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.

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.

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

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.

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.”

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.

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?”