Vladimir Novikov retweeted
We're about to enter a period where not reading or writing becomes as ubiquitous as smoking once was. We can hope people will eventually realize this is bad for them, as they did with smoking. But this is far from certain; the cost may not be as obvious.
there is one universal truth:
you have to use emotion for acquisition & substance for retention.
you can apply it to products, dating women, whatever. the same damn principle applies everywhere: what gets someone interested is rarely if ever the same thing that makes them stay.
Offered my wife to allocate time for our side projects to consistently move from ideas to next steps. Curious about this venture
Vladimir Novikov retweeted
I'm leaving X for some time to focus on building.
But before I leave, I want to share some thoughts on where we are with AI right now.
If you are building with AI, then this is for you.
I wish I had at least 1% of @levelsio's reach already because I feel more builders need to hear this.
TLDR: Your skills and good taste matter a lot. Be the most ambitious you've ever been and use AI wisely.
Superintelligence is defined as systems exceeding all human capability across pretty much all domains imaginable, and especially strategic agency. Models like Fable 5 and K3 are superhuman in some domains like code generation and breadth of recall, but they are also subhuman in domains like long-horizon autonomy, physical-world interaction, sustained original research, good taste, etc.
We are directing them, and they remain constrained by us humans and institutions. They're not behind the steering wheel yet. The tool hasn't become our master yet. We're still the master over the tool.
The reality is, human actors of all kind, not just deeply technical ones, are wielding AI like a magic wand now, shaping the software economy at the speed of compute, throttled by their limited attention, human speed of expression and prompting.
You reading this, you are special. Special in your own unique and wonderful way. And what AI gives you is an amplifier to express yourself, your ideas, your dreams, your imagination, and your ambition more than you ever could before.
It seems unfair to us old-school coders who had to grind our ways through the dark coal mines, hiking through manual coding and debugging hell in order to create amazing software systems and apps. It's frustrating as to the moon and back to see your skills become irrelevant so fast, at least if you believe the narrative that you've wasted the better part of your life acquiring them.
It is true that there is a certain, quite strong homogenization effect. Millions of people prompting to replicate or iterate on what's known must inevitably lead to a lot of overlap with structurally low diversity. AI produces convergent styles. It's in its nature, like that one designer doing all the designs for everything. In the same way we are also all starting to sound the same, being influenced by AIs way of expressing thought.
X, and everyone's own social circle or audience, produces selection bias. It's skewing the reality we perceive based on what the people in our feed and around us talk about or show us. So we tend to oversample trend-chasing indie apps and undersample deep tech systems, enterprise systems, research tooling or domain-specific work that flies under the radar, below the clouds of hype or algorithmic push.
Apps that took a year to make now take mere hours, it seems. It is both true and false at the same time. Superficially it is true because you get something that appears to behave like an app that was meticulously crafted over the course of an entire year by a talented engineer or even a whole team. What took so long can now be scaffolded with ease, by anyone. Engineers, chefs, strippers. Even our non-technical partners, friends and parents. Great.
But when we dig deeper, the truth is that complex products require reliability engineering, security, compliance, integrations, support and so much more that in the end, even with this powerful AI we have now to help us go from A to B through something like an Einstein-Rosen-Bridge warping space and time, things take their good amount of time to get right. Less than they did before, all things equal, but not mere hours.
Your skill is still highly relevant because AI amplifies you. It gives you leverage. The widespread idea that AI renders skill irrelevant doesn't compute, because either we have output quality that varies, in which case skill still differentiates, or it doesn't vary at all. And if it doesn't vary at all, the concept of "better" is meaningless. In other terms: A quality gradient can't exist in a flattened distribution. This is essentially your mathematical proof right there that your skill is in fact highly relevant.
What AI did is, it raised the floor dramatically. It also raised the ceiling, but not as much as the floor. This somewhat compresses the skill relevance gradient, but it doesn't eliminate it in any meaningful way.
When was the last time that access to powerful tools has resulted in broadly equal results? Never.
There was a time you needed to have a degree in chemistry or something in order to be able to take and develop photos. My grandfather, a scientist, used to have a laboratory for that. Taking and then developing pictures required immense skill.
Then one day digital cameras came along. Now any fool could take pictures, faster and easier than ever, thousands a day in full color or 3D even, instead of just 10 in monochrome. And yet, we all know that some people routinely take amazingly awe-inspiring photos, National Geographic front cover style that make us pause and look, while most others take tens to hundreds of photos a day that just end up clogging up our cloud drives.
We all have access to the same English language, but not everyone writes equally well. The equalization applies to the generation layer that is commoditized, but not to our taste, judgement, distribution, trust, or timing.
AI smashed through barriers to entry and brought them down like the Berlin wall. Now competition floods the market and drives economic profit toward zero in the layer that got commoditized, that is, the generation layer. Margins gravitate to zero, but not equally everywhere.
So what is it that survives this kind of "commoditization of everything"? What do we do, if we drink this snake oil? The classic sets of moats persist. Distribution, brand, trust, network effects, proprietary data, switching costs, regulatory position, capital intensity, and any kinds of significant embeddedness.
Yes, anyone can start using Claude Code & co and copy some app's code in an hour just based on screenshots. But it doesn't copy its user base, proprietary data in the cloud, its trust, its integrations or anything like that.
There is a sense that AI forces us to rush, because everyone is working so fast now. Well, the race was always on. So how about speed-to-trend? It's a temporary edge at best, but not a long-term differentiator. The fruits that hang low are just being picked faster overall, by more hunters and gatherers searching for them.
There is a widespread belief that all the value is going to accrue to the frontier AI companies in the end, who are the ones selling this immense power to everyone else.
In fact it's a bit sad to see all these non-influencer vibe coders raising their hopes for nothing, buying the picks and shovels from the AI frontier labs to go dig for gold. It's like watching 100 ducks fight for a small handful of breadcrumbs, and it's funny until you realize they are essentially fighting for their survival. That's not how the world should be, right?
Well, in reality the frontier AI companies spend astronomical amounts of money on research and development, including things like AI infrastructure and energy, training and inference, etc.
They themselves are essentially ducks in the lake, fighting for their survival for breadcrumbs. They suffer competitive commoditization pressure as much as we do, just at a different level. At the model layer, rather than the generation layer.
Structurally the winners are consumers and users who capture the surplus when production costs fall to the ground, but that of course doesn't really help us builders much, at least not those who have to make a living off of it.
And, let's be real. It may seem funny to have AI spit out an app that someone else previously spent an entire year on creating meticulously by hand, and then upload that to compete. The opportunity is short-lived, and the cost of opportunity a lot are missing is this:
The real gold rush is not that now you can make an app in a few hours instead of a year. The non-obvious that will become common sense soon is that an app that takes 2 hours to make is worth -5$ minus the value of your time + the value of demand which is likely $0 unless you have distribution, which you then burn with slop.
The real opportunity that AI has opened up is WIELDING ENORMOUS COMPLEXITY x EXCELLENCE.
Just imagine for a moment what you could accomplish, if only you would take this new superpower, your amazingly valuable skills, your great taste, your power of imagination, and do something that is outrageously AMBITIOUS! Something that makes others think you must be absolutely mental to even think for a second that it could be accomplished. And then go and work for a full year on just that, utilizing AI to the fullest extent possible. Milking the beast until its dry.
What will that be? No, not another Bumble clone. Not another weather app. For AI's sake, please, not an "app" at all. But something that the world actually needs. Think about it! Think bigger! And when you thought you've thought bigger, think even bigger. You're still aiming too low. THINK BIGGER!
Here is my bucket list of things I want to accomplish before I die.
- A platform that solves the number one root cause of poverty for good, giving everyone a fair chance at living a good life free of financial worries (it's far more than a "platform").
- A floating city in international waters, driven and governed by AI under a human charter (capital intensive, long story, and yes it will be fundable and doable)
- A digital twin of Earth for climate analytics, climate communications and Earth sciences education (the world's most advanced nature simulation by far, unlike anything you've ever seen or heard of)
- Truly reliable, explainable AI that can reason through complex systems and across extremely vast design spaces without stalling under the pressure of combinatorial explosion, laying the foundation for generative engineering so we can work on great and complex things like the USS Voyager and other incredible things far beyond today's vibe coding
- Something so insane, they would lock me up just for saying it out loud
A good heuristic: If talking about it doesn't make you sound like a complete lunatic, and building it doesn't scare you, you're probably aiming too low and the thing you're about to do will be commoditized rapidly (or is already).
Gone building. Back when there's something to show.
Like good odds I'm wrong but I wanted to write this down:
It's pretty clear to me that superintelligence is here and it's more powerful than us and it's moving where things are going now, not humans anymore
I don't see many people realize this yet, it feels like that pic I posted the other day, everyone is running after the same carrot which is the AI, thinking they're special, and their work is special and their use of AI is special, but it's really not, we're all mostly making the same slop, I mean it's nice slop, useful slop but everyone is making the same slop
And because it's so fast to make things, like people used to spend a year on just building an app, now it's done in hours, it's such an immense change, people send me their projects and it all looks the same, it's useful but it's slop
Non-technical people (e.g. the gfs) are now building the same or better things than technical people like us
So AI has made everybody is just as capable as everyone else, it equalized everyone in the world, as in everyone can make everything (software, music, images, art etc) now and everyone is equal, at least in the digital realm now
And maybe now we're in some odd transitionary time where we have to find out waht the next differentiator is as coding/building/execution isn't one anymore for sure
I thought it'd be distribution but who knows, obviously creativity and ideas, but if you can copy a successful apps in an hour, then how does that differentiating work?
A guy on here @aporia9n wrote how he increasingly meets founders who blast through apps/products/startups, kind of money grabs, very quickly jumping on a trend, building super quickly with AI, make lots of money quick, everyone copies them, then their margins go to 0%, and they shut it down and go to the next thing, in a way they found one differentiator which is speed
That's one way to do it, but it shows how radically things are changing I think
For now I think the only ones winning is the AI superintelligence itself and the companies providing the AI
Vladimir Novikov retweeted
As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:
1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales
Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.
A healthy team needs a mix of these, depending on the product:
- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2
Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?
Vladimir Novikov retweeted
The most important component of writing clearly is simply to have high standards for clarity. Then if you write something unclear, you notice, and ask: what did I mean to say? You can just keep doing this over and over. And if you have high standards for clarity, you will.
Vladimir Novikov retweeted
It all boils down to:
1. Providing the best tools to create content
2. Incentivizing the highest quality content
3. Ranking that content so it finds its audience
Nothing else matters.
AI. Work. Care.
why in 2026 we need to care more than ever before
vovanovaque.substack.com/p/t…
Vladimir Novikov retweeted
Solarpunk is a movement that imagines a sustainable and optimistic future where humanity thrives in harmony with nature.
Vladimir Novikov retweeted
Hamming's talk is so important that I reproduced it on my site. It's one of the only things on my site written by someone else.
paulgraham.com/hamming.html
A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work.
His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing.
In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen.
Here's the framework that has been quoted by every serious scientist for the last 40 years.
His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired.
He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow.
The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one.
The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed.
The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else.
The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices.
He finished the lecture with a line I have never been able to shake.
He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day.
The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword.
Hamming died in 1998. He gave his final lecture a few weeks before. He was 82.
The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.
Vladimir Novikov retweeted
Brands I love: Lego, Leuchtturm, Oxford University Press, Pentel, Schöffel, Aqualung, Paradores, Staedtler, Birkenstock, Braun, Knoll, Patagonia, Herman Miller, Iittala, L.A. Burdick, Artemide, Aman, Thames & Hudson, Yeti, Rimowa, L.L.Bean, Timbuk2, Eschenbach, Ridge, Maui Jim.
how to build a bootstrapped startup without funding:
1. pick a problem you personally have. if you don't use your own product daily, quit now
2. skip the pitch deck. open your code editor. ship something ugly in a weekend
3. charge money from day 1. free users give you nothing but support tickets
4. use boring tech. PHP, SQLite, vanilla JS. frameworks are a trap that mass waste your time
5. host on cheap VPS ($5-20/mo). not AWS. you don't need kubernetes for 1,000 users
6. do customer support yourself. it's the fastest product feedback loop that exists
7. automate everything you do more than twice. cron jobs > employees.
8. grow on Twitter/X by building in public. your journey IS the marketing
9. keep your burn rate near zero so you never need to raise. ramen profitable > series A
10. say no to investors, cofounders, and "advisors" who want equity for intros
i've been doing this for 10+ years now. no employees, no funding, no board meetings
the entire VC game is designed to make you think you need permission to start
you don't
Vladimir Novikov retweeted
An important, and perenially underrated, aspect of "trustlessness", "passing the walkaway test" and "self-sovereignty" is protocol simplicity.
Even if a protocol is super decentralized with hundreds of thousands of nodes, and it has 49% byzantine fault tolerance, and nodes fully verify everything with quantum-safe peerdas and starks, if the protocol is an unwieldy mess of hundreds of thousands of lines of code and five forms of PhD-level cryptography, ultimately that protocol fails all three tests:
* It's not trustless because you have to trust a small class of high priests who tell you what properties the protocol has
* It doesn't pass the walkaway test because if existing client teams go away, it's extremely hard for new teams to get up to the same level of quality
* It's not self-sovereign because if even the most technical people can't inspect and understand the thing, it's not fully yours
It's also less secure, because each part of the protocol, especially if it can interact with other parts in complicated ways, carries a risk of the protocol breaking.
One of my fears with Ethereum protocol development is that we can be too eager to add new features to meet highly specific needs, even if those features bloat the protocol or add entire new types of interacting components or complicated cryptography as critical dependencies. This can be nice for short-term functionality gains, but it is highly destructive to preserving long-term self-sovereignty, and creating a hundred-year decentralized hyperstructure that transcends the rise and fall of empires and ideologies.
The core problem is that if protocol changes are judged from the perspective of "how big are they as changes to the existing protocol", then the desire to preserve backwards compatibility means that additions happen much more often than subtractions, and the protocol inevitably bloats over time. To counteract this, the Ethereum development process needs an explicit "simplification" / "garbage collection" function.
"Simplification" has three metrics:
* Minimizing total lines of code in the protocol. An ideal protocol fits onto a single page - or at least a few pages
* Avoiding unnecessary dependencies on fundamentally complex technical components. For example, a protocol whose security solely depends on hashes (even better: on exactly one hash function) is better than one that depends on hashes and lattices. Throwing in isogenies is worst of all, because (sorry to the truly brilliant hardworking nerds who figured that stuff out) nobody understands isogenies.
* Adding more _invariants_: core properties that the protocol can rely on, for example EIP-6780 (selfdestruct removal) added the property that at most N storage slots can be changedakem per slot, significantly simplifying client development, and EIP-7825 (per-tx gas cap) added a maximum on the cost of processing one transaction, which greatly helps ZK-EVMs and parallel execution.
Garbage collection can be piecemeal, or it can be large-scale. The piecemeal approach tries to take existing features, and streamline them so that they are simpler and make more sense. One example is the gas cost reforms in Glamsterdam, which make many gas costs that were previously arbitrary, instead depend on a small number of parameters that are clearly tied to resource consumption.
One large-scale garbage collection was replacing PoW with PoS. Another is likely to happen as part of Lean consensus, opening the room to fix a large number of mistakes at the same time ( youtube.com/watch?v=10Ym34y3… ).
Another approach is "Rosetta-style backwards compatibility", where features that are complex but little-used remain usable but are "demoted" from being part of the mandatory protocol and instead become smart contract code, so new client developers do not need to bother with them. Examples:
* After we upgrade to full native account abstraction, all old tx types can be retired, and EOAs can be converted into smart contract wallets whose code can process all of those transaction types
* We can replace existing precompiles (except those that are _really_ needed) with EVM or later RISC-V code
* We can eventually change the VM from EVM to RISC-V (or other simpler VM); EVM could be turned into a smart contract in the new VM.
Finally, we want to move away from client developers feeling the need to handle all older versions of the Ethereum protocol. That can be left to older client versions running in docker containers.
In the long term, I hope that the rate of change to Ethereum can be slower. I think for various reasons that ultimately that _must_ happen. These first fifteen years should in part be viewed as an adolescence stage where we explored a lot of ideas and saw what works and what is useful and what is not. We should strive to avoid the parts that are not useful being a permanent drag on the Ethereum protocol.
Basically, we want to improve Ethereum in a way that looks like this:
My mental and marketing MBA @garyvee
Looking forward to 2026 Podcasts
Vladimir Novikov retweeted
one major problem for a lot of crypto protocols: they're willing to throw unfathomable sums of money towards one-time marketing events but are often too overwhelmed to do recurring work like tweets, reels, or contests
this is why i'm proposing a new solution for this problem:
it's called work-to-earn. protocols pay people money, and in return, they do work for the protocol.
who's building this?




















