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“No AI” – the “No Software” re-run in enterprise techhnology

Posted on 31 August 2026 by dselz

A week ago I argued that AI may keep legacy software alive longer. Longer though does not mean infinitly. So would could happen?

Around the turn of the century the boldest claim in enterprise software was a red circle with a line through the word SOFTWARE, or simply “No Software” in plain English. It took thirteen years to be right.

Marc Benioff put that mark on everything. A phone number, 1-800-NO-SOFTWARE. A logo shaped after the Ghostbusters symbol. It was said by a software company, which was the whole point. Salesforce was software. What you no longer had to do was install it, house it, staff it, survive the eighteen month implementation. Notice what he never said. He never said NO CRM. The value was never in question, only the burden attached to it.

For those of us who had grown up with the new reality, it was obvious this would replace the old world eventually. As always, it took a moment longer than anyone thought. The slogan launched in February 2000. IDC did not rank Salesforce the number one CRM vendor until around 2013, and as late as the second half of 2011 it was still third, at nine and a half percent, behind Oracle and SAP. Siebel was not even beaten by Salesforce. SAP took the revenue lead in 2004, Oracle bought Siebel in 2005 and closed it in January 2006, and the new architecture reached the top of the market seven years after the old one had ceased to exist as a company. Nobody staged a duel. The winner arrived late and stayed.

That is worth holding onto, because we are in the patching phase again. Last week I argued that AI behaves like WD40 and Play-Doh on legacy software, lubricating the parts users hate and filling the shapes that are missing, which dulls the pain enough that replacement budgets never appear. The renewal numbers back it. ServiceNow posted 98 percent renewals in the same year roughly 300 billion dollars of software market value evaporated in a single day. The market priced disruption. Buyers renewed.

So the fair question is what ends it. I have a hypothesis, not a proof, and it rests on having watched the last cycle rather than on hard data to underscore my hunch.

A copilot always has someone checking every output (or so they should). Eventually that someone is absorbing the fact that the data underneath is a mess, given poor answers. Remove that someone, which is exactly what full workflow automation does, and data quality stops being an annoyance and becomes the constraint. Many (Gartner) surveys already show the shape of it: Agent adoption is running well ahead of any confidence that the data feeding those agents is fit for production.

Next up is accountability. Once something other than a person is deciding, you need an architecture that makes decisions visible, governable and attributable. A system built to record transactions was never built to record why. That cannot be bolted on. I have no survey for this one. It comes from building it.

So to bolt on AI on top of a legacy system will carry you through for only so long.

This plaster above plaster above plaster architecture will not break on a date. It breaks on a cost and versatility vector, the way Siebel did. Plastered over for years, entirely defensible, until the new SaaS stack was cheap and flexible enough that the old one was not, and then the move was fast and total and nobody announced it.

Solutions that are truly built AI first will eventually pick up the workloads and become the new enterprise backbone (and yes enterprises and value chains will look very differently to today). Solutions like Kora, built AI first, with these decision architectures embedded right from start will replace legacy from the ground up rather than patching it.

In short: Most predictions are about the immediate future. They say nothing about 2033. The last slogan needed thirteen years.

Which brings me to the part I have not settled. If this cycle gets its own red circle, what word goes inside it. NO AI has the audacity, and no AI vendor can say it back without conceding, though it only works if AI has already become a word carrying the theater as well as the value. NO APPS is the generational rhyme. Benioff negated the noun Siebel sold, and the noun Salesforce then sold everyone was the app.

I do not know which one it is. Neither did anyone standing in front of that red circle in 2000. What they knew was that the word in the middle was the one the incumbent could not say back. Curious to hear from you which slogan it is now.

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AI won’t kill legacy software, it may actually keep it alive

Posted on 24 August 2026 by dselz

AI may not be killing legacy software. It may keep it alive. Bad news for founders. And contrary to what the SaaSapocalypse supposedly was meant to be.

On Tuesday 4 February 2026, roughly 300 billion dollars came off software in a single day. Salesforce, ServiceNow, Adobe and Workday each fell about 7 percent. The trigger was an AI product release. The iShares Expanded Tech-Software Sector ETF (IGV) index was already about a third below its late September 2025 peak, forward multiples had been squeezed, down from around 39x to 21x. Short sellers cleared more than 20 billion dollars in 2026 betting against legacy SaaS.

We AI-founders read that as our moment. The incumbent’s product is terrible, and now the market knows it.

Fast-forward to last month: ServiceNow renewed at 98 percent, in the same quarter its agent deployments grew ninefold in nine months, its AI contract value passed one billion, and their subscription revenue rose 24.5 percent. By early June the index had gained 40 percent off its April low.

Same arena, opposite verdict on the play given, just five months apart. The swings aren’t the story. The story is what buyers did while investors panicked and then apologised. They renewed.

Three reasons that matters more to and founder than it does to the incumbent.

First, AI is WD40 and Play-Doh at the same time. WD40 lubricates what grinds, a chat layer over an interface nobody has liked in a decade, an agent that fills the form nobody wanted to fill, a model that reads data the schema never supported. Play-Doh fills the missing shape, the integration that was never built, the report nobody could get out. Legacy roadmaps are frozen; the requested feature will now sit on the roadmap forever because a language model covers the gap well enough. The approach does not fix the product underneath. But it raises the floor of what a buyer will put up with when it comes to replacement costs.

Second, the friction was the wedge. Hardly anybody in a large organization rips out a working system because a better one exists. They rip it out because the current one hurts every Monday, every Tuesday and so on. Or until someone senior gets the wrong number in a board pack. That’s when the replacement budget magically appears. Displacement runs most of the time on annoyance, not on architecture. Take the annoyance away and the replacement conversation gets harder, by a lot.

Third, AI attacks lock-in and defends it at the same time, and the defence is the half that reaches the buyer. On 23 February 2026, Anthropic published a post on using Claude Code for the analysis, dependency mapping and documentation work that made COBOL modernisation expensive. IBM fell 13.2 percent the next day, its worst day since October 2000, and closed February down 27 percent, its biggest one-month loss since the sixties. That market priced the death of switching costs. Yet five months later IBM’s CFO said there was no evidence of clients moving off mainframe, and that AI was pulling new workloads onto it…

Both things are true. Writing the replacement got much cheaper. Switching did not. Undocumented business logic from three decades ago, data structures welded to the old environment, regulatory sign off that moves at its own pace, the original authors retired years ago. Cheaper code is not a cheaper switch.

There is a second reading of the same data that I find harder to argue with. Test Double looked at what AI changes about legacy systems and separated three signals: user friction, engineer momentum, and the cost of carrying the thing. AI improves the last two. Friction stays where it was, or gets worse, because the same tools make it easy to bolt on features faster than anyone removes them. Their line is that AI is a maintenance tool, not a prevention tool. Put that next to my argument and it gets worse, not better. If AI preserves old bad software and helps produce new bad software faster, product quality decays as a differentiator from both ends at once.

So what is left as a wedge? Not the better interface, that is a weekend of work away. Not the missing feature, Play-Doh covers it. What is left is what cannot be generated. A process end to end that the incumbent does not own and cannot reach from where it sits. Data nobody else holds, which is the part everyone skips because curation is boring and that is exactly where the value sits. An audit trail designed in from the start, because governance does not retrofit onto a system built to be trusted rather than inspected.

All this puts single-purpose AI-vendors into a bad spot. Or to quote Terra Higginson, principal research director at Info-Tech Research Group: “Those guys are in trouble right now. They don’t own a lot of the workflow, they don’t own a lot of the transaction, they don’t own a lot of the data and they’re easy to replicate through vibe coding”.

That is not a reason to build a better AI first product. It is a reason to stop treating the incumbent’s badness as your go-to-market plan, because badness is now a solved problem.

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Flat screens and enterprise AI, the waiting game…

Posted on 17 August 2026 by dselz

Waiting to buy a flat screen in 2004 cost you nothing. Waiting on enterprise AI in 2026 costs you the one thing the price drop will never deliver.

In 1997 Fujitsu shipped the first commercial 42-inch flat panel. It cost 17,500 dollars and it flopped, selling mostly to trade shows and conference rooms. By 2005 a comparable Toshiba set cost 4,500 dollars, and prices kept falling about 30 percent a year. Every buyer in the showroom knew next year’s screen would be bigger and cheaper. Waiting was the rational move.

I hear the same math in enterprise AI conversations every week. Models improve every quarter, prices drop every quarter, so the pilot gets parked and the budget moves to next year. The organization sits still, the hare in front of the foxhole. Ears flat, frozen. It feels safe. It is not, for three reasons.

First, the price argument is even stronger than the waiters think, and that is the problem. Television prices fell 94 percent over 18 years, per the Bureau of Labor Statistics, the most famous deflation in consumer history. The price of constant AI performance is falling between 9x and 900x per year depending on the task, per Epoch AI, roughly 40x per year at GPT-4 level. AI deflates more in a quarter than flat screens managed in a decade. Follow that logic honestly and it ends in never buy, because tomorrow is always cheaper. An argument that ends in never buy is not a procurement strategy.

Second, the frame is broken because a TV is finished the day it goes on the wall. The family that waited from 2004 to 2007 watched exactly the same TV as the family that bought early, at half the price. No skill was lost by waiting, because none was needed. Enterprise AI is a different kind of purchase. US Census Bureau data across tens of thousands of firms shows the shape, a J-curve. Adopters take a productivity dip first, caused by the mismatch between new tools and old processes, then outperform non-adopters in productivity and market share over the following four years. The dip is deepest at older firms, and about a third of it traces to plain management practice, not technology. The dip is the tuition. It gets paid in curated data, redesigned processes, and governance muscle, and it does not get cheaper when the tokens do, because it was never priced in tokens. Waiting does not skip the dip. Waiting moves your dip two years out, to a time when your competitors are already climbing out of theirs.

Third, the flat screen story has an ending the waiters forget. The boom came in the mid 2000s, while prices were still falling 30 percent a year. People did not wait for the floor. They bought the moment a screen crossed their threshold of worth it. Even in the perfect market for waiting, waiting lost its grip. The floor never announces itself.

Now the honest numbers, because the vendor decks will not give them to you. The European Investment Bank studied 12,000 European and US firms and puts the near-term labour productivity gain from AI at about 4 percent, and only for firms that invest in the boring complements, software, data, training. Four percent, not 10x. And yes, the much-quoted MIT figure says most GenAI pilots show no measurable P&L impact. Both numbers argue for starting, not waiting. A 4 percent gain that compounds while you learn beats the zero you defer, and pilots fail on exactly the things you can only fix by doing. Deferring the tuition does not waive it. It adds interest.

The flat screen logic was right for flat screens and is wrong for AI, not because the prices behave differently, but because the products do. You are not buying a screen. You are buying the distance between your dip and your outperformance, and that distance is measured in time spent doing, not in dollars saved waiting.

The hare eventually has to move. Better to move on your own terms. Pick one process, document heavy, rule based, measurable. Give it an owner and an error budget. Run it live for ninety days, in the actual workflow, not in a lab. The screen will keep getting cheaper. Your learning curve starts the day you start.

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If AI is a bubble, the real question is: Who survives the bust

Posted on 9 August 2026 by dselz

In 1882, America laid more than 11,000 miles of railroad track in a single year. Eleven years later, half the industry was in receivership and unemployment ran between 12 and 18 percent. The track stayed.

As many know, I like trains, so the parallel was hard to miss. Today four companies, Microsoft, Alphabet, Meta, and Amazon, plan to spend about 725 billion dollars on AI infrastructure in 2026, up 77 percent in a single year. Amazon alone is guided to around 220 billion. Every second post calls it a bubble and predicts the pop. They are probably right about the pop. They are asking the wrong question.

The question is not whether AI is a bubble. It is what survives the bust, and who. History answers both. Three things worth holding onto.

The pattern is old, not new. In the 1880s, railroads were 15 to 20 percent of all national investment. Companies laid competing lines side by side into empty prairie, funded by debt, chasing territory nobody had asked them to win. By 1892, fewer than half of rail shares paid a dividend. Then the Philadelphia and Reading collapsed in February 1893 and pulled the rest down. Union Pacific, Northern Pacific, and the Santa Fe all went bankrupt. Swap miles of track for gigawatts of compute and you have read this morning’s headlines.

The infrastructure survived. The speculators did not. About 200 railroads went into receivership after 1893, roughly 41,000 miles of track, and by 1895 a third of the network was bankrupt. The track did not rust into nothing. J.P. Morgan and a handful of disciplined operators bought it cheap, put in fresh capital, cut the ruinous competition, and ran it at a profit for fifty years. The winners were not the ones who laid the most track fastest. They were the ones who bought proven track cheap and made it earn. Discipline beat speed.

This time the track spoils. Railway iron lasted decades, which is exactly why buying it cheap in 1894 was a good trade. AI compute does not. In January 2025, Amazon cut the assumed life of its servers from six years to five and took about 920 million dollars in accelerated depreciation. Its Trainium 2 chips look set for replacement at around 20 months, while the hardware needs roughly three years just to break even. Read that again. The replacement cycle is shorter than the payback. The track was iron. A lot of this buildout is fresh milk with a date stamped on the carton.

The analogy is imperfect, and pretending otherwise would be the nonsense I am in my posts point out. The 1893 crash became a banking panic because the railroads ran on risky debt. The hyperscalers fund most of this from operating cash, so a slowdown is less likely to take the banks down with it. And some of the demand is real today, not a bet on empty prairie. Fair. That said, Amazon still burned about 7.6 billion in free cash flow as spending outran income, and a chip that is scrap in two years does not care whether you bought it with cash or debt. The bill comes due either way.

So we should stop asking whether AI is a bubble. It has the shape of one and will probably deflate like one. The interesting question is which side of 1893 you want to be on. The durable layer survives every cycle: power, land, buildings, fiber, and the workflows on top that earn their keep before the silicon ages out. The rest is fresh milk. The operators who can tell the two apart inherit the network. The ones who cannot are laying redundant track into empty land and calling it strategy.

Here’s a real question before a franc or a dollar is committed to anything with AI stamped on it: Does this spend pay for itself before it depreciates, and how fast. If nobody can tell you, you are not buying infrastructure. You are buying milk.

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All things agentic: Are we (just) talking to ourselves?

Posted on 3 August 2026 by dselz

Fifteen people in the room and I could not tell if we were ahead of the curve or just talking to ourselves.

The gathering was built around everything agentic, the informal kind where people who actually build the stuff show up to argue about what works. Counting the room afterward, in my head, the way you do, there was one woman, and only two of us bald or close enough to it, James and me. We know each other for a couple of years, done startups prior. James and I also carried something else the room split on without anyone naming it. We were the old hands, the ones with scar tissue from being wrong before. The younger, first time founders in the room ran on gung ho conviction, no scars yet to slow them down. Everyone talked over each other with the easy confidence of people who already agree.

That agreement is not evidence of anything. Research on homophily, the tendency of similar people to cluster, found the pattern structures nearly every kind of network tie there is, friendship, work, advice, who tells whom what. A room like ours is not a special case or a warning sign, it is the ordinary shape of any group that forms around a shared interest. The feeling of being surrounded by people who get it is not a signal. It is just what clustering looks like from inside the cluster.

History gives a cleaner test than my own memory of one evening. The Homebrew Computer Club met in Menlo Park through the seventies, a small, homogeneous group of hobbyists, and Steve Wozniak debuted the Apple I there in 1976. Long Term Capital Management was every bit as convinced and just as homogeneous, a fund run by two Nobel laureates in economics and a former Federal Reserve vice chairman. Nine months after the Nobel medals were handed out, the fund collapsed under about twenty five to one leverage, and the Fed had to organize fourteen banks to put up three point six billion dollars to contain it. Same conviction. Same sameness. One room built Apple. The other needed a bailout.

Neither the scar tissue nor the gung ho helped either room see itself clearly. Caution earned from past failure can mean you have learned something real, or it can mean you are simply too marked to move. A clean record can mean you are early enough that nothing has gone wrong yet, or it can mean you have not been tested at all. Both postures feel completely justified from the inside. Neither tells you anything about the room you are actually sitting in.

None of this proves agentic AI is a bubble or a genuine turn. Two historical examples are not a statistical case, they are a demonstration that conviction alone settles nothing. What actually distinguishes Homebrew from LTCM was never visible on the night itself. It only showed up later, in who outside the room started paying for what was being built.

That is the only test worth running. Not how sure the room feels, caution or conviction alike, but whether anyone outside that room is paying for the idea yet. I left that evening no more certain than I walked in. The room never tells you. Only the people spending money outside it do. If you have sat in a room like that and later found out which kind it was, tell me what told you.

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Everything turns on its head. Often a good thing.

Posted on 31 July 2026 by dselz

Banks like UBS spent years fighting the regulation that now protects them.

The complaint has a real basis. Capital rules limit how much a bank can earn and how big the bonus pool gets. In April 2026 the Swiss Government finalized its capital package, including the requirement that systemically important banks fully back their foreign subsidiaries with core equity. For UBS that means roughly 22 billion dollars of additional capital, down from the 26 billion first floated but still a significant number. UBS called the package extreme, said it lacked international alignment, and warned of consequences for the Swiss economy. From where they sit, that is rational. Capital sitting idle is money not earning a return.

Now the part that never makes the press release.

That same capital is the shock absorber. It soaks up losses when a bank runs into trouble, so that someone else does not have to. And we know exactly who that someone else is. It’s us. In March 2023 Credit Suisse was days from collapse. Swiss authorities put up close to 260 billion francs in liquidity and state guarantees to stop the failure from spreading. The taxpayer stood behind all of it. The country’s own post mortem concluded the too big to fail regime had to be strengthened to reduce the risk to the economy, the state, and the taxpayer. The 2026 rules are the direct answer to 2023. So UBS is fighting the mechanism built to make sure the taxpayer never has to write that check again. The thing being fought is also the shield.

That is irony number one. Irony number two is bigger.

AI has genuinely lowered the cost of building a bank. You can reach the market through banking as a service and a sponsor in a few months rather than years, and in 2023 the large majority of successful fintech launches used exactly that route rather than getting their own license. So the intuition is obvious. A new roster of banks at scale is coming, and the incumbents should be nervous.

Except the wall does not fall. It moves. A full banking license still runs into the tens of millions and takes 18 to 36 months, and every year the compliance requirements get heavier, not lighter. Europe already ran this experiment. Frameworks like GDPR and PSD2 raised the barrier in a way that favored incumbents with established legal machinery and made life harder for the newcomers. When a challenger did get dangerous, the incumbents had the balance sheet to simply buy it before it reached critical mass. Fintech founders now say it plainly themselves. Regulation is the moat, because doing the hard compliant work is what keeps the tourists out.

So follow the full loop. The incumbents complained the rulebook was strangling them. The rulebook made the system safer. And the same rulebook became the wall that protects the incumbents from the AI-era challengers now trying to climb over it. The thing they fought protects them twice. Everything turns on its head.

As always it’s not black and white: UBS did win real easing on the treatment of deferred tax and software assets, so this was not a clean defeat. Incumbents lobby, and they partly win. An industry commissioned study put the cost of the strictest version at up to 34 billion francs of Swiss GDP over ten years, so the rule is not free. And regulatory capture means “regulation protects incumbents” is sometimes not an accident but the quiet design. None of that breaks the pattern. It sharpens it.

Because the same reversal runs straight through startups, and this is where it should sting a little.

For a decade the gospel was blitzscaling. Hire faster than you can onboard. Raise bigger than you can spend. Burn a hundred million a quarter and wear it as a badge. It worked, but only because money was free. Then in March 2022 the Fed started raising rates, eleven times over. Funding fell off a cliff, late stage valuations were cut, the IPO window shut. Growth at all costs flipped to profit at all costs overnight. The exact behavior the loudest voices had cheered became the behavior that killed companies. The consensus did not fade. It inverted. And the people amplifying it were at their loudest right before the flip.

That is the lesson under all three stories. What “everyone knows is right” is a snapshot of one set of conditions, cheered by people who mistake the weather for the climate. Change the rates, change the technology, change the rules, and the safe consensus becomes the risk. The regulation that strangled you becomes the wall that shelters you. The strategy that made you a star becomes the one that sinks you.

So the question is not which side of today’s consensus you are on. It is harder than that. Many times over the course of a business cycle and life things turn into the exact opposite of what they initially looked like.

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When to trust your Gut, and when not to

Posted on 27 July 2026 by dselz

Early 2004, we were sketching what would become local.ch, and one call we made early was to put the map at the center of the whole experience. We bet on a small Lucerne company called Endoxon to supply it. Digital maps were rough back then, nobody had a good experience yet, and plenty of people told us a directory did not need to look like this.

Two years later, December 2006, Google bought the mapping half of Endoxon outright, to build the European coverage for Google Maps and Google Earth. The person who built the thing we bet on went on to run Google’s Maps team in Zurich for close to fifteen years afterward. Nobody told us we were right in 2004. Google told us in 2006, by buying the same company we already trusted.

It happened again, years later, at Squirro. We placed an early bet on retrieval augmented models, back when we called it retrieval augmented LLM, before the Americans found a catchier name for it and it became RAG. At the time the room’s honest question was, how could a technology that mostly hallucinates ever be trustworthy enough to replace search. We built on it anyway.

None of this would have survived a pitch deck. A deck argues for a future that has not happened yet, and by the time anyone can check whether it was right, the deck has already done its only job, raised the round or not. Investors know this too, ask any of them and they will tell you they do not expect your numbers to be accurate, only defensible. Nobody at the table is actually testing the claim. They are testing the argument.

What actually got tested, twice, was not a slide, it was thirty years of pattern recognition against reality. Research on when expert intuition can genuinely be trusted, not just believed, found it takes two things, a reasonably predictable environment, and a very large number of real decisions with real feedback, often described as tens of thousands of hours of it. Kahneman wrote his famous book on “thinking fast and slow” and won a Noble Prize for it.

The same research is just as clear about the other half. Outside that kind of feedback loop, in an unpredictable environment, confident intuition is often no better than a coin flip, sometimes worse, and it feels exactly the same from the inside either way. Two calls landing right is not proof the method never fails. It is evidence that, those two times, the conditions were actually there to trust it.

So here is the actual lesson, thirty years in. It taught me to trust my gut for some decisions, and never to trust it in others, and no pitch deck, mine or anyone else’s, will ever tell you which decision you are standing in. Only the years will.

If you have made a call like that, one that took years to get proven right by someone else’s decision, not your own, I would like to hear which decision you thought of in similar terms.

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Got Five Minutes? The Hidden Work Your Calendar Won’t Show

Posted on 10 July 2026 by dselz

A colleague came back from a trip years ago and stopped by my desk. Got five minutes, he said. I said yes, because that is what you say. He wanted to update me on a customer meeting, the kind of check in that usually takes exactly five minutes.

It turned out the customer had been quietly trying to build the same capability themselves, using know how they had picked up from working with us. Five minutes became the rest of the day. The customer relationship, the financial exposure, the strategic response, all of it, worked through in real time, none of it on my calendar that morning.

That is the part nobody tells you about running a company. You are the spider in the web, the one node everyone else routes through, and the actual shape of your week lives in the got five minutes, not the blocks your calendar shows.

It is not just a founder problem, and it is not solvable with better staffing. Harvard Business School tracked twenty seven CEOs around the clock for thirteen weeks, sixty thousand hours of data, every one of them with a full executive assistant managing the schedule. They still spent seventy two percent of their work time in meetings, and of the time nominally alone, fifty nine percent came in blocks under an hour. If a calendar assistant cannot protect a Fortune scale CEO from this, a founder without one has no chance, and does not need one to explain why.

The got five minutes moments are not a distraction from the job either. Research on structural holes, the gap between two people or groups who hold pieces of information neither one has alone, found that the person who sits at that junction sees problems earlier and gets their input weighted more, not less. Being the one everyone comes to is not a side effect of being a founder. It is close to the job description.

Here is the arithmetic that never makes it into any of this. Four or five got five minutes in a day is not four or five small interruptions, it is thirty minutes gone, and stacked across a full day it is three or four hours that never show up as a block anywhere, and that I never counted as work myself, even while I was doing it.

None of this is free. Refocusing after an interruption costs real time, one interruption study puts it at about twenty three minutes to fully return to what you were doing before. I am not going to pretend the interruptions come at no cost. The honest version is that the cost is real, and I pay it anyway, because the alternative is worse, a customer left waiting, a decision that sits stuck for three days instead of getting made in the hallway, a problem that grows quietly until it is too big to fix in an afternoon.

My calendar that day showed two meetings and a light afternoon. What actually happened was a customer relationship nearly unraveling, worked through standing at my colleague’s desk, decided before lunch. The calendar was not wrong about the time. It was wrong about the day.

If your calendar has ever looked emptier than your day felt, that gap is not a filing error. That is where the job actually is.

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“How True” or the Grind nobody sees

Posted on 9 July 2026 by dselz

On 20 March 2012 I read a paragraph by Chris Dixon. Rovio’s Angry Birds was their fifty second game. Pinterest called its first year catastrophically small. I posted the paragraph on my blog and added one line underneath it, “How true!”

I did not say what year I was actually in. By March 2012, Squirro was starting, built directly out of Memonic failing. Namics had already spent years clawing through what the dot com crash did to the agency business. Local.ch had already fought its way through a brutally competitive market to become the one that won. Three cycles of the same thing, two of them finished, one just beginning again, and all I wrote in public was two words agreeing that it takes a while.

Nobody needs convincing anymore that overnight success is a myth. Rovio’s fifty second game and Pinterest’s brutal first year are the two examples everyone already reaches for. CB Insights puts the real odds of a venture backed startup reaching a billion dollar outcome at about 1.28 percent. Everyone already knows the truth is grind, not luck on day one. That is not the confidential part of this story anymore. Saying so out loud is the safe thing to say.

The seemingly confidential part is what you do while you are still inside the grind. Research on entrepreneurship names this directly: The way many approach is “Fake it till you make it”. It is a documented, accepted norm in how some think startups get built. You claim traction before you have it because that is what gets you the next round, the next customer, the next hire. Until the fake catches up with reality. You never hear of those ones again.

The uncomfortable part is what never got closed afterward. Namics survived the crash. Local.ch won its market. Squirro took years after Memonic to become what Gartner calls the only European vendor with a notable GenAI offering. Three real outcomes. It’s the grind in-between that never gets much attention. And yes what Chris Dixon wrote “You tend to hear about startups when they are successful but not when they are struggling. This creates a systematically distorted perception that companies succeed overnight. Almost always, when you learn the backstory, you find that behind every “overnight success” is a story of entrepreneurs toiling away for years, with very few people except themselves and perhaps a few friends, users, and investors supporting them.” is today true as it was then.

If you have a post like mine sitting somewhere, one where you agreed with someone else’s startup truth, I would read it.

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Grey, Corporate, or Start Again

Posted on 7 July 2026 by dselz

Some years ago, after Namics and local.ch were both already working, we were finalists for a serious startup prize. The jury flew in the last three teams for interviews. One juror, I will call him Peter, was the right hand man at a large, successful IT consultancy. Peter spent most of the interview doubting we would survive (we do).

I turned the question around. Imagine we fail tomorrow, I said, and the team needs jobs. A team that had already built two outsize Swiss successes. Would you hire us.

Peter hesitated. Not long. A second too long. That second was the whole answer. No, he would not have hired us. Not because we lacked merit. Because a team like that walking into his hierarchy was a risk to his own comfortable seat.

The easy explanation for why founders start over is skill. Pattern recognition compounds, you get faster at spotting the right market and the right timing, and research on repeat founders backs this up, entrepreneurs who succeeded once are more likely to succeed again. But the same research is honest about the flip side. Founders coming off a shutdown do not get the same boost. They do worse than first timers, not better. So if you have had an ending that was not a win, and I have, skill is not the reason you go again. That story only applies to the version of you that already won.

The second explanation is more romantic. You are addicted to the thrill, wired for risk, chasing the next dopamine hit. A 2023 study on serial founders actually tested this, and the finding cuts the other way. For founders high in sensation seeking, the outcome of the last venture, win or lose, changes far less than the addiction story assumes. That is not addiction to winning. That is something that does not care about the scoreboard at all. Addiction is a flattering word. It makes the behavior sound uncontrollable and a little glamorous. It is actually closer to what is left after you rule out the alternatives.

Here is the third answer, the one nobody puts on LinkedIn. By your late forties or fifties, the world offers you two respectable exits. Go grey, maybe dressed up as advisory work or a seat on a few boards, comfortable, watching the game from the stands. Or go corporate, put your fourth company down as an interesting chapter and step into someone else’s hierarchy. That second door is the one you cannot actually walk through, and Peter’s half second told you why. Startups run on getting things done and telling the truth even when it costs you. Large hierarchies run on managing upward and protecting your own position. Those are not two flavors of the same job. They are opposite operating systems, and both sides can smell it in about the time it takes to hesitate before answering a question. Nobody here is being wronged. Two systems are recognizing each other accurately.

The one thing that is not true is that biology forces the choice. A study out of MIT and Northwestern found that the average founder behind the fastest growing one in a thousand new companies in America started at age 45. A 60 year old first time founder beats a 30 year old on the numbers. Nobody is being pushed toward the grey door by their body. They are being pushed there by a story about age that the data does not support.

So the honest answer is not skill and it is not thrill. Grey is a slow death by boredom. Corporate is a door that will not open for people built this way, and neither side is wrong for keeping it shut. Once you take both off the table, there is exactly one thing left to do. I am not starting company number five because I found my calling again. I am starting it because the other two doors are slower ways to die, and Peter taught me that a long time before I could put it into words.

If you are standing in that same hallway right now, mid career, weighing the same two doors, tell me which one you are stuck on.