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Entering the fast fashion era of SaaS..
I shared my take on Sam Altmanâs recent tweet that âweâre entering the fast fashion era of SaaS very soonâ on LinkedIn here.
The post triggered a controversial discussion with 139 comments (and counting) - something I really enjoy and one of the key reasons I regularly post online. Few better ways to challenge your thoughts at scale ;)
Today, Iâd like to dive deeper on this topic and share:
How software development has changed and why thereâs only 2 categories left: the hard and the easy build
âWhen building remains hard, winning gets easy. When building gets easy, winning gets hard.â - and how you can still win in an era where everyone can build
Scale, moats, and margins across the stack: Why the hard build gets rewarded today but the easy build has hope for tomorrow
Letâs dive in!
Thereâs only 2 categories of software left: the hard and the easy build
Thereâs no software company without AI anymore. This was different not too long ago.
Deconstructing the AI Landscape (2020)
In 2019, I conducted an internal deep dive at Earlybird about the implications of AI on businesses and established a simple framework that I also published in my âDeconstructing the AI Landscapeâ article.
Do you want to build a core-AI tech component which is applicable to variety of use cases and independent from a specific industry (horizontal), or do you aim to solve a very use-case- or domain-specific issue by applying AI (vertical)?
With this simple question, I split AI businesses into horizontal and vertical ones, and explored what it takes for either one of them to win.
Re-reading my medium post today, Iâm frankly a bit surprised how little my perspective on the fundamentals has changed.
Yes, AI dominates the news and we keep pushing the boundaries on a daily basis. And of course we reflect this in more detailed frameworks as the one below from my 2023 âAI Cheat Sheetâ.
But in its core, if we simplify to the lowest denominator, we only have 2 categories of AI businesses.
The two categories of AI businesses in 2025
While in the past I called them âhorizontal / core AI / industry agnosticâ and âvertical / application / industry specificâ companies, I would slightly reframe and simplify even further today:
Hard to build - aka the horizontal ones: These are the companies that despite transformative AI innovations remain notoriously hard to build. They require deep technical expertise and sophisticated software development skills to create and scale complex architectures.
Examples include infra companies (like Nvidia, HPE, Intel, GCP, Azure, AWS - and our portfolio company Arago), intelligence & foundation model companies (like Black Forest Labs, ElevenLabs, Mistral - and our portfolio company spAItial), middle layer companies (i.e. the picks and shovels like our portfolio companies TopK for hybrid databases or Aivenâs open source data platform), but also full-stack platforms (like Antrophic, OpenAI, Databricks, Microsoft, Google - and our portfolio company Aleph Alpha).Do they use AI? Of course. Does it accelerate their delivery? Surely. And does AI reduce the entry barriers to build better competitors with ease? Well, actually no. At least not today. All of the above companies remain hard to build. AI or not.
Easy to build - aka the vertical ones: Building these companies has massively changed in the last 12 months. With Lovable, Bolt, Cursor, Replit and likes weâve seen entry barriers to build vertical or use case specific solutions almost disappear. Examples include anything you can classify as an âAI wrapperâ such as the code-gen tools mentioned above but also (rather simple) use case specific products like DocuSign.
What it takes to win
âWhen building remains hard, winning gets (relatively) easy.â
For the first category of companies that remain hard to build, Iâd say that once the build has been completed and the metrics indicate PMF, the easier part is to scale the customer acquisition and win.
Sounds silly? Yes, I know. No win is easy.
But for these companies, the harder part is definitely finding the right talent - the AI infra engineer or the LLM researcher, the kind of people that receive these $MM offers from Meta these days - and pulling off this hyper complex product.
Once this is done, the scale becomes easier as there are very few alternatives in the market that have solved the same problem and shipped a great product, that itâs relatively easy to stand out and acquire customers. Thereâs just less noise.
âWhen building gets easy, winning gets (exponentially) hard.â
As entry barriers disappear, it becomes easier to build AI application layer companies, and as a result, the number of similar solutions starts exploding.
Speed over perfection. Products will be launched faster than ever. In line with Samâs âfast fashionâ analogy, features will be trendy, disposable, and iterated at breakneck speed. Once the product v1 is built and demand got proven, the hard part begins:
âĄïž Go hyper niche or go broke. Buying behaviours seem to split into two camps: hyper-niche best-of-breed versus all-in-one platforms. You donât want to end up somewhere in-between. While all-in-one platforms tend to fall into the camp of âhard buildsâ, the âeasy buildsâ are likely the be hyper-niche products. Donât go to broad too early and rather become the go-to solution for one very specific problem.
âĄïž Building a solid v2. You donât need an engineer to build the v1. But once demand got proven and your early user base keeps growing, you should double down and solidify the product sooner than later. You hire a few engineers. Not average ones, but 10x engineers. Reducing churn and improving retention will be key, as eventually this will drive your customer LTV.
âĄïž Acquiring customers at low CACs. Standing out in a flood of look-a-likes is hard, and will become harder every single day. Therefore, it becomes critical to build content & community early, no matter if via founder personal brand, your own newsletter or podcast, events, Slack channels, or other means.
In summary, go narrow and deep, and find an unfair advantage for distribution as early as possible.