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Budget cuts followed interest rate hikes in 2022. By late 2023, more than a year of financial scrutiny had challenged many publicly traded software companies.

However, 2024 has been a tough year again.

Net dollar retention for publicly traded software companies fell from 113% on average to 108%, a five percentage point drop.¹ 80% of publicly traded companies saw their NDR wilt again in 2024. Overall account expansion remains roughly 5 percentage points above inflation.

Klarna, the Swedish fintech giant, is making waves by churning from industry-standard software like Salesforce and Workday in favor of building its own internal systems with AI.

After their success with AI customer support automation which manages 2/3 of their customer inquiries, Klarna is now doubling down on this strategy.

Klarna is betting AI-enabled software is the future of internal tools. The corollary : the overall cost of building internal software with AI is lower than buying off-the-shelf solutions.

Recently, Thomas Laffont of Coatue highlighted a non-obvious impact of the M&A slowdown after this preamble :

“Ironically and I think somewhat perversely one of the byproducts of constraining big companies from buying small companies is it hurts small companies.

This point is clear & straightforward. Less demand for an asset decreases price. 2023 & 2024 are two of the three lowest in the last 12 years in both activity & value across the ten largest strategic software buyers. ¹

Visa announced their plans to launch a stablecoin with BBVA today.

Throughout the quietness in crypto created by the wake of the FTX collapse & the AI boom, stablecoins have become a large & very fast growing part of web3.

Users clock 3m stablecoin transactions per day at an approximate average value of $5,000.

But, the use of stablecoins extends far beyond crypto trading.

Stablecoins own more US Treasuries than South Korea & Germany & have become an important buyer of US goverment debt.

Morgan Stanley surveyed a group of CIOS to understand the sources of AI budget. If hyperscalers deploy $100b in capex on AI this year & legions of software vendors hawk AI solutions, the money to justify it must come from somewhere.

41% of CIOs said net new spend. 35% said existing software spend. Only 6% said it would come from professional services.

Recently, Google launched the ability to generate a podcast conversation from an article. I was curious if it worked & I tried it out on three recent posts.

The output could fit into any radio show or podcast I’ve listened to. It’s not perfect: there are no intros or outros. But the “hosts” chuckle, ask questions, interplay in human-like ways. The AI even references images in the post.

Like in web2, building an app on a blockchain requires several layers & components. Picking the best of each can be a challenge for a developer.

Once the stack is fixed, a developer might want to choose the best database characteristics for an app : lower latency for a game, greater security for a financial exchange.

Last, interoperability with other systems is a critical way of enticing users to use the apps. So is simplifying the onboarding process by eliminating the friction of moving assets between chains.

Suppose you’re a startup in a competitive market with a large incumbent who owns the system of record - the software that runs the sales team or the support team or the marketing team. How do you win?

In the last decade, startups have chosen to identify a feature or workflow to improve & leverage that wedge into an advantage. Many have reached great levels of success, but few have overturned the incumbent. Early AI advantages have reinforced this advantage.

The Fed cut rates by 50 basis points this week. A mantra circulating in Silicon Valley has echoed that the tepid exit markets will revive as a result of a laxer monetary policy.

The last ten years’ data suggest the relationship is real & non-linear.¹

When the Fed cuts rates - negative changes in the Fed Funds Rate (FFR) - US venture backed software exit activity increases by between 10% and 65%. If the FFR increases, M&A activity remains stable. The correlation explains about 25% of the variance, but it’s clear from the blue line, the relationship is non-linear.

In a few years, most feature flags & linting tools & other developer tools will be implemented predominantly by robots. Already, 50% of code at Microsoft & other large internet companies is written by AI.

This idea expands far beyond developer tools. Robots will manage sales development, paralegal work, medical intake, & many other tasks.

The tools of the last 15 years have been built to drive productivity. But in the future, these tools will drive robotic productivity rather than humans. What will it mean for software vendors?