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Minimum Delightful Product — sai
Minimum Delightful Product — sai
In today's AI-driven world, creating user delight is not just an add-on but a crucial competitive advantage
I find myself rethinking "minimum". Instead of asking, What's the least we can do to launch? I'm asking, What's the least we can do to make people love this?
Half-baked functionality is not enough in an age where AI accelerates the product development lifecycle—people want experiences that feel intuitive, engaging, and yes, delightful.
Sometimes, it's the smallest things—a clever animation, seamless usability, or a thoughtful touch—that leave a lasting impression. An MDP isn't about perfection; it's about ensuring even the simplest version of a product creates joy. In a world of endless options, delight isn't a bonus; it's a competitive advantage.
#ideals#product design#startups#product strategy#design#product development
·article.app·
Minimum Delightful Product — sai
Your "Per-Seat" Margin is My Opportunity
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.
#trends#business/models#business/strategy#product design#opinion#ai#LLMs#tech#startups
·writing.nikunjk.com·
Your "Per-Seat" Margin is My Opportunity
Inside the Collapse of Venture for America
Inside the Collapse of Venture for America
In the beginning, VFA was an institution beloved by many of its fellows. “It was a wonderful way to leave college and enter the real world because you’re surrounded by a community and there’s support from the organization,” says Jamie Norwood, co-founder of feminine hygiene brand Winx Health. Norwood and her co-founder, Cynthia Plotch, are a VFA success story. They met as fellows in 2015 and VFA eventually helped them launch their company with a grant and advisement. “We always say, Winx Health would not be here without VFA,” Norwood says.
Norwood and Plotch went through the standard VFA admissions protocol, which was rigorous. It required two written applications, a video interview, and in-person interviews at an event called “Selection Day,” many of which were held in New York City and Detroit over the years. By the end of each university term in May, accepted fellows would get access to Connect, VFA’s job portal, and have until November to land a job. For each fellow hired in a full-time job, VFA received a $5,000 placement fee, paid by partner companies. This fee became a crucial revenue stream for the organization—effectively wedding the professional success of its fellows to its bottom line.
Selection Day interviews were conducted by judges who often pitted interviewees against each other. Candidates were told to organize themselves in order of least to most likely to be successful, or according to whose answers had the most value per word. The format felt ruthless. “People cried” during the interview process, Plotch remembers.
The problems with the business bled into the fellows’ experience in 2023 and 2024, leaving them disenchanted, financially struggling, or expelled en masse from the program for reasons they believe were beyond their control. Despite a multitude of financial red flags, VFA leadership still insisted on recruiting for the 2024 class. “The talent team was traveling nonstop, using prepaid Visa cards since the corporate cards didn’t work,” explains a former director who worked closely with fellows.
Onboarding fresh recruits became increasingly crucial if VFA was going to survive. The organization asked companies for placement fees upfront in 2023, according to internal VFA documents and conversations with former employees. The policy change gave companies pause. Fewer companies signed up as partners, meaning fellows weren’t getting jobs and VFA was losing money.
In the spring of 2023, “there were 15 jobs on opening day,” for a class that eventually grew to over 100 fellows, the former director explains. Gabriella Rudnik, a 2023 fellow, estimates that when training camp began in July 2023, less than half of her peers had jobs, “whereas in previous years it would be closer to like 80 percent.”
Fellows were made to pay the price for the shortage of companies partnering with VFA in 2023. “We weren’t getting more jobs on Connect, and that’s what led to so many fellows being off-boarded,” explains a former director who worked closely with fellows.
Traditionally, VFA gave fellows a deadline of November of their class year to find a job, which typically meant a few stragglers were given extra help to find a position if they were late. In those rare cases during earlier years, fellows were offboarded by the organization, a former director says.
In previous years, expulsion was a much more serious and infrequent occurrence. “Removal from the fellowship was not something done lightly. During my tenure, we instituted an internal investigation process, similar to an HR investigation,” says the former executive who worked at VFA from 2017-20.  In total, at least 40 fellows from the 2023 class were expelled for failing to get jobs that weren’t available, according to research by former VFA fellows who tracked the number of fellows purged from a Slack channel. Records of their participation were removed from the VFA website, the fellows say.
Many fellows had made sacrifices to be part of the highly selective and prestigious VFA, which cited acceptance rates of around 10 percent of applicants. “There were fellows who turned down six-figure jobs to be a part of this program, and were told that the program that Andrew Yang started would live up to its reputation,” says Paul Ford, a 2024 fellow.
Though internal documents show that VFA was slowly imploding for months, in all external communications with fellows, the nonprofit still maintained that 2024 training camp would take place in Detroit.
“From an ethical perspective, it does reek of being problematic,” says Thad Calabrese, a professor of nonprofit management at New York University. “You entered into an arrangement with people who don’t have a lot of money, who believed that you were going to make them whole. Then you’re going to turn around and not make them whole.”
#trends#capitalism#economy#business#startups#VFA#profiles#bts#vc
·archive.is·
Inside the Collapse of Venture for America
The art of the pivot, part 2: How, why and when to pivot
The art of the pivot, part 2: How, why and when to pivot
people mix up two very different types of pivots and that it’s important to differentiate which path you’re on: Ideation pivots: This is when an early-stage startup changes its idea before having a fully formed product or meaningful traction. These pivots are easy to make, normally happen quickly after launch, and the new idea is often completely unrelated to the previous one. For example, Brex went from VR headsets to business banking, Retool went from Venmo for the U.K. to a no-code internal tools app, and Okta went from reliability monitoring to identity management all in under three months. YouTube changed direction from a dating site to a video streaming platform in less than a week. Hard pivots: This is when a company with a live product and real users/customers changes direction. In these cases, you are truly “pivoting”—keeping one element of the previous idea and doubling down on it. For example, Instagram stripped down its check-in app and went all in on its photo-sharing feature, Slack on its internal chat tool, and Loom on its screen recording feature. Occasionally a pivot is a mix of the two (i.e. you’re pivoting multiple times over 1+ years), but generally, when you’re following the advice below, make sure you’re clear on which category you’re in.
When looking at the data, a few interesting trends emerged: Ideation pivots generally happen within three months of launching your original idea. Note, a launch at this stage is typically just telling a bunch of your friends and colleagues about it. Hard pivots generally happen within two years after launch, and most around the one-year mark. I suspect the small number of companies that took longer regret not changing course earlier.
ou should have a hard conversation with your co-founder around the three-month mark, and depending on how it’s going (see below), either re-commit or change the idea. Then schedule a yearly check-in. If things are clicking, full speed ahead. If things feel meh, at least spend a few days talking about other potential directions.
Brex: “We applied to YC with this VR idea, which, looking back, it was pretty bad, but at the time we thought it was great. And within YC, we were like, ‘Yeah, we don’t even know where to start to build this.’” —Henrique Dubugras, co-founder and CEO
#business#business/strategy#startups#advice#work/productivity#examples#profiles#case-study
·lennysnewsletter.com·
The art of the pivot, part 2: How, why and when to pivot
Spreadsheet Assassins | Matthew King
Spreadsheet Assassins | Matthew King
Rhe real key to SaaS success is often less about innovative software and more about locking in customers and extracting maximum value. Many SaaS products simply digitize spreadsheet workflows into proprietary systems, making it difficult for customers to switch. As SaaS proliferates into every corner of the economy, it imposes a growing "software tax" on businesses and consumers alike. While spreadsheets remain a flexible, interoperable stalwart, the trajectory of SaaS points to an increasingly extractive model prioritizing rent-seeking over genuine productivity gains.
As a SaaS startup scales, sales and customer support staff pay for themselves, and the marginal cost to serve your one-thousandth versus one-millionth user is near-zero. The result? Some SaaS companies achieve gross profit margins of 75 to 90 percent, rivaling Windows in its monopolistic heyday.
Rent-seeking has become an explicit playbook for many shameless SaaS investors. Private equity shop Thoma Bravo has acquired over four hundred software companies, repeatedly mashing products together to amplify lock-in effects so it can slash costs and boost prices—before selling the ravaged Franken-platform to the highest bidder.
In the Kafkaesque realm of health care, software giant Epic’s 1990s-era UI is still widely used for electronic medical records, a nuisance that arguably puts millions of lives at risk, even as it accrues billions in annual revenue and actively resists system interoperability. SAP, the antiquated granddaddy of enterprise resource planning software, has endured for decades within frustrated finance and supply chain teams, even as thousands of SaaS startups try to chip away at its dominance. Salesforce continues to grow at a rapid clip, despite a clunky UI that users say is “absolutely terrible” and “stuck in the 80s”—hence, the hundreds of “SalesTech” startups that simplify a single platform workflow (and pray for a billion-dollar acquihire to Benioff’s mothership). What these SaaS overlords might laud as an ecosystem of startup innovation is actually a reflection of their own technical shortcomings and bloated inertia.
Over 1,500 software startups are focused on billing and invoicing alone. The glut of tools extends to sectors without any clear need for complex software: no fewer than 378 hair salon platforms, 166 parking management solutions, and 70 operating systems for funeral homes and cemeteries are currently on the market. Billions of public pension and university endowment dollars are being burned on what amounts to hackathon curiosities, driven by the machinations of venture capital and private equity. To visit a much-hyped “demo day” at a startup incubator like Y Combinator or Techstars is to enter a realm akin to a high-end art fair—except the objects being admired are not texts or sculptures or paintings but slightly nicer faces for the drudgery of corporate productivity.
As popular as SaaS has become, much of the modern economy still runs on the humble, unfashionable spreadsheet. For all its downsides, there are virtues. Spreadsheets are highly interoperable between firms, partly because of another monopoly (Excel) but also because the generic .csv format is recognized by countless applications. They offer greater autonomy and flexibility, with tabular cells and formulas that can be shaped into workflows, processes, calculators, databases, dashboards, calendars, to-do lists, bug trackers, accounting workbooks—the list goes on. Spreadsheets are arguably the most popular programming language on Earth.
#trends#saas#metrics v humanities#software#subscriptions#file management#ethics#corporate#capitalism#vc#startups
·web.archive.org·
Spreadsheet Assassins | Matthew King
Maven
Maven

Maven is a new social network platform that aims to provide a different experience from traditional social media.

  • It does not have features like likes or follower counts, focusing instead on users following "interests" rather than individual accounts.
  • Content is surfaced based on relevance to the interests a user follows, curated by AI, rather than popularity metrics.
  • The goal is to minimize self-promotion and popularity contests, instead prioritizing valuable information and serendipitous discovery of new ideas and perspectives.
  • The author has been using Maven and finds it a slower, deeper experience compared to other social media, though unsure if it will become a regular timesink.
  • Overall, Maven presents an intriguing alternative model for social networking centered around interests and expanding horizons, rather than following individuals or chasing popularity.
Niche InternetNiche Internet#social media#ideals#product page#startups#platforms
·heymaven.com·
Maven
the rogue investor's guide to venture
the rogue investor's guide to venture
Many people try out careers in venture and then wind up leaving after a year when it stops feeling novel and starts feeling like they’re floating in lonely limbo without any markers of success. That’s because the craft of venture is not for people who derive their satisfaction from external indicators of progress — it’s for people who find the development of their relationships and refinement of their internal model of the world to be motivation enough to keep going.
If you derive satisfaction from refining a craft, don’t go into venture yet.
Here are five individual investor archetypes I’ve noticed can produce outsized returns in the early-stage venture game:Philosopher: hangs one’s reputation on their predictions about the futureHustler: simply outwork everyone else and are great at networkingHawk: most competitive, gets a thrill out of the fight to win a deal Friend: confidante and coach to founders, often founders’ first callCelebrity: a person widely respected for their work/knowledge/skill
A good archetype for you is whichever one you can sustain the longest. A great archetype for you is one that no one else is doing and you have some sort of signal works.
Some good advice I got: build your fund’s structure and strategy around allowing yourself to invest in whichever way you most enjoy and are naturally good at (admittedly, it will probably get harder and harder to stick to this as your fund scales).
Examples: if you like being a friend to founders and want your fund to function as Switzerland (i.e. not compete with anyone), write small checks. If you like to fight and are naturally hawkish, it might make sense to set yourself up to try to lead rounds. If complex problems and futuristic theories are what get you excited, investing in series A companies that fit into where you see the world going could be quite gratifying. If for some reason you love living in spreadsheets, consider growth investing (and don’t follow literally any of my advice).
In many ways, the job of the writer and the job of a VC are quite similar, in that they both ask you to produce an original end product (in the writer’s case, articulated ideas and stories; in the investor’s case, a differentiated portfolio with outsized financial returns) without much of a map for how you get there. The reason professional writers complain about writing so much is that it’s really difficult to wrangle your brain into producing uniquely interesting thoughts all the time, and highly frustrating when you consider it your job to do so. Making good investment decisions is similar; just with the added element of also being highly social. Taking the quality of your self talk seriously seems superfluous but is an investment that will result in better decisions.
A lot of the game of investing is won by getting people to think of you — a sign that you’ve built the kind of moat we call a strong brand. Remember: a fund is just a pile of money with a person on top to sell it. As an investor, putting down stakes in the ground about what you invest in saves you a lot of time in the long run because it allows people to self-select for fit
I’ve been surprised by how much it’s benefited my fund to make Moth’s brand (i.e. what I invest in and look for) difficult to summarize in a sentence. For small early-stage generalist funds like my own, quality matters much more than quantity. Quality deals almost always come from trusted sources who resonate with my taste, not from a list of random companies for which I have no context.
A brand is a promise to show up in the same way time and time again. Good brands are built on being decent and principled with all of the people you interact with.
Lastly and of utmost importance: remember that fear of failure fades into the background if you focus on leaving everyone you encounter along the way better than you found them.
#vc#craftsmanship#startups#business#advice#funding#branding#brand strategy#ambition
·mothfund.substack.com·
the rogue investor's guide to venture
the earnest ambitious kid's guide to investors
the earnest ambitious kid's guide to investors
  1. Fundraising is brain damage, so spend as little time doing it as possible
  2. Create an alter ego who you don for fundraising purposes
  3. Don’t spend a lot of time with VCs if you don’t need VC $
  4. Only talk to investors with decision-making power, preferably angels
  5. You know more about your business & domain than 90% of investors
  6. Momentum matters and sequencing is smart
  7. People don’t belong on pedestals
  8. Beware of intellectual dementors and clout demons
  9. People will help you if you ask for what you want clearly and concisely
VCs need to believe that your company could be a billion-dollar business and generally lack imagination — you need to paint a vivid picture of this path for them, starting with the striking protagonist character you play in your company’s story.Your alter ego should never lie, but it should be completely comfortable showing the fullest expression of your ambition to people who probably intimidate you. Fundraising is a snap judgment game — most VCs are trying to pattern-match you to a founder archetype who already won. They index primarily on IQ, self-belief, experience, and personability (in that order). A general rule of thumb is that to be taken seriously in SV, male founders would benefit from acting warmer, while female founders are taken more seriously when they act colder. Both benefit from acting a little entitled.
a VC’s job is to make a diversified portfolio of bets — you are only one. Most founders find being around VCs distracting and draining because they feel pressure to perform the role of ‘impressive person.’ If you can’t immediately capture value from your performance… why waste your energy?
don’t expect the average investor to provide much value beyond money and connections. This makes the 10% of investors who can be legitimately useful to your business worth their weight in gold. Develop litmus tests to identify the valuable ones quickly and avoid wasting your time trying to convince nonbelievers.
our goal here is to spend as little time fundraising as possible — which requires being strategic about the order in which you talk to investors and how you talk about where things stand as you progress through the raise. The combined force of controlling those two variables are what “generates momentum” during your fundraise process.
Make a list of all the investors you know and can get introduced to, ordering them by the ones you most want on board to the ones you couldn’t care less aboutTalk first to a few low-stakes investors at the bottom of your list to practice your pitch and identify common investor questions and critiques you’re going to getIf available to you, next get a few investors who already wanted to give you money on board so you have a dollar amount you can say you’ve raisedWork your way up your investor list, talking to the investors you most-want-on-board-but-still-need-to-convince last (this optimizes your odds they say yes)
This all goes by much faster if you court investors similarly to how hot girls treat their many potential suitors. If your raise is already a little taken and you exude an air that you don’t need them, mimetically-minded investors become much more interested.
If you’re anything like me, you will worry intensely about not making a fool of yourself. It will probably go ok, but not as amazing or illuminating as you’d hoped. You might leave and feel a deep sense of lostness set in. This is all very normal. In time you will see them in increasing clarity, often noticing the differences between your and their values and why you would not enjoy living their life at all.
the people on pedestals probably hate being there. It’s lonely, hard to trust that the intentions of the new people around you are pure, and you often feel like you’re constantly letting people down. In the end, idolization hurts everyone involved.
Beware of intellectual dementors and clout demonsIntellectual dementors will try to eat your ideas and interestingness — not necessarily to copy you, but to wring your brain dry to amass knowledge themselves. They often play mini IQ games/tests of will in conversation and masquerade as investors while never actually investing. Clout demons are similar, but view people less as brains and more as stepping stones towards supreme social status. The power move to protect yourself from both is to simply abstain from playing their games — give as little info on yourself and your ideas as possible and reflect their questions directly back at them.
People will help you if you ask for what you want clearly and concisely
Knowing what you want requires a lot of upfront soul-searching, followed by strategic and long-term thinking once you’ve committed to a thing (I can’t really demystify this more). Once you’re all in, I highly recommend diligently keeping a list somewhere of the top three things you currently need help with so when people ask, you’re ready.
You don’t want to make people feel like you’re using them but you do want to use your social capital for things you care about. General rule of thumb: ask for things either 1) after a positive interaction or 2) completely out of the blue with a concisely written and compelling email/text. Tone matters because you don’t want to sound desperate and you do want to show you know how to play the game (write like the founder you most admire talks).
once we’ve taken action on behalf of something, our brain assigns more value to said thing. Tim Keller: “The feeling of love follows the action of love.” Love is a strong word here, but the point stands — help people help you. Startups are long-term games, so it only makes sense to do them with people you truly want to be around for a very long time.
#ambition#advice#finance#startups#guides#hiring#business#relationships#pseudo#interviews#vc#funding
·mothfund.substack.com·
the earnest ambitious kid's guide to investors
How Perplexity builds product
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.
#startups#business#product strategy#companies/Perplexity#ai#search#future#project management#job-titles#business/hierarchy#tech#examples#work/productivity#workflows#tools
·lennysnewsletter.com·
How Perplexity builds product
Looking for AI use-cases — Benedict Evans
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.
#LLMs#trends#tech#workflows#startups#history#sql#automation#ai/auto-ai#mental models#ui#companies/OpenAI#GPT#ai
·ben-evans.com·
Looking for AI use-cases — Benedict Evans
A New Marketplace That Helps Creators Earn More And Gives Brands Easy, Direct, On Demand Access To Creators
A New Marketplace That Helps Creators Earn More And Gives Brands Easy, Direct, On Demand Access To Creators
To quote Alexis Ohanian, “Pearpop is the marketplace for brand deals for anyone with an audience. I love my agency, UTA, but the traditional agency model cannot support the breadth and diversity of internet creators. There’s no way you can have agents in an office doing all those deals, nor should you. You want a marketplace for that, and that’s what Pearpop has built."
Many of the first users were successful artists/creators who wanted smaller influencers with highly engaged followings to share their content to extend their reach and awareness.
As Pearpop has grown, brands have been drawn to its ability to execute influencer activations directly in a quick, targeted, frictionless, hyper-localized, economically attractive manner. Pearpop’s self-serve marketplace is a win/win for creators and brands because it’s as simple for brands to find creators as placing a Facebook, Google, or LinkedIn ad.
The briefs go out as a type of casting call and brands are instantly/automatically paired directly with relevant creators. Brands can accept all that apply or specify to approve each influencer before they post.
“Brands play an absolutely critical role in the Creator Economy, and technology has the power to streamline access to the most relevant creators for a brand in the same way Uber and Airbnb streamlined access to cars or home rentals. As just one example, Pearpop shrinks the average time it takes to launch an influencer program from 6 weeks to 6 hours,” said Morrison.
Another aspect creators like is how easy it is to “get found” because of both the way they’re listed in the database, and how challenges are shared.
While the “Creator Economy” is experiencing hockey stick growth, the sad reality, is only about 1% of creators earn a living from their content. Social media platforms have been the primary beneficiaries.
The Wall St. Journal reported the top 1% of streamers on Twitch earn more than half of all streamer revenue, and the majority made less than $120 each in the first 3 quarters of 2021. In spite of that, the number of creators increased 48% in 2021
#startups#influencers#marketing#trends#stats
·forbes.com·
A New Marketplace That Helps Creators Earn More And Gives Brands Easy, Direct, On Demand Access To Creators
Why Success Often Sows the Seeds of Failure - WSJ
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.
#tech#trends#startups#faang#companies/google#capitalism#americanism
·archive.is·
Why Success Often Sows the Seeds of Failure - WSJ
Please just tell me what you do - Evan Conrad
Please just tell me what you do - Evan Conrad
Describe things that someone can explain to someone else, or you'll miss out on word-of-mouth growth. Imagine you wandered into some party and met an investor/donor/customer named Emily. Even if you're the most persuasive person ever and she walks away from the conversation energized and excited, your time is wasted because Emily can't explain to her coworkers/friends/legislative-body what specifically she's excited about.
If Emily is a potential customer and all she heard was nothing-language, then you've gained no information. She might be interested! But that interest might only be in something she's imagined — not what you're actually making.
Nothing-language is describing your product as "an investigation into how we generate dispersed intimacy, signify alliance, and physical representations of our digital coordination praxis"1 instead of saying you're an investment fund. If you're going to "revolutionize", "create the operating system for", "build at the intersection between", "empower", "democratize", "individually flourish", or "be interdisciplinary", the universe should pause, rewind, and let you explain again. It's kind and wants you to succeed.
Be boring. Say you're "plaid for messaging apps" or "a figma plugin that generates svg icons from gpt-3" or "chrome extension that adds cmd-k to every website".
#about#bio#product strategy#branding#narratives#career#marketing#copywriting#startups
·evanjconrad.com·
Please just tell me what you do - Evan Conrad
AI startups require new strategies
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.”
#business#startups#business/models#faang#tech#silicon valley#business/strategy#trends#ai#companies/OpenAI
·longform.asmartbear.com·
AI startups require new strategies
Rethinking the startup MVP - Building a competitive product - Linear
Rethinking the startup MVP - Building a competitive product - Linear
Building something valuable is no longer about validating a novel idea as fast as possible. Instead, the modern MVP exercise is about building a version of an idea that is different from and better than what exists today. Most of us aren’t building for a net-new market. Rather, we’re finding opportunities to improve existing categories. We need an MVP concept that helps founders and product leaders iterate on their early ideas to compete in an existing market.
It’s not good enough to be first with an idea. You have to out-execute from day 1.
The MVP as a practice of building a hacky product as quickly and cheaply as possible to validate the product does no longer work. Many product categories are already saturated with a variety of alternatives, and to truly test the viability of any new idea you need to build something that is substantially better.
Airbnb wanted to build a service that relied on people being comfortable spending the night at a stranger’s house. When they started in 2009, it wasn’t obvious if people were ready for this. Today, it’s obvious that it works, so they wouldn’t need to validate the idea. A similar analogy works for Lyft when they started exploring ridesharing as a concept.
Today, the MVP is no longer about validating a novel idea as quickly as possible. Rather, its aim is to create a compelling product that draws in the early users in order to gather feedback that you then use to sharpen the product into the best version of many.
If you look at successful companies that have IPO'd in the recent years–Zoom, Slack, TikTok, Snowflake, Robinhood–you see examples not of novel ideas, but of these highly-refined ideas.Since many of us are building in a crowded market, the bar for a competitive, public-ready MVP is much higher than the MVP for a novel idea, since users have options. To get to this high bar, we have to spend more time refining the initial version.
The original MVP idea can still work if you’re in the fortunate position of creating a wholly new category of product or work with new technology platforms, but that becomes rarer and rarer as time goes on.
Let’s jump over the regular startup journey that you might take today when building a new product:You start with the idea on how you want to improve on existing products in a category.You build your first prototype.You iterate with your vision and based on feedback from early users.You get an inkling of product market fit and traction.Optional: You start fundraising (with demonstrable traction).Optional: You scale your team, improve the product, and go to market.
In today’s landscape, you’re likely competing against many other products. To win, you have to build a product that provides more value to your users than your competition does.To be able to do this with limited resources, you must scope down your audience (and thus your ambitions) as much as possible to make competing easier, and aim to solve the problems of specific people.
When we started Linear, our vision was to become the standard of how software is built. This is not really something you can expect to do during your early startup journey, let alone in an MVP. But you should demonstrate you have the ability to achieve your bigger vision via your early bets. We chose to do this by focusing on IC’s at small startups. We started with the smallest atomic unit of work they actually needed help with: issue tracking.
We knew we wanted our product to demonstrate three values:It should be as fast as possible (local data storage, no page reloads, available offline).It should be modern (keyboard shortcuts, command menu, contextual menus).It should be multiplayer (real-time sync and teammates presence).
Remember, you’re likely not building a revolutionary or novel product. You’re unlikely to go viral with your announcement, so you need a network of people who understand the “why” behind your product to help spread the word to drive people to sign up. Any product category has many people who are frustrated with the existing tools or ways of working. Ideally you find and are able to reach out to those people.
Once you have a bunch of people on your waitlist, you need to invite the right users at each stage of your iteration. You want to invite people who are likely to be happy with the limited set of features you’ve built so far. Otherwise, they’ll churn straight away and you’ll learn nothing.
To recap:Narrow down your initial audience and build for them: Figure out who you're building the product for and make the target audience as small as possible before expanding.Build and leverage your waitlist: The waitlist is the grinding stone with which you can sharpen your idea into something truly valuable that will succeed at market, so use it effectively.Trust your gut and validate demand with your users: Talk, talk, talk to your users and find out how invested in the product they are (and if they’d be willing to pay)
#startups#product design#product strategy#business/strategy
·linear.app·
Rethinking the startup MVP - Building a competitive product - Linear
Competition is overrated - cdixon
Competition is overrated - cdixon
That other people tried your idea without success could imply it’s a bad idea or simply that the timing or execution was wrong. Distinguishing between these cases is hard and where you should apply serious thought. If you think your competitors executed poorly, you should develop a theory of what they did wrong and how you’ll do better.
If you think your competitor’s timing was off, you should have a thesis about what’s changed to make now the right time. These changes could come in a variety of forms: for example, it could be that users have become more sophisticated, the prices of key inputs have dropped, or that prerequisite technologies have become widely adopted.
Startups are primarly competing against indifference, lack of awareness, and lack of understanding — not other startups.
There were probably 50 companies that tried to do viral video sharing before YouTube. Before 2005, when YouTube was founded, relatively few users had broadband and video cameras. YouTube also took advantage of the latest version of Flash that could play videos seamlessly.
Google and Facebook launched long after their competitors, but executed incredibly well and focused on the right things. When Google launched, other search engines like Yahoo, Excite, and Lycos were focused on becoming multipurpose “portals” and had de-prioritized search (Yahoo even outsourced their search technology).
#startups#business#business/strategy#examples#history#faang
·cdixon.org·
Competition is overrated - cdixon