rearranged itself around the shape of it, the way a house rearranges itself around a family it didn’t ask for.

Maybe the third element isn’t a filter for the idea. Maybe it’s a filter for you — a way of finding out, by accident, at 2:40 in the morning, staring at a ceiling that has decided to keep the light on.

Categories
AI Startups

A New Reason to Launch

“Before you launch, the speed you can build is now mainly limited by your imagination in what you tell AI. After you launch, the AI can watch your users and make improvements on its own.”
— Jared Friedman, Y Combinator

Jared Friedman watches hundreds of founders a year navigate the gap between idea and launched product. He notices patterns the rest of us miss. And what he’s describing above is not an incremental improvement in how software gets built. It is a change in the nature of the advantage.

This is a different kind of liberation than founders have known before.

The old liberation was launch early and the market corrects your wrong assumptions. Humbling, but useful. You were still the one doing the correcting, late at night, rewriting the onboarding flow based on what the data told you.

The new liberation he’s describing is something closer to multiplication. You launch, and now there are effectively more of you. The AI is watching session replays you’ll never have time to watch. It’s noticing the drop-off after step three that you’d have caught in month four. It’s holding the pattern of a thousand user paths simultaneously and asking what they mean. Your imagination seeded the thing. Reality is now feeding it.

That observation redraws the map cleanly. Pre-launch and post-launch used to differ in degree — you knew more after than before. Now they differ in kind. Pre-launch you are the sensing organ. Post-launch you’ve grown new ones.

The founders who feel this most viscerally, I suspect, are the ones building alone or in pairs — the people for whom every previous era of building had a hard ceiling imposed by human hours. They could only read so many support tickets. They could only run so many experiments. The ceiling is lifting and the feeling is of a room getting larger.

The core advice hasn’t changed. Paul Graham was saying “launch early” twenty years ago and it was true then. What’s changed is the reason underneath it — the mechanism that makes it true now is nothing like the one he had in mind.

The advice is twenty years old. There is a new reason and it is brand new. Most people haven’t noticed the swap yet. But they will.

That window does not stay open long.

Categories
Business Storytelling

The Closed Laptop

The conference rooms all look the same after a while. Same long table. Same chairs that cost more than they should. Same window with the same view of the same parking lot baking in the same California sun. You stop seeing them. You develop a kind of practiced receptivity, a professional openness that is also, if you are honest, a professional distance. You have heard the story before. You know where you are in the presentation without looking at the slide number.

Until the day someone sits down across from you and closes their laptop and says: can I just tell you our story?

Fred Wilson, the venture capitalist at Union Square Ventures, has spent forty years learning to tell the difference between founders who can build and founders who can make you believe. The skill he overweights now, he’ll tell you plainly, isn’t technical. It’s selling. Recruiting, fundraising, convincing customers, inspiring teams. “Actually being able to write code,” he said recently, “is probably not a big deal anymore.” What matters is whether you can cross the distance between your vision and someone else’s imagination and deposit something true and alive on the other side.

Most founders never figure this out. They build the deck instead. They pull the projector cable from the drawer — there is always a drawer, there is always a cable — and the room fills with blue light and bullet points and the comfortable geometry of a prepared presentation, and what never happens is the thing that needed to happen.

But there was this one morning.

He came in with his cofounder in the flat gray light that Silicon Valley gets in February, when the rain has stopped but the sky hasn’t decided what it wants to be. They were early. He set his bag down and sat directly across from me — not at the presenter’s angle, not with one eye already calculating the distance to the screen — directly across, the way you sit with someone you already know, or intend to. Neither of them reached for the cable in the drawer.

He looked at me with the particular steadiness of a person who has decided not to manage the moment.

Can I just tell you our story?

I want to be honest about what happened next, which is that I felt something shift before he said another word. Not a decision exactly. More like the precondition for a decision, the ground tilting slightly in a direction I hadn’t chosen. I was, in some way I couldn’t have defended rationally at the time, already with him. And I knew it, and I knew it was not an entirely reasonable response to a man who had been in the room for less than a minute, and I felt it anyway.

The laptop stayed closed for the next twenty minutes. No transitions. No bullet points. No hockey stick arcing toward a number reverse-engineered from a desired outcome. Just his voice and what he believed and the quality of attention you give a person when there is nothing else in the room to look at.

The deck came later. It was beautiful. By then it didn’t need to be anything except true.

Storytelling is not a skill in the way that financial modeling is a skill. It is older than that by such a margin that the comparison almost doesn’t make sense. What we are really talking about is the oldest technology human beings possess — a person in a room, a voice, an image made of nothing but words and the willingness to believe in them. It was doing its work around fires forty thousand years before the first conference room was built, and it has never once required a projector.

What the great storytellers understand, and what the best founders understand in the same unspoken way, is that a story is not a transfer of information. It is a transfer of inner states. When it works — when it really works — something that existed inside one person gets reconstructed inside another, and the listener emerges changed. Not persuaded. Not informed. Changed. These are different experiences, and only one of them makes a person willing to bet their career on something that doesn’t exist yet.

The deck puts glass between the teller and that possibility. The founder stands at the edge of the blue light pointing at things, and the room evaluates the things, and what never happens is the transfer. Everyone files out having formed opinions about the slides rather than beliefs about the person. Opinions and beliefs are not the same.

Wilson understands this even if he wouldn’t use these words. When he says the skill is selling, what he means underneath the selling is: can this person walk into a room and make other people inhabit their vision? Not convince them. Inhabit. The difference is the difference between reading about a place and being there. One of them changes how you act. The other one you forget on the drive home.

The projector cable is still in the drawer. Someone will pull it out next week, and the room will fill with blue light, and another founder will stand at the edge of it pointing at things, hoping that the right font and the right graph will do the work that only a human being, exposed and without props, can actually do.

It won’t. It never does.

The CEO who closed his laptop had been carrying a story he believed in, and he knew the story was the thing, not the packaging around it. He understood that the oldest container is also the most powerful one. His own voice. A room. Someone willing to listen.

I was ready to work with him before he said another word.

Categories
AI Stanford

The Unit of Production Just Collapsed

The lecture was a Stanford CS session, AI-native companies, Garry Tan walking through what it now takes to build something. He’d rebuilt his old startup, Posterous, in five days on a modest Claude plan. A thing that once required a team and a runway. He said it matter-of-factly, the way you describe something that’s already obvious to you and hasn’t yet reached everyone else.

The argument Tan and his colleague Diana Hu were making wasn’t really about AI. It was about the economics of effort — specifically, what breaks when the cost of turning an idea into a working thing falls by an order of magnitude.

Their framing: AI-native organizations running as closed-loop systems, agents with access to the real artifacts of work, able to iterate without the error-accumulation that comes from handoffs and headcount. Revenue-per-employee ratios of a million dollars or more, with live examples already in the YC portfolio. Document processing, logistics, voice agents for specialized workflows.

What I kept hearing underneath all of it was a quieter claim: the mental model of what a startup requires is wrong.

Or rather, it’s right about the past and increasingly wrong about the present.

The assumptions embedded in “I can’t do this alone” or “we’d need to hire for that” or “we don’t have the bandwidth” — those are load-bearing assumptions, and the load is shifting.

I have some small version of this — not as a founder, but as someone who retired into curiosity. The blog, the reading, the daily effort to keep up with what’s moving: each one is a practice in staying oriented while the map keeps changing.

What I notice is that the constraint has shifted. It’s not information anymore. It’s not even tools. It’s the capacity to ask better questions of the abundance, to know what matters when everything is accelerating.

That’s the thing I find unsettling, yet also genuinely interesting: the skills that remain irreplaceable are the hardest ones to teach, and the hardest to evaluate in yourself. Knowing what matters. Recognizing when an output is almost right and almost wrong. Setting direction in ambiguous conditions and being willing to be wrong about it. These were always the valuable things. They were just obscured by all the coordination overhead that surrounded them.

The students in that Stanford course were asked to build something called a One-Person Frontier Lab — use the best available tools to extend your own reach over ten weeks. It’s framed as an academic exercise. It doesn’t feel like one.

But I’m not building. I’m mostly watching, and thinking about what this radical new fermentation does to everything downstream — to labor markets, to what a company even is, to how we’ll organize work and meaning when the old unit of production no longer applies. Those are slower questions. But they’re the ones that feel urgent to me.

The old excuses are getting lighter. Not that everything is possible — but that the weight of the usual constraints has changed.

What you choose to build, and whether you choose to build it at all, is more purely a decision than it used to be. That’s either clarifying or terrifying, depending on the day and my mood.

Categories
AI California San Francisco/California

Distant Billboards

Greg Isenberg came back from San Francisco with seventeen observations. The billboards advertising either B2B inference infrastructure or vertical agent companies, the seed rounds, the forward-deployed engineers, the founders showing each other their Obsidian vaults like athletes comparing gym routines.

He noted an important thing in observation fifteen, almost as an aside.

Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats — none of them use any AI at all.

Everett Rogers formalized the technology diffusion model in 1962. He was studying hybrid seed corn in Iowa. He noticed that the farmers who adopted early weren’t just better informed — they had different social networks, different relationships to risk, different orientations toward outside knowledge. The late adopters weren’t slower. They were operating from a different set of facts about what was safe to try.

Those AI billboards in SoMa are not visible in the Mission. That’s not metaphor. That’s just geography.

What strikes me about the taqueria is not that it’s behind. It’s that the conversation happening a mile away — about MCP endpoints and agent fleets — is not legible to it. The vocabulary doesn’t exist there yet. Nobody has sat across from the woman making carnitas for twenty years and said: here is what this could do for your ordering, your scheduling, your response to a customer who asks on Yelp at 11pm whether you’re open on Monday. One day her daughter or son might.

The builder class optimizes for the builder class. You build what you understand, for people whose problems you can see. The founders in SoMa understand each other’s problems with extraordinary precision.

The woman making carnitas has different problems — thinner margins, less access to capital, relationships built over decades that don’t easily transfer to a new system. Nobody is at the Series A meeting making the case that her problems are the interesting ones.

The historian of technology David Nye wrote about the “technological sublime” — the awe Americans felt in the nineteenth century standing before a great bridge or a locomotive or the first electrified city. The feeling was real. But the sublime is a view from a particular angle. The workers who built the bridge experienced something quite different. The families displaced by the railroad’s right-of-way experienced something different still.

The question isn’t whether the technology will eventually reach her. It will. The diffusion curve is patient. It likely will surprise.

The question is whether anyone is doing the translation work. The act of standing in a specific kind of life and asking: what would this actually change here? In the actual kitchen, on the actual Tuesday.

Isenberg noted that the coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people.

The taqueria is also a place where people want to be around people. It has been that for a long time.

She’ll adapt. She’s been adapting for twenty years.

But that’s a very different story than the one being told in San Francisco on those billboards.

Categories
AI Business

The Topography of a Face

I found myself staring at the physical geometry of a conversation the other day—not the words, but the topography of the faces delivering them.

Elad Gil recently shared a fascinating experiment during a conversation with Tim Ferriss. He’s been uploading photos of startup founders into AI models and asking the machines to predict if they’d be successful, purely based on their “micro-features.”

“Because if you think about it, we do this all the time when we meet people, right? We quickly try to create an assessment of that person, their personality, and what they’re like. There are all these micro-features—like, do you have crow’s feet by your eyes, which suggests that your smiles are genuine? […] So, I have this whole set of prompts that I’ve been messing around with, just for fun, around: ‘Can you extrapolate a person’s personality based off of a few images?'”

He notes the model breaks down the crow’s feet and the furrowed brows, extrapolating a personality from a static frame. It’s a parlor trick, perhaps. But it works because it holds a mirror to our oldest, most unexamined instinct.

We are all amateur phrenologists of the human face. We sit across a table, measure the crinkle of an eye or the tightness of a jaw, and we build a rapid, invisible architecture of trust or suspicion. Over decades of investing and making career choices, I’ve often leaned heavily on this silent language. I’ve backed founders because their intensity felt genuine, and I’ve passed on others because something in their posture felt misaligned.

But if I am brutally honest, that intuition has sometimes been a mask for my own blind spots. I’ve held on to failing investments for far too long because I trusted a reassuring smile. We like to think our gut instinct is a sophisticated instrument. Often, it is just a pattern-matching engine running on deeply flawed historical data.

Now, we are handing that very human habit over to a machine. We prompt the AI to become a “cold reader,” and it obliges, predicting who will be the quiet observer and who will deliver the dry wit.

The unsettling part isn’t that the machine might get it wrong. The unsettling part is that it might get it exactly right—by mimicking the very same rapid, superficial judgments we make every day, just at a terrifying scale.

We are teaching silicon to read the human code. The future will belong to those who realize the code was always written in our own biases.

Categories
AI

The Geometry of Speed

We are surprised when witnessing something move faster than our intuition expects. We are inherently wired to understand slow, compounding growth. We expect the long, grinding years of the plateau—the quiet periods where nothing seems to happen before a sudden breakthrough.

I was looking at a chart Patrick Collison shared this morning, and it challenged that very intuition. It’s a simple, stark visualization: AI model intelligence relative to the formation date of the lab that built it.

If you trace the lines for Google and OpenAI on the right side of the graph, you see the history we’ve all lived through. Thousands of days—more than a decade of quiet, methodical, often unglamorous research—before their trend lines finally bend and shoot upward. It is a geometry of patience. It’s the visual representation of laying bricks, one by one, year by year, until you have a foundation sturdy enough to support the weight of a revolution.

And then, on the far left of the chart, there is a red line. MSL. The team behind Meta’s new Muse Spark model, released today.

The red line doesn’t curve. It doesn’t slope. It simply strikes straight up, like a lightning bolt in reverse.

In roughly 200 days since formation, this new effort achieved a level of capability that took the early pioneers thousands of days to reach. Collison noted how much he loves seeing things done quickly, and it’s hard not to share that specific, visceral thrill of seeing the boundaries pushed so aggressively.

I find myself thinking about the architecture of speed and what it means for the rest of us.

We spend so much of our lives absorbing the lesson that “good things take time.” We are taught that the crucible of meaningful work requires a long, slow simmer. And mostly, that remains true. The compound interest of human experience is real, and wisdom is rarely rushed.

Yet, every once in a while, a new paradigm emerges that doesn’t just accelerate the timeline—it collapses it entirely.

The pioneers cut the agonizingly slow path through the jungle, taking the brunt of the time, the friction, and the missteps. The ones who follow—like xAI, Anthropic, and now MSL—don’t have to clear the brush from scratch. They can look at the map, pave the road, and simply drive.

What does it mean for our own mental models when the timeline from “formation” to “frontier” shrinks from five thousand days to a few hundred?

It is a jarring reminder that the past pace of performance is not a law of physics.

I think about my own assumptions—how often I assume a project, a habit, or a societal shift will take a while, simply because similar things took a while in the past. We anchor our expectations to old geometry.

Meta’s release of Muse Spark is a technical feat, certainly. But the chart itself holds a broader, more human lesson. It’s a visual prompt to constantly re-evaluate our assumptions about how long the impossible is supposed to take.

The future doesn’t always arrive on a comfortable, predictable schedule. Sometimes, it just shows up unannounced, demanding we adjust our stride to keep up.

Categories
AI India

Intelligence as a Public Good: India’s “AI ka UPI” Revolution

There is a recurring rhythm to human progress: a breakthrough is born as a luxury, matures into a commodity, and ultimately solidifies into infrastructure.

We saw it with electricity, we saw it with the internet, and in 2016, we saw India do it with money through the Unified Payments Interface (UPI). UPI took the friction out of digital finance, transforming it from a walled garden guarded by private banks into a digital public good.

Now, it appears India is attempting to do for intelligence what they did for payments.

The global narrative around Artificial Intelligence is currently dominated at one end by massive private moats. At the other end are various open source/open weight efforts.

Silicon Valley primarily approaches AI as a capital-intensive arms race. Trillion-dollar tech players ramp huge compute, train very large models, and rent out intelligence via by the drink APIs. This intelligence is a proprietary and monetized luxury.

Enter the “AI ka UPI” initiative and the IndiaAI Mission discussed by Ashwini Vaishnaw at this week’s India AI Impact Summit.

Instead of treating AI as a product to be sold, India is architecting it as a Digital Public Infrastructure (DPI). The government is doing the heavy lifting—subsidizing the compute, curating population-scale datasets, and building foundational models.

Currently, they are making over 38,000 GPUs available to startups and researchers at around ₹65 (less than a dollar) an hour, a sheer fraction of the global cost. They are rolling out sovereign stacks like BharatGen and conversational models fluent in 22 regional languages.

“They are building an ‘orchestration layer’ for cognition.”

If a developer wants to build a voice-agent to help a rural farmer diagnose a crop disease, they don’t have to worry about the backend compute, the dataset acquisition, or paying a premium to a tech giant. They just plug into the public rails.

As I watch this unfold, I am struck by the philosophical shift it represents. We have become deeply conditioned to view AI through the lens of scarcity and subscription. But what happens when intelligence becomes a public utility?

It shifts the center of gravity of innovation. It becomes about who can solve the most acute, localized, human problems. The friction of creation drops to near zero. A bootstrapped team in a tier-two city can suddenly wield the same computational reasoning as a VC funded Silicon Valley startup.

There is also an element of sovereignty here. In the 21st century, relying on foreign infrastructure for your population’s cognitive processing seems akin to relying on a foreign nation for your electricity. True technological independence requires sovereign AI—models trained on indigenous data, reflecting local culture, nuances, and values, rather than the implicit biases of others.

The implications could be staggering. We are moving from an era where AI is an elite tool to an era where it is the invisible, ubiquitous fabric of daily life for over a billion people.

The true measure of AI’s ultimate impact won’t be found in benchmark scores on a server farm. It will be found in the quiet dignity of a citizen accessing global markets through a vernacular voice assistant, or a rural clinic predicting patient outcomes with public compute.

I look forward to following India’s AI efforts as this and other AI initiatives are more clearly defined.

Questions to consider

1. The Value of Human Capital: If artificial intelligence becomes as ubiquitous, reliable, and cheap as public electricity, what uniquely human skills will become the new premium in a hyper-automated society?

2. Cognitive Sovereignty: How will the geopolitical landscape shift when emerging economies no longer need to import their “cognitive infrastructure” and inherent cultural biases from Western tech players?

3. The Centralization of Truth: When a government builds and curates the foundational AI models for over a billion people, where is the line between providing a democratized public good and engineering a centralized cultural narrative?

What else???

Categories
AI Leadership

The Power of Two

I recently watched and thoroughly enjoyed Harry Stebbings’ interview with OpenAI’s Sam Altman (CEO) and Brad Lightcap (COO). In addition to gaining new insights into OpenAI’s evolution, their conversation covered a wide range of topics regarding the future of AI and its implications for society and new ventures.

One of the most fascinating aspects was the dynamic between Altman and Lightcap — hearing them discuss their respective strengths, weaknesses, and how those translate into their roles at OpenAI. It’s uncommon to witness a dual interview like this, with two colleagues who have clearly worked together for years and have complete confidence and trust in each other’s judgment and insights.

Throughout my involvement with various small companies, I wish I could have experienced such a powerful duo! In my experience, it’s not uncommon for the CEO to dominate the senior management team’s dynamics. While this sometimes works well, I’ve also seen it lead to reduced performance or frustration among senior managers due to the CEO’s actions.

Altman and Lightcap (and OpenAI by extension) appear to have a much more synergistic working relationship — effectively amounting to a co-equal division of responsibilities. I highly recommend watching this conversation for anyone involved in a startup aiming to scale quickly and effectively! Congratulations to Harry Stebbings for his hosting this excellent conversation with two key individuals leading the evolution of AI!

Categories
Living Work

The Silver Bullet Mindset

In my strategy consulting practice, I’ve come across a pattern that I find interesting. It’s what I’ve come to call “silver bullet thinking” – our desire to find the one right answer for any particular problem.

I think this need for one right answer is something we’re born with – which is then further developed in each of us through the years of education we go through. And, finally, when we go to work in a company, especially a larger company, the decision processes further refine this kind of thinking.

But sometimes the search for the silver bullet leads to the wrong outcome – a premature focus on a particular strategy which then gets organizationally committed, funded, and elevated in importance. In my experience, the larger the company, the more likely this kind of silver bullet thinking will dominate.

Yet, when I’ve worked with smaller, more innovative companies, they are less wed to their silver bullet – and more open to a process of on-going evaluation of the strategy based on the feedback from the market. Ideally, they’re able to pursue a couple of different strategies and test the market response to each in the process.

It seems to take a different, more entrepreneurial mindset for this to happen – and might one of the reasons that executives with big company experience find it so challenging to work in small company settings. Learning to be situational – considering when to stay loose and pursue multiple initial strategies vs. binding the whole organization to a single strategy – the silver bullet – may be one of the key leadership skills required of successful innovators.