Between code generation foundation models like Poolside and new IDEs like Cursor.ai enabling multiple code generation models within the same UI, it’s now evident that software engineering is changing. I met many engineers who had tried early/ier versions of these last year and weren’t very convinced but now couldn’t do without them. Menlo Ventures just surveyed 600 IT decision-makers in the US and reported that 51% were already using code copilots (see chart below).

To that extent, code generation has reached what we called (back in 2018) the MAP (Minimum Algorithmic Performance) to get broad adoption. Anyone in software development today should use these, and all of us in software should likely bake the assumption of higher levels of productivity in software engineering in the way we build and invest in software products and businesses.

The consequence of the above is that the broader software landscape is changing. Smaller engineering teams can build more comprehensive software products faster and cheaper. Companies like Linear get to tens of millions in ARR with only tens of employees. 

The positive consequence is that specific opportunities requiring considerable software development investments before getting to market will be easier to seize. The negative is that other markets will become increasingly competitive. 

This is good news for vertical software companies with long product roadmaps, comparably little competition, but that need comprehensive software offerings (and higher ARPAs) to make the opportunities they go after large enough (more on that here). Beyond faster software development, the new possibilities enabled by LLMs (e.g., document generation, text extraction) or simply new API building blocks more broadly available (e.g., payment, lending APIs) will unlock new opportunities for significant value creation through software. All of the above (faster software developments, more building blocks available) compounds in a fascinating way.

We might wonder what lower and lower software development costs mean for the broader software opportunity. 

First, we are convinced that one of the bottlenecks remains the ability of software companies to imagine the right software features (i.e., do the right product management) iterating with customers. This takes time and won’t go away anytime soon. 

Second, we are convinced that there are still a lot of digitization/automation opportunities that will be unlocked thanks to the above: digitizing vertical B2B markets or B2B commerce (i.e. B2B marketplaces), bringing software to the physical world through (new) sensors, building software-enabled hardware or building AI-first service businesses that look like software businesses, for example. 

Last and obviously, the fundamentals of software companies remain as attractive: very low delivery cost, high margin, recurring cash flow, etc… 

One way to put it is that AI might shrink existing market segments but will also make the overall software opportunity (pie) significantly larger. The slide below, published by Index, shows it simply and well.

Building on the above, we’re noticing a dramatic expansion in the number of use cases AI allows software companies to tackle. 

In parallel, the shared conviction that LLMs represent a technological disruption that’s sustainable and worth riding is attracting a lot of people to start businesses. It’s happening in Europe, but it’s happening in the US at a significantly faster pace. Without commenting on the economic and political situation, I saw many European founders moving to the US to start LLM-enabled businesses. 

Here’s just a list of cool software ideas I came across in the past 3 weeks:

• Foundation models for data science

• Vertical foundation models (e.g. bio, material, …)

• Verticalised versions of information retrieval and data normalization across multiple databases for finance, healthcare, pharma…

• Verticalised generation of documents to automate the answer to RFPs in the public sector or construction,

• Fully automated interviews for recruitment,

• …just to name a few!

Beyond “pure software” opportunity and, as we wrote in this blog published earlier this year, we (and many others) now believe that specific repetitive tasks humans perform will be partially or fully automated through LLMs. 

People are attacking this opportunity by building AI-first service businesses from the ground up or buying, merging, and automating existing businesses.

I heard of multiple roll-ups in accounting, healthcare, and customer service on both coasts of the US (none in Europe). Another exciting thing I encountered was a prominent film production company raising money to train a foundation model on their film archives.

Other exciting ideas we came across lately were rebuilding market research firms with AI-first survey definitions and/or AI-led interviews, rebuilding IT consultancies, staffing agencies, or expert networks, all AI-first.

Last but not least, organizations are and will be changing. Beyond the increased productivity of engineering teams mentioned in the first part, there’s now an increasingly strong belief that AI agents will perform specific tasks inside organisations such that they (and work) will change more broadly.

I’ll finish with this quote from Jensen Huang at Nvidia (here), who imagines a world where Nvidia remains an organisation with 60 direct reports for Jensen Huang, “only” 50,000 employees (which they more or less already have today) that will “all be CEOs” of 5M AI agents 🤯

Things are moving... so fast these days!