Hello and welcome to the Moonfire newsletter.

Rather than just one set of extraordinary founders, this time we’re casting a spotlight across our portfolio.

We asked our founders what major trends are shaping their strategy, the most pressing hiring challenges and opportunities, how they’re using AI in their engineering and product development workflows – and their “hot takes”. Read on for what they had to say.

As always, we’ve also got our usual roundup of great reads, book, and podcast.

Enjoy.

Mattias and the Moonfire team

🌓🔥

The Snapshot

Here's a quick roundup of interesting stuff we saw this month:

• ‘Hyperlegibility’ and the pursuit of information
• Daytime napping is associated with a larger total brain volume
• Brett Adcock on the latest AI and robotics news
• Anthropic tracing the thoughts of a large language model
• And our very own Mike’s deep dive into data infrastructure

What’s Up at Moonfire?

The year of churn and the death of the specialist: What’s top of mind for tech startups?

We asked our portfolio founders.

Death of the specialist. Taste as the new bottleneck in programming. The year of churn. We recently surveyed our founders – who, together, are building the future of robotics, data analytics, fintech, healthtech and more – to understand what major trends, or unexpected market developments, in their sectors are currently shaping their strategy, the most pressing hiring challenges and opportunities, and their “hot takes” (these ones are shared anonymously, to encourage only the boldest predictions). We also wanted to know how AI is changing their product development and engineering workflows, and what API-based LLMs they’re using or planning to use.

It’s a snapshot of what early-stage tech founders are thinking, planning for and prioritising right now.

So what did they have to say?

Market trends

Unsurprisingly, AI is top of mind for most of our founders.

From experimentation to application

For some, the focus is shifting from experimentation to full-scale deployment, with businesses looking to integrate AI into core operations across different verticals.

Anders Krohn of Kernel observes that “enterprises are moving (or trying to move) from experimental budgets and production to deployment of AI applications.” Even traditionally conservative technology adopters, like finance departments, are embracing AI. Three-quarters of companies were already using AI to some degree in their financial reporting processes according to a KPMG survey last year, and Ole Heine of Haydn notes that finance teams “are getting more and more open-minded about implementing LLMs in their workflows.”

This extends to compliance and security. Mike McNeil of Fleet sees IT and security teams “automating and changing their practices to be more efficient,” and Baran Ozkan of Flagright sees “LLMs becoming the forefront strategy for all AML providers.”

The future is verticalised and niche

“Incumbents will eat most of your basic AI feature set. Keyword: Salesforce Agentforce”, was one of our anonymous takes.

As tech giants like Salesforce integrate AI features into their products and cause the first cohort of B2B AI applications to churn, startups will need to rethink the application layer, combining both depth and breadth in their offerings.

Our founders see the future of AI in verticalised, agentic applications. One founder building back-office SaaS for businesses believes that “The biggest wins will come from vertical solutions that use LLMs to augment humans in specific domain-specific complex processes.” Dhruv Tandon of Decisional adds that “verticalised AI-native UX will evolve into a maker-checker experience: the agent will make and the human will check and regenerate.”

So verticality, product depth, and proprietary data are the moats. “Also, AI products should require more user ‘depth’ and skill; single one-button tools are actually bad for wide adoption of AI,” was another founder’s take.

Open source and roll-your-own

Eduardo Candela of Maihem reflects that “The commoditisation of smaller and open source LLMs will enable many new AI companies and applications.” New models like DeepSeek R1 – and the fact that the DeepSeek team openly documented their methodology – are lowering adoption costs and driving faster innovation, and founders see open-source AI addressing scalability issues encountered by proprietary systems.

However, the real bottleneck is not AI but data. Success will depend on startups’ ability to invest in backend optimisation and data formatting to get the most use out of these models, whether fine-tuning an open source model or training one from scratch. “The open source or roll-your-own models are attractive but will require more funding to properly leverage as a startup,” says George Webster, stealth. “You have to have the business structures in place to optimise your systems with AI and the data needs to be in the right structure and backend for the AI to execute against. Data engineering and business processes are 90% of AI.”

Demand for simplicity

Across every category of B2B software, the signal is clear: simplicity wins.

“After years of being bombarded by SaaS and point solutions, HR buyers crave simplicity more than ever,” says Sançar Sahin of Oliva. “This means consolidated tools, simple pricing models, and low admin.”

This expectation isn’t limited to HR. In compliance, AI is moving beyond “black box” solutions toward “glass box” systems. “Compliance officers now demand the same intuitive interfaces as consumer apps, with AI decisions explained in clear business language,” says Madhu Nadig of Flagright. “This convergence of advanced AI and human-centered design is redefining what compliance teams expect.”

We also covered hiring, talent, and AI tooling. Click here to read on…

Podcast of the Month

Bloomberg’s Joe Weisenthal and Tracy Alloway chat with Blair Levin, policy adviser at New Street Research and former Federal Communications Commission Chief of Staff during the 1990s telecom deregulation, about the surge in AI data centre investments and potential parallels with the late 90s telecom bubble.

Listen now

Good Read of the Month

This book was given a great write up in the FT and is published this week. Joel Burke sets out how Estonia transformed from Soviet state to Govtech pioneer within a few decades, and now has the most tech startups per capita in Europe.

Buy it here

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That’s all for this newsletter.

Until next time, all the best,

Mattias and the Moonfire team.

🌓🔥

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