Open Source Veteran Launches Skyseed - First Ecosystem Fund and Incubator for the Bluesky AT Protocol
Your "Per-Seat" Margin is My Opportunity
Traditional software is sold on a per seat subscription. More humans, more money. We are headed to a future where AI agents will replace the work humans do. But you can’t charge agents a per seat cost. So we’re headed to a world where software will be sold on a consumption model (think tasks) and then on an outcome model (think job completed) Incumbents will be forced to adapt but it’s classic innovators dilemma. How do you suddenly give up all that subscription revenue? This gives an opportunity for startups to win.
Per-seat pricing only works when your users are human. But when agents become the primary users of software, that model collapses.
Executives aren't evaluating software against software anymore. They're comparing the combined costs of software licenses plus labor against pure outcome-based solutions. Think customer support (per resolved ticket vs. per agent + seat), marketing (per campaign vs. headcount), sales (per qualified lead vs. rep). That's your pricing umbrella—the upper limit enterprises will pay before switching entirely to AI.
enterprises are used to deterministic outcomes and fixed annual costs. Usage-based pricing makes budgeting harder. But individual leaders seeing 10x efficiency gains won't wait for procurement to catch up. Savvy managers will find ways around traditional buying processes.
This feels like a generational reset of how businesses operate. Zero upfront costs, pay only for outcomes—that's not just a pricing model. That's the future of business.
The winning strategy in my books? Give the platform away for free. Let your agents read and write to existing systems through unstructured data—emails, calls, documents. Once you handle enough workflows, you become the new system of record.
Fast Crimes at Lambda School
How Perplexity builds product
inside look at how Perplexity builds product—which to me feels like what the future of product development will look like for many companies:AI-first: They’ve been asking AI questions about every step of the company-building process, including “How do I launch a product?” Employees are encouraged to ask AI before bothering colleagues.Organized like slime mold: They optimize for minimizing coordination costs by parallelizing as much of each project as possible.Small teams: Their typical team is two to three people. Their AI-generated (highly rated) podcast was built and is run by just one person.Few managers: They hire self-driven ICs and actively avoid hiring people who are strongest at guiding other people’s work.A prediction for the future: Johnny said, “If I had to guess, technical PMs or engineers with product taste will become the most valuable people at a company over time.”
Typical projects we work on only have one or two people on it. The hardest projects have three or four people, max. For example, our podcast is built by one person end to end. He’s a brand designer, but he does audio engineering and he’s doing all kinds of research to figure out how to build the most interactive and interesting podcast. I don’t think a PM has stepped into that process at any point.
We leverage product management most when there’s a really difficult decision that branches into many directions, and for more involved projects.
The hardest, and most important, part of the PM’s job is having taste around use cases. With AI, there are way too many possible use cases that you could work on. So the PM has to step in and make a branching qualitative decision based on the data, user research, and so on.
a big problem with AI is how you prioritize between more productivity-based use cases versus the engaging chatbot-type use cases.
we look foremost for flexibility and initiative. The ability to build constructively in a limited-resource environment (potentially having to wear several hats) is the most important to us.
We look for strong ICs with clear quantitative impacts on users rather than within their company. If I see the terms “Agile expert” or “scrum master” in the resume, it’s probably not going to be a great fit.
My goal is to structure teams around minimizing “coordination headwind,” as described by Alex Komoroske in this deck on seeing organizations as slime mold. The rough idea is that coordination costs (caused by uncertainty and disagreements) increase with scale, and adding managers doesn’t improve things. People’s incentives become misaligned. People tend to lie to their manager, who lies to their manager. And if you want to talk to someone in another part of the org, you have to go up two levels and down two levels, asking everyone along the way.
Instead, what you want to do is keep the overall goals aligned, and parallelize projects that point toward this goal by sharing reusable guides and processes.
Perplexity has existed for less than two years, and things are changing so quickly in AI that it’s hard to commit beyond that. We create quarterly plans. Within quarters, we try to keep plans stable within a product roadmap. The roadmap has a few large projects that everyone is aware of, along with small tasks that we shift around as priorities change.
Each week we have a kickoff meeting where everyone sets high-level expectations for their week. We have a culture of setting 75% weekly goals: everyone identifies their top priority for the week and tries to hit 75% of that by the end of the week. Just a few bullet points to make sure priorities are clear during the week.
All objectives are measurable, either in terms of quantifiable thresholds or Boolean “was X completed or not.” Our objectives are very aggressive, and often at the end of the quarter we only end up completing 70% in one direction or another. The remaining 30% helps identify gaps in prioritization and staffing.
At the beginning of each project, there is a quick kickoff for alignment, and afterward, iteration occurs in an asynchronous fashion, without constraints or review processes. When individuals feel ready for feedback on designs, implementation, or final product, they share it in Slack, and other members of the team give honest and constructive feedback. Iteration happens organically as needed, and the product doesn’t get launched until it gains internal traction via dogfooding.
all teams share common top-level metrics while A/B testing within their layer of the stack. Because the product can shift so quickly, we want to avoid political issues where anyone’s identity is bound to any given component of the product.
We’ve found that when teams don’t have a PM, team members take on the PM responsibilities, like adjusting scope, making user-facing decisions, and trusting their own taste.
What’s your primary tool for task management, and bug tracking?Linear. For AI products, the line between tasks, bugs, and projects becomes blurred, but we’ve found many concepts in Linear, like Leads, Triage, Sizing, etc., to be extremely important. A favorite feature of mine is auto-archiving—if a task hasn’t been mentioned in a while, chances are it’s not actually important.The primary tool we use to store sources of truth like roadmaps and milestone planning is Notion. We use Notion during development for design docs and RFCs, and afterward for documentation, postmortems, and historical records. Putting thoughts on paper (documenting chain-of-thought) leads to much clearer decision-making, and makes it easier to align async and avoid meetings.Unwrap.ai is a tool we’ve also recently introduced to consolidate, document, and quantify qualitative feedback. Because of the nature of AI, many issues are not always deterministic enough to classify as bugs. Unwrap groups individual pieces of feedback into more concrete themes and areas of improvement.
High-level objectives and directions come top-down, but a large amount of new ideas are floated bottom-up. We believe strongly that engineering and design should have ownership over ideas and details, especially for an AI product where the constraints are not known until ideas are turned into code and mock-ups.
Big challenges today revolve around scaling from our current size to the next level, both on the hiring side and in execution and planning. We don’t want to lose our core identity of working in a very flat and collaborative environment. Even small decisions, like how to organize Slack and Linear, can be tough to scale. Trying to stay transparent and scale the number of channels and projects without causing notifications to explode is something we’re currently trying to figure out.
Oh the Humanity
Looking for AI use-cases — Benedict Evans
- LLMs have impressive capabilities, but many people struggle to find immediate use-cases that match their own needs and workflows.
- Realizing the potential of LLMs requires not just technical advancements, but also identifying specific problems that can be automated and building dedicated applications around them.
- The adoption of new technologies often follows a pattern of initially trying to fit them into existing workflows, before eventually changing workflows to better leverage the new tools.
if you had showed VisiCalc to a lawyer or a graphic designer, their response might well have been ‘that’s amazing, and maybe my book-keeper should see this, but I don’t do that’. Lawyers needed a word processor, and graphic designers needed (say) Postscript, Pagemaker and Photoshop, and that took longer.
I’ve been thinking about this problem a lot in the last 18 months, as I’ve experimented with ChatGPT, Gemini, Claude and all the other chatbots that have sprouted up: ‘this is amazing, but I don’t have that use-case’.
A spreadsheet can’t do word processing or graphic design, and a PC can do all of those but someone needs to write those applications for you first, one use-case at a time.
no matter how good the tech is, you have to think of the use-case. You have to see it. You have to notice something you spend a lot of time doing and realise that it could be automated with a tool like this.
Some of this is about imagination, and familiarity. It reminds me a little of the early days of Google, when we were so used to hand-crafting our solutions to problems that it took time to realise that you could ‘just Google that’.
This is also, perhaps, matching a classic pattern for the adoption of new technology: you start by making it fit the things you already do, where it’s easy and obvious to see that this is a use-case, if you have one, and then later, over time, you change the way you work to fit the new tool.
The concept of product-market fit is that normally you have to iterate your idea of the product and your idea of the use-case and customer towards each other - and then you need sales.
Meanwhile, spreadsheets were both a use-case for a PC and a general-purpose substrate in their own right, just as email or SQL might be, and yet all of those have been unbundled. The typical big company today uses hundreds of different SaaS apps, all them, so to speak, unbundling something out of Excel, Oracle or Outlook. All of them, at their core, are an idea for a problem and an idea for a workflow to solve that problem, that is easier to grasp and deploy than saying ‘you could do that in Excel!’ Rather, you instantiate the problem and the solution in software - ‘wrap it’, indeed - and sell that to a CIO. You sell them a problem.
there’s a ‘Cambrian Explosion’ of startups using OpenAI or Anthropic APIs to build single-purpose dedicated apps that aim at one problem and wrap it in hand-built UI, tooling and enterprise sales, much as a previous generation did with SQL.
Back in 1982, my father had one (1) electric drill, but since then tool companies have turned that into a whole constellation of battery-powered electric hole-makers. One upon a time every startup had SQL inside, but that wasn’t the product, and now every startup will have LLMs inside.
people are still creating companies based on realising that X or Y is a problem, realising that it can be turned into pattern recognition, and then going out and selling that problem.
A GUI tells the users what they can do, but it also tells the computer everything we already know about the problem, and with a general-purpose, open-ended prompt, the user has to think of all of that themselves, every single time, or hope it’s already in the training data. So, can the GUI itself be generative? Or do we need another whole generation of Dan Bricklins to see the problem, and then turn it into apps, thousands of them, one at a time, each of them with some LLM somewhere under the hood?
The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.
Why Success Often Sows the Seeds of Failure - WSJ
Once a company becomes an industry leader, its employees, from top to bottom, start thinking defensively. Suddenly, people feel they have more to lose from challenging the status quo than upending it. As a result, one-time revolutionaries turn into reactionaries. Proof of this about-face comes when senior executives troop off to Washington or Brussels to lobby against changes that would make life easier for the new up and comers.
Years of continuous improvement produce an ultra-efficient business system—one that’s highly optimized, and also highly inflexible. Successful businesses are usually good at doing one thing, and one thing only. Over-specialization kills adaptability—but this is a tough to trap to avoid, since the defenders of the status quo will always argue that eking out another increment of efficiency is a safer bet than striking out in a new direction.
Long-tenured executives develop a deep base of industry experience and find it hard to question cherished beliefs. In successful companies, managers usually have a fine-grained view of “how the industry works,” and tend to discount data that would challenge their assumptions. Over time, mental models become hard-wired—a fact that makes industry stalwarts vulnerable to new rules. This risk is magnified when senior executives dominate internal conversations about future strategy and direction.
With success comes bulk—more employees, more cash and more market power. Trouble is, a resource advantage tends to make executives intellectually lazy—they start believing that success comes from outspending one’s rivals rather than from outthinking them. In practice, superior resources seldom defeat a superior strategy. So when resources start substituting for creativity, it’s time to short the shares.
One quick suggestion: Treat every belief you have about your business as nothing more than a hypothesis, forever open to disconfirmation. Being paranoid is good, becoming skeptical about your own beliefs is better.
AI startups require new strategies
comment from Habitue on Hacker News: > These are some good points, but it doesn't seem to mention a big way in which startups disrupt incumbents, which is that they frame the problem a different way, and they don't need to protect existing revenue streams.
The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others.
The hard-tech is a level playing field; startups do not have an advantage over incumbents.
There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.
This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required
Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial.
Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.
Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).
While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.
A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).
AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different
Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.
the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”
Tech has graduated from the Star Trek era to the Douglas Adams age
The Power of NPS in Your Product Strategy | Productboard
Writing on what to do with the data provided by product feedback survey questions like 'how likely are you to recommend our product to other people?"
Can technology’s ‘zoomers’ outrun the ‘doomers’?
Hassabis pointed to the example of AlphaFold, DeepMind’s machine-learning system that had predicted the structures of 200mn proteins, creating an invaluable resource for medical researchers. Previously, it had taken one PhD student up to five years to model just one protein structure. DeepMind calculated that AlphaFold had therefore saved the equivalent of almost 1bn years of research time.
DeepMind, and others, are also using AI to create new materials, discover new drugs, solve mathematical conjectures, forecast the weather more accurately and improve the efficiency of experimental nuclear fusion reactors. Researchers have been using AI to expand emerging scientific fields, such as bioacoustics, that could one day enable us to understand and communicate with other species, such as whales, elephants and bats.
Online daters love to hate on Hinge. 10 years in, it’s more popular than ever.
One key problem across the apps is the slog of self-presentation, or “impression management,” said Rachel Katz, a digital media sociologist who studies online dating at the University of Salford in the UK. “An important aspect of it is knowing your audience,” Katz said. On dating apps, you don’t know who exactly you’re presenting yourself to when picking a profile picture or composing your bio. You also don’t have physical cues that can help you adjust that self-presentation. “You’re trying to come up with something that’s generally appealing to people, but it can’t be too weird. It can’t be too unique,” said Bryce. “That’s partly why it’s exhausting,” Katz explains, “because it’s this constant labor. ... You’re not really sure of how to do it, you can’t just fit into a comfortable social role.”
When dating apps are not delivering on compatibility, Dean said, they are leading you to “believe that there’s a forever volume of people you can always like.”
Ury rejects the notion that apps should be asking people for more about themselves in writing or through extensive questionnaires. Users may match up on paper but end up disappointed in real life. “I would have rather that people understand that sooner by meeting up earlier,” she said. “Use the app as a matchmaker who gives you the matches — and then, as quickly as possible, the two of you should be chatting live to see if you are a match,” she said. “We found that three days of chatting is the sweet spot for scheduling a date.”
Deta Space
The Effective Altruism Shell Game 2.0
Criticism of the Effective Altruism movement and its griftiness
Snapchat, The Browser Company, and picking winning founders with Ellis Hamburger
Is the founder focused on a market opportunity, or a way that they want to change and improve our daily lives? It’s the difference between pitching the tool vs. the benefit. The best founders are always focused on the benefit—they’re putting themselves in the shoes of the consumer, instead of just building something because they can.
On how to identify a winning founder: “Great, thoughtful design. Great design tells you if the founder is focused, has good taste, understands the simplicity required to connect with the average consumer, and has a strong, specific point of view on what they’re building. It has always been my barometer. Great design is harder to identify than it sounds, though.”
Pmarchive · The only thing that matters
How these 20-somethings brought in over $1 million in a year with an Instagram-friendly app
The cult of Obsidian: Why people are obsessed with the note-taking app
Even Obsidian’s most dedicated users don’t expect it to take on Notion and other note-taking juggernauts. They see Obsidian as having a different audience with different values.
Obsidian is on some ways the opposite of a quintessential MacStories app—the site often spotlights apps that are tailored exclusively for Apple platforms, whereas Obsidian is built on a web-based technology called Electron—but Voorhees says it’s his favorite writing tool regardless.
Generative AI’s Act Two
This page also has many infographics providing an overview of different aspects of the AI industry at time of writing.
We still believe that there will be a separation between the “application layer” companies and foundation model providers, with model companies specializing in scale and research and application layer companies specializing in product and UI. In reality, that separation hasn’t cleanly happened yet. In fact, the most successful user-facing applications out of the gate have been vertically integrated.
We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on shaky ground: the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.
Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category). This means that users are not finding enough value in Generative AI products to use them every day yet.
generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. As our colleague David Cahn writes, “the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?”
Venture-backed startups are failing at record rates
Brex’s Second Act
BuildBetter AI | Make better product decisions 5x faster
"Technically speaking, it’s a semi-autonomous, model-agnostic, domain-trained, data-integrated AI assistant. Which is a fancy way of saying: it answers questions about your products, customers, or company, really, really well."
Spotify
Spotify dominates the music streaming industry with over 500 million monthly active users and 210 million paid subscribers, and is expanding into new areas like podcasts and audiobooks. The company aims to generate $100 billion in annual revenue by 2030 through expanding margins, increasing prices, and growing its userbase to 1 billion monthly active users. According to the author's analysis, Spotify represents a significant investment opportunity with a potential stock price increase of around 7 times by 2030.
How the Push for Efficiency Changes Us
Efficiency initiatives are all about doing the same (or more) with less.
And while sometimes that can be done purely through technology, humans often bear the brunt of efficiency initiatives.
When Zuckerberg says the organization is getting “flatter,” he means that more non-management workers will have to take on types of work—coordinating, synthesizing, communicating, and affective tasks—that managers used to do. For many, that means a significant intensification of a style of work that is not for everyone.
becoming more efficient and productive seems to hold positive moral value. It goes into the plus column on the balance sheet of your character. But this moral quality of efficiency acts to turn us each into a certain kind of person. Not just a certain kind of worker, but a certain kind of voter, parent, partner, mentor, and citizen.
Social theorist Kathi Weeks argues that the responsibilities we feel toward work—and I’ll add our responsibility specifically to efficiency and productivity—have “more to do with the socially mediating role of work than its strictly productive function.” In other words, the stories we tell about work and our relationships to it are actively creating our “social, political, and familial” stories and relationships, too.
A Year of Efficiency is bound to make shareholders happy. But what does it do to the humans who create the value those shareholders add to their portfolios?
A Year of Efficiency might mean you can fit in more social media posts, more podcast episodes, more emails, or even more products or services. But how do you feel at the end? How has your relationship with yourself changed? How has your relationship with others changed?
Who do you become when efficiency is your guiding principle?
It’s worth questioning the moral quality we assign to efficiency and productivity in our society is healthy, or even useful. And it’s worth asking whether efficiency and productivity are really the modes through which we want to relate to our partners, children, friends, and communities.
While I certainly won’t deny the satisfaction of learning how to do a task faster, I do think it’s worth interrogating the way efficiency comes to shape our lives.
Watershed: A Climate Pragmatist
“I can’t make products just for 41-year-old tech founders”: Airbnb CEO Brian Chesky is taking it back to basics
Of course, you shouldn’t discriminate, but when we say belonging, it has to be more than just inclusion. It has to actually be the proactive manifestation of meeting people, creating connections in friendships. And Jony Ive said, “Well, you need to reframe it. It’s not just about belonging, it’s about human connection and belonging.”And that was, I think, a really big unlock. The next thing Jony Ive said is he created this book for me, a book of his ideas, and the book was called “Beyond Where and When,” and he basically said that Airbnb should shift from beyond where and when to who and what?Who are you and what do you want in your life? And that was a part of the inspiration behind Airbnb categories, that we wanted people to come to Airbnb without a destination in mind and that we could categorize properties not just by location but by what makes them unique, and that really influenced Airbnb categories and some of the stuff we’re doing now.
Tony Hsieh and the Emptiness of the Tech-Mogul Myth
Opinion: I Interviewed for Andrew Yang’s Nonprofit. It Was Demoralizing.
“Adventure is out there!”: Opportunities in AI x Travel | Andreessen Horowitz
A Student's Guide to Startups
Most startups end up doing something different than they planned.
The way the successful ones find something that works is by trying
things that don't. So the worst thing you can do in a startup is
to have a rigid, pre-ordained plan and then start spending a lot
of money to implement it. Better to operate cheaply and give your
ideas time to evolve.
Successful startups are almost never started by one person. Usually
they begin with a conversation in which someone mentions that
something would be a good idea for a company, and his friend says,
"Yeah, that is a good idea, let's try it." If you're missing that
second person who says "let's try it," the startup never happens.
And that is another area where undergrads have an edge. They're
surrounded by people willing to say that.
Look
for the people who keep starting projects, and finish at least some
of them. That's what we look for. Above all else, above academic
credentials and even the idea you apply with, we look for people
who build things.
You need a certain
activation energy to start a startup. So an employer who's fairly
pleasant to work for can lull you into staying indefinitely, even
if it would be a net win for you to leave.
Most people look at a company like Apple and think, how could I
ever make such a thing? Apple is an institution, and I'm just a
person. But every institution was at one point just a handful of
people in a room deciding to start something. Institutions are
made up, and made up by people no different from you.
What goes wrong with young founders is that they build stuff that
looks like class projects. It was only recently that we figured
this out ourselves. We noticed a lot of similarities between the
startups that seemed to be falling behind, but we couldn't figure
out how to put it into words. Then finally we realized what it
was: they were building class projects.
Class projects will inevitably
solve fake problems. For one thing, real problems are rare and
valuable. If a professor wanted to have students solve real problems,
he'd face the same paradox as someone trying to give an example of
whatever "paradigm" might succeed the Standard Model of physics.
There may well be something that does, but if you could think of
an example you'd be entitled to the Nobel Prize. Similarly, good
new problems are not to be had for the asking.
real
startups tend to discover the problem they're solving by a process
of evolution. Someone has an idea for something; they build it;
and in doing so (and probably only by doing so) they realize
the problem they should be solving is another one.
Professors will tend to judge you by the distance between
the starting point and where you are now. If someone has achieved
a lot, they should get a good grade. But customers will judge you
from the other direction: the distance remaining between where you
are now and the features they need. The market doesn't give a shit
how hard you worked. Users just want your software to do what they
need, and you get a zero otherwise. That is one of the most
distinctive differences between school and the real world: there
is no reward for putting in a good effort. In fact, the whole
concept of a "good effort" is a fake idea adults invented to encourage
kids. It is not found in nature.
unfortunately when you
graduate they don't give you a list of all the lies they told you
during your education. You have to get them beaten out of you by
contact with the real world.
really what work experience refers to is not some
specific expertise, but the elimination of certain habits left over
from childhood.
One of the defining qualities of kids is that they flake. When
you're a kid and you face some hard test, you can cry and say "I
can't" and they won't make you do it. Of course, no one can make
you do anything in the grownup world either. What they do instead
is fire you. And when motivated by that
you find you can do a lot more than you realized. So one of the
things employers expect from someone with "work experience" is the
elimination of the flake reflex—the ability to get things done,
with no excuses.
Fundamentally the equation is a brutal one: you have to spend
most of your waking hours doing stuff someone else wants, or starve.
There are a few places where the work is so interesting that this
is concealed, because what other people want done happens to coincide
with what you want to work on.
So the most important advantage 24 year old founders have over 20
year old founders is that they know what they're trying to avoid.
To the average undergrad the idea of getting rich translates into
buying Ferraris, or being admired. To someone who has learned from
experience about the relationship between money and work, it
translates to something way more important: it means you get to opt
out of the brutal equation that governs the lives of 99.9% of people.
Getting rich means you can stop treading water.
You don't get money just for working,
but for doing things other people want. Someone who's figured that
out will automatically focus more on the user. And that cures the
other half of the class-project syndrome. After you've been working
for a while, you yourself tend to measure what you've done the same
way the market does.
the most important skill for a startup founder isn't a programming
technique. It's a knack for understanding users and figuring out
how to give them what they want. I know I repeat this, but that's
because it's so important. And it's a skill you can learn, though
perhaps habit might be a better word. Get into the habit of thinking
of software as having users. What do those users want? What would
make them say wow?