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?
Last week at SaaStr, we unveiled the results of the Theory Ventures 2024 Go-to-Market Survey. This annual survey shines some light into the state of SaaS sales & marketing, offering a glimpse into how founders & companies are navigating the current business landscape.
Here’s a breakdown of the key findings:
Despite the challenging economic environment, founders are more optimistic than 2022. The average outlook score has risen from 6.1 in 2022 to 6.7 in 2024.
The first version of the SaaS GTM playbook was written twenty years ago. Salesforce championed it. Mark Roberge published The Sales Acceleration Formula about Hubspot’s journey.
Over the next two decades, we analyzed, quantified, instrumented, & optimized many aspects of the SaaS GTM. Account executive to SDR ratios, sales cycle lengths, conversion rates, customer acquisition costs, customer lifetime values, net dollar retention.
The new software GTM playbook has yet to be written. But the companies who figure it out will become the next wave of massive businesses.
Large language models are wonderful at ingesting large amounts of content & summarizing. Uploading an academic paper and I can pester it with an infinite list of questions & it will respond with equally infinite patience.
In comparing the two most recent Microsoft earnings calls, Claude highlighted:
Excellent analysis.
The AI Demo isn’t easy. Many of the major AI companies have demoed their AI systems, first starting with pre-recorded, & now pushing into live demos. They don’t always work.
Multiply Murphy’s Law by a non-deterministic system & it’s not unreasonable to expect AI demos to nearly always hiccup.
Demo disruptions aren’t disaster. These systems are early & changing rapidly. They might suggest the system requires work & tuning, not a fundamental challenge.