Record numbers of start-ups founded: The first half of 2026 will bring a historic start-up boom, massively driven by AI. But while software tools lower barriers to entry, failure is often only postponed. An analysis.
More companies are being founded again in Germany. According to figures from the startup association and the analysis company startupdetector, there were 3,053 new start-ups in Germany in the first half of 2026. According to the association, this is the highest number of start-ups in the six months since the survey began in 2019.
What is particularly striking is that around a third of the start-ups, namely 1038, are said to have an AI connection to the company’s purpose. The central driver of this increased start-up activity is artificial intelligence because it enables founders to start faster, easier and with less capital.
Founding start-ups: It’s worth taking a look at the numbers
The report about the high number of new start-ups sounds impressive at first. And a closer look shows that it is also impressive. Because it is becoming clear that more people in Germany are apparently willing to try something entrepreneurial again.
In a country where people like to ask before setting up a business whether the project is actually permitted, financeable, insurable, tax-optimal and socially secure, this start-up dynamic is definitely a positive signal.
But it’s still worth a second look. Because 3,053 start-ups say one thing above all: 3,053 companies were founded. No longer. Nothing less. They don’t say anything yet about whether these companies find customers.
They say nothing about whether they make sales. They say nothing about whether they will become profitable. And above all, they say nothing about whether the founders can make a living from it.
An entry in the commercial register is not yet a business model
In Germany people like to count. We count business ideas at events in incubators, startups, financing rounds, funding programs, patent applications, innovation centers, incubators, accelerators, networking events and of course panels with people who talk about innovation. The problem, however, is that many of these numbers describe “movement.” But movement is not progress.
A commercial register entry for a new company is a starting point. It is a formal act, and certainly a courageous step. But it is not yet proof that a viable company will emerge.
That sounds banal, but it is often forgotten in the euphoria of such reports. Starting a business is not the end goal of a business. It’s not even the first kilometer of the marathon. It’s more like putting on your running shoes.
Of course, one thing is clear: no one starts running without running shoes. But no one would claim after buying new running shoes that they have already successfully completed a marathon.
That’s exactly why you should be careful with start-up records and the associated reports. They are pleasing, but they are not proof of quality. They show that more people are starting to become entrepreneurs. Whether they arrive remains unclear.
Founding start-ups: AI lowers the hurdles, but not the requirements
It can hardly be denied that AI makes starting a business easier. Anyone who has an idea for an app, for example, can program a prototype more quickly, create a website with an online shop, formulate texts, develop presentations, evaluate customer feedback, automate processes or carry out initial market analyses.
What used to take weeks and external service providers or a small team can now often be done with just a few tools. This is a huge change.
But this is also where the first trap lies: If entry becomes easier, the actual requirements for a company do not automatically disappear.
When artificial intelligence hides the requirements
AI can help build a product faster. AI can help make pitch presentation slides look better. And AI can formulate texts more professionally. But AI cannot automatically answer the question of whether someone will pay for the product.
It also can’t help make it more attractive for investors to get started if the team doesn’t fit. And it does not automatically create clear customer benefits. In other words: AI can speed up a lot of things. But it doesn’t make the market any more merciful or more favorable to the founders.
But that is exactly the crucial point. Customers don’t buy because something was built with AI. Customers buy because it solves a problem that is important enough for them to spend money on.
This distinction sounds simple. But it is central. Because many AI start-ups will not fail because they cannot do anything technically. They will fail because their offer is not economically important enough.
If founding becomes too easy, failure is postponed
In the past, founders had to overcome certain hurdles before starting their business. They needed capital, technical skills, contacts, time, persistence, or at least someone who could build a website without it looking like it was built in 2003.
Today, AI can mitigate or even eliminate many of these initial hurdles. And that’s good because it means more people can get started. But you also have to see that AI also means that immature ideas look like finished companies more quickly. However, this does not make the hurdles go away. They just postpone and with it the failure.
In the past, an idea might have failed because it never came out of the drawer. Today, such an idea quickly gets a landing page, a logo, a pitch deck, a demo, a LinkedIn post and maybe even a few sweet-sounding AI-generated market assumptions.
This looks like entrepreneurship. Sometimes it’s entrepreneurship.
But sometimes it’s just very well-crafted tentativeness. And that then leads to a new quality. Because AI can not only generate productivity, but also simulate professionalism. And I don’t mean that as an accusation (at least not alone).
Rather, it is simply a simple observation. Bad ideas used to often look bad. Today, however, bad ideas can look amazingly good because of AI.
Founding start-ups: The real competitive advantage is not AI
And I see another problem. If many start-ups build on the same AI models, the same interfaces, the same no-code tools and the same automation logics, there will be no differentiation but, on the contrary, more interchangeability.
If everyone gets faster, speed alone is no longer an advantage. If everyone can write better texts, better texts alone are not a business model. And if everyone can program a prototype in a few days, this alone is no longer an advantage.
The crucial question should therefore not be: Does a start-up use AI?, but rather: What does this start-up have that others cannot easily copy despite AI?
Interchangeability instead of differentiation
This can be special data. It can be specific industry knowledge or practical experience. It could be unusually good customer access or other sales force. It can be regulatory competence.
It can be trust. Or it can simply be the ability to turn an innovative technology into a product that people actually use regularly and are willing to pay for. This is exactly where the wheat will be separated from the chaff. Because AI as a tool is becoming increasingly self-evident.
The mere reference to AI becomes less special. Or to put it another way: “We’re doing something with AI” might still have been an attention magnet in 2023. In 2026 it will just be a matter of course with no particular utility value.
The underestimated metric: Can the company support its founders?
Finally, a simple question should be placed more in the foreground when assessing start-ups. This question is: Can this company actually support its founders?
This question sounds old-fashioned, perhaps even a little unromantic. In any case, it doesn’t sound particularly forward-looking. But it’s a brutally relevant metric.
A company that is permanently unable to generate the livelihood of the people who build it (and without the use of funding or investments for this purpose) is perhaps an experiment. Maybe a project, an experiment or a bet – but not a sustainable company.
AI euphoria does not protect against the reality of sales
This point is often overlooked, especially in the AI euphoria. There is talk about scaling, about automation, about efficiency, about new business models and about the democratization of starting a business. And please don’t get me wrong: these are all important topics.
But in the end, enough money has to be earned, not just through a round of financing, but through customers who say: The product, service, etc. is so important to me that I will pay for it, gladly several times over.
Maybe that’s why every start-up report should at some point show a second number in addition to the number of new companies: How many of these companies pay their founders a living income after twelve, 24 or 36 months? That number would probably be less glamorous. But it would be much more meaningful.
The incorporation record must be read correctly
With what I said above, I don’t want to downplay the start-up record, on the contrary: Germany needs more people who think entrepreneurially. Germany needs more experiments.
Germany needs more courage, more speed and certainly more AI expertise in new business models. If AI leads to people testing faster, building faster and learning faster and therefore starting companies faster, that is a good development.
But we shouldn’t make the mistake of confusing start-up dynamics with economic substance. The number of 3,053 new start-ups is an initial signal, but nothing more. In particular, it is not proof of success.
The question of existence: After the starting signal comes the long haul
It shows that more people are getting started and – if you look at the reasons for getting started – that AI is lowering the threshold for starting a business. It also shows that more is possible in Germany than one would sometimes like to admit in the usual complaints about bureaucracy, risk aversion and lack of capital.
But in my opinion, the actually relevant topics are not discussed: How many of these companies manage to turn an idea into an offer? A product from an offer? Sales from a product? Turning sales into a viable company? And perhaps even a real contribution to the economic strengthening of our economy from a viable company?
Only when we have reliable statements on these topics will the founding record become more than just a nice number. Until then, it’s good that more companies are being founded. But we shouldn’t just celebrate the starting signal. But especially those who continue running after the start.
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