Software spending in 2024 should rise 12%, increasing from 9% last year according to Gartner. Overall spending will rise from $4.4t to $4.6t. Why doesn’t it feel that way?
Devices will be the only category to decline. CDW, a $20b distributor of software & hardware, announced earnings last week & their results echoed these. CDW has a significant government & education business.
Every company will be an AI company. Every startup needs an AI strategy - not just for fundraising or press appeal.
User expectations have changed. When one email composer window autocompletes sentences, every email product will need to follow. When one customer support bot provides a meaningfully better experience to answer questions, every competitor will match it.
ChatGPT & Midjourney have educated hundreds of millions that we can & should expect more from software. Now those teeming masses will demand it.
In The Figures that Will Move the Venture Capital Market in the Next 3-5 Years, I wrote about the correlation between interest rates & venture capital investing.
In the past two years, the correlation has grown stronger from -0.46 correlation to -0.51.
The correlation is strong enough to build a simple prediction of early stage venture capital activity in 2023.
For each of the 14 weeks, more people searched for ChatGPT than Taylor Swift according to Google Trends data.
You won’t find people outside of tech hubs googling for microservices or layer 2 blockchains or serverless databases with any great frequency. But Minnesotans & Idahoans & Vermontans are searching for ChatGPT.
Blockchains are databases application developers use to build novel user experiences. Just as hundreds of different databases exist in web2, different blockchains have evolved in web3.
In September, I published the State of Web3 in Data. I’ve been watching one of those charts very closely : slide 25 which tracked L2s & L1s.
Layer 2s (or L2s) like Arbitrum & Optimism, sit atop Ethereum (an L1). They provide faster & less expensive transactions for application developers.
There are 4 questions a startup should ask themselves about building a startup that uses generative AI.
I presented those questions & my views on their answers at Saastr’s Workshop Wednesday.
I had a blast putting this deck together. I started with a few sentences, uploaded them to gamma.app to outline the presentation, popped over to Midjourney to generate images along the story line, & published it in IA Presenter.
The video is here. The last slide contains the prompts for the images in the presentation.
I’m thrilled to announce the debut of Theory Ventures & our first fund of $230m.
I’ve spent my career as a student of startups. I’ve worked for two, invested in many, seven of which became unicorns. I study them, benchmark them, analyze them, interview their leaders to understand their mechanics & share what I’ve learned on this blog.
The throughline of my work has been in identifying technological discontinuities that enable go-to-market advantages, & it’s patently clear to me that we are in the midst of another revolution now.
Burn multiple measures the capital efficiency of a startup. Burn multiple calculated like this : net burn divided by net new ARR. Startup burn multiples have changed markedly in 2023.
Companies with more efficient burn multiples between 1-2 plan to increase their net burn per new dollar of bookings by between 14-40%. In contrast, startups with higher operating expenses will reduce their burn multiple by 34-70%. The average startup with a burn multiple of 5 plans to reduce to 1.5.
As generative AI captivates Startupland, startups will do what they have always done: integrate new technology to build transformative businesses.
Incumbents have seized the moment with Microsoft, Adobe, & others integrating generative AI into their products quickest.
In response, startups must develop moats to stake out their market. What are these moats?
At the moment, capital & technical expertise create competitive advantage. Models require millions of dollars & technical expertise to deploy: document chunking, vectorization, prompt-tuning or plugins for better accuracy & breadth.
Sales cycles shifted dramatically in 2023. Slower sales cycles create pipeline shocks & startups are feeling the impacts.
The average startup saw a 24% increase in sales cycle from early 2022 to 2023. 60 day sales cycles are now 75 days.
But the latency isn’t evenly distributed. Startups selling to enterprises have increased 36%, twice those of Mid-Market & SMB focused companies. This figure is statistically significant with a p value of 0.0005.