The AI industry currently produces new startups in something that is probably best described as a Cambrian explosion. We have seen this frantic pace of company creation before in other technology waves, such as the dot-com boom of the late 90s and the SaaS boom of the early 2010s.
Nobody knows what the AI startups of the future will look like exactly and what will them make successful. What will their business model be? Which technical architecture decisions will be crucial? How will they go to market?
Is there something we can learn from previous technology waves to help us think about success recipes? Can we maybe even see early glimpses of what a new playbook for success might look like?
The Software-as-a-Service wave might be a good place to look for historical echoes. It’s hard to remember now, but software was built and sold very differently before SaaS became mainstream ca. 2010.
What we did and didn’t know about SaaS in 2007
I had the pleasure of writing my master’s thesis at MIT about the then emerging SaaS market in 2007. My advisor was Michael Cusumano, a professor who had already done research about the software industry for decades (and has continued to do so). 2007 was a moment quite comparable to where we stand in AI now: It was clear that there is going to be a big wave, first successful companies were growing very quickly, but there were many things still to be figured out. My old thesis probably describes quite well what the consensus in the industry was back then, and in particular how many open questions there were.
For example, here are some things that we now take for granted that were unclear in 2007:
- Where do I host my SaaS applications? AWS had just started out, Azure and GCP didn’t exist. The assumption was that SaaS startups needed to own and manage their own servers and that doing so efficiently might be a strategic advantage.
- How do you create a UI efficiently? Modern standards like React didn’t exist, and it wasn’t even clear that the dynamic HTML UIs of today were the right way forward. Some people believed in hosted backends with locally installed clients.
- How do you integrate SaaS with existing systems? Doing so in 2007 was clumsy and manual. The elegant API methods of today didn’t exist yet.
- How do you find customers? In 2007 software sales were still dominated by field salespeople in suits who flew to clients to take them to expensive dinners. Product-led growth, free trials and self-serve onboarding were only being tested in tiny niches.
- How do you price your software? Seat-based pricing already existed, but many traditional software products were priced on a per-CPU basis and other now obsolete schemes. Usage-based pricing — now increasingly popular in SaaS — was not practical for locally installed software. On the other hand, many people in 2007 believed that there would be such a thing as advertising-financed software, but that hasn’t happened outside of gaming.
- How often do you release a new product version? For locally installed enterprise software, doing this even quarterly was ambitious. Now SaaS companies often push new updates several times a day — unimaginable in 2007.
- How do you measure your business, and which KPIs matter? A vague concept of LTV and CAC existed, but things like net revenue retention, rule of 40 or magic number had yet to be invented. And it wasn’t clear what a reasonable gross margin for a SaaS company should be (I guessed 85%, but that was too optimistic. Median margins for public SaaS are currently 76%).
This shows that while general assumptions (SaaS is going to be big and will disrupt many incumbents) were obvious, many of the crucial details had yet to be figured out. I’m certain it’s the same with our current AI wave.
Market expansion out of nowhere
Even with all its optimism, the tech industry sometimes falls into the same biases when it comes to predicting market growth.
In the case of SaaS, there was (as so often) a bit too much optimism about the short term. It turned out that enterprises needed a bit more than just a few years to wrap their heads around this new model. But the longer-term analyst predictions were too pessimistic. Many thought that SaaS would top out below 50% share of the total software market, and of course we’re now way beyond that.
When I wrote my thesis in early 2007, I found a grand total of 108 companies that offered some kind of SaaS product. Almost all of them provided horizontal solutions such as CRM or project management.
Now there are (according to most estimates) about 30’000 SaaS companies in the market. And presumably most of them offer very focused vertical solutions for certain use cases in certain industries.
This huge market expansion in vertical SaaS was something that was still hard to see in 2007. For traditional installed software, selling to small verticals was much too expensive. You can’t send field salespeople to customers with small budgets. SaaS with product-led growth, lean tech stacks and quick feature iteration cycles was ideal to crack these smaller market segments.
Great disruption or great times for incumbents?
A frequent discussion in every tech wave is what it will mean for the existing incumbents in the industry. Historically, predictions of total annihilation (“AI will kill the entire SaaS industry!”) tend to be very wrong. Incumbents are not idiots, and they see tech waves as much as everybody else. The question is how quickly they can and want to adapt while new players are emerging. Sometimes they emerge as the big winners, sometimes they get disrupted. The truth is often in the middle.
In the case of SaaS, the transition was certainly not easy for incumbents such as Microsoft, Adobe, SAP or Oracle. Interestingly, even as early as 2007 they all experimented with some kind of SaaS product. But who wants to give up a comfortable business with 95% gross margins for an experimental new approach?
By now this is of course history. A subscription-based business model is now the standard, and hosting software in the cloud is too, apart from some conservative corners of enterprise IT. The big incumbents have all transitioned to the new world, and in some cases spectacularly well.
However, the story was less glamorous in the segment behind the biggest names. Many mid-market vendors in the ERP and CRM spaces saw their market share shrink due to competition from SaaS. The lesson is that big incumbents with ample resources are in a better position than smaller players to deal with a disruption.
What are the lessons for AI startups?
The takeaway from the history of SaaS is that many macro trends are pretty obvious early on, but the details of implementation are not. Who could have predicted that the majority of SaaS companies would host their products on the IT infrastructure of an online book store? No doubt, we’re going to see similarly crazy-sounding things in the future of AI. Market expansion is not obvious either and often goes beyond what people are able to imagine in the early days.
There are a few things we probably can say about the future of AI startups:
- AI products will look different from SaaS, but we don’t know how. Right now most AI products still are structured like SaaS. If history is any guide, this will be a very temporary pattern. For example, it doesn’t make much sense for a powerful autonomous AI agent to have a login page, dashboard and settings page as the primary units of interaction. It’s hard to predict where this is going. The “dominant design” (to use James Utterback’s term) typically takes years to emerge. But it’s likely that we will interact with AI products in many more granular ways.
- Software (if that’s still what we will call it) will be able to solve many small problems. Very much like SaaS enabled niche solutions for even small verticals, AI will make it possible to provide solutions for tiny problems. Of course we’re already experiencing this when we use today’s chatbots. The countless small problems we solve on ChatGPT are not something that would be worth creating a software product for. But where is the boundary between one-off solutions (like what we would have done in a spreadsheet in past times) and repeating problems that would warrant creating something that feels more like a product? Hard to tell, but the boundary will shift towards much more granularity.
- Moats will change towards speed, vertical expertise and depth of integration. In traditional pre-SaaS enterprise software, moats for software companies were much about customization, long deployment cycles, sales relationships and expensive product creation. This changed with SaaS to agility, network effects, ease of use, and flexible pricing. We’ll see probably more of this with AI, but potentially taken to the extreme. Let’s say I need an agent that solves a particular workflow problem for me. Why would I want to evaluate software products? Why wait until tomorrow? Can somebody just solve the problem for me in an intelligent way, right now, based on a vague description? The AI companies that will be able to do that will build on deep experience with a certain class of use case in certain verticals paired with infrastructure that is optimized for speed. This will be hard to copy.
- Many of today’s infrastructure concerns will go away. In 2007 startups worried about which server type and database to buy, today they worry about foundation model pricing. This kind of broadly used infrastructure often gets commoditized quite quickly, and we’re already seeing signs of it in AI foundation models. Infrastructure worries will move to a higher level. It’s a good idea to plan for this.
- There will be new forms of distribution. Most AI startups still follow the good old SaaS GTM playbook (PLG, inside sales, etc.), but it’s very likely that the future will find other ways to take products to the market. Maybe agents will be bought and sold on marketplaces, with buyer agents automatically finding the best solution. In the enterprise, we’re currently seeing a return to a consulting-heavy model with Forward Deployed Engineers. Is this due to market immaturity, or will it be the way of the future? Experimentation with fresh routes to the market could pay off hugely for innovators.
- Pricing will evolve to something new. The standard model now is something like $20 per seat per month for many AI products. That’s of course just a leftover from SaaS pricing, not reflecting true product value at all. Most likely we’ll see some kind of seat+usage hybrid pricing for a while, but future schemes could look very different. Value-based pricing for example has been quite elusive for SaaS, but maybe AI will enable it at scale.
- Dealing with regulation will be table stakes. Early SaaS companies didn’t have to worry about regulation and certifications. Later we got GDPR, SOC2 and many other great inventions of the regulatory-industrial complex. It’s likely that regulatory pressure will further increase with AI. Dealing with this responsibly, but also efficiently, will be a crucial skill for AI startups.
- The dance with big platforms will continue to be both an enabler and mortal threat. Most software companies have to build on somebody else’s platform. That’s just efficient for technical enablement and distribution. In the good old days it was the Windows OS or an Oracle database. In SaaS it’s hyperscalers, API providers and marketing channels such as Google. In mobile it’s Apple and Google. In AI it’s foundation model providers. What these have in common: They are elephants that don’t particularly care about all the small players that build on their platform. If the elephant feels like releasing a new feature that will kill off dozens of its small customers, it will do it. If the elephant decides to change rules that will critically affect the business of thousands of its customers, it will do it. Balancing these threats with the great opportunities that platforms provide has always been a crucial skill. There are startup concepts that are not very smart (like filling a temporary hole in a big platform’s functionality) and there are others that have historically worked well (like being the cross-platform glue for people who want to use multiple platforms). It’s the same in AI. Of course countless startups are one OpenAI or Google announcement away from obsolescence. Others are not. Studying these platform dynamics is one of the most useful things that founders can do.
AI is at a moment that is quite similar to the early days of the SaaS boom. Everyone sees a tidal shift, but the money is in the details that haven’t solidified yet. Just as SaaS rewired many dimensions of the software industry(hosting, UI, integrations, distribution, pricing, release cadence, plus a surprise boom in vertical niches), AI will change many dimensions. But we should be just as confident that our current assumptions about the “how”—the infrastructure we rely on, the way we price our products, the channels we use to find customers—will look profoundly naive in retrospect. Founders who move fastest on these learning loops will be the winners.
(Thanks to GPT-5 Pro and Gemini 2.5 Pro for research and inputs, DeepL Write for copy editing and GPT-5 Thinking for the visualization. I’m sure thanking AIs for their contribution will pay off some day…)
