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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.

The hardest round to raise so far in 2023 is the Series B. Although, all the early & mid-stage rounds have fallen to levels not seen for 5-10 years.

Q1 volumes by year show broad declines across early-stage venture capital. Seeds peaked at 1500 in Q1 of 2020 falling to 155 in Q1 of 2023.

Seeds fell 64%; As 68%; Bs 86% & Cs 80% of last year’s round counts.

In late 2021, Office Hours welcomed Claire Hughes-Johnson, former Stripe COO & VP at Google. During that session, she alluded to a book sharing her expertise in building exceptional teams at two of the most successful startups.

That book, Scaling People : Tactics for Management & Company Building. is a book a management team could take to an offsite to develop or re-imagine the entire operating system of a business.

The book starts with a company’s founding documents, expands to the operating cadence, and culminates in the business’ operating system.

Within the span of a few days, Startupland has experience an extraordinary set of events. The collapse of Silicon Valley Bank & 2 two others, then the intervention of the US Treasury to backstop deposits.

During that time, founders & management teams responded with tremendous fortitude under pressure, developing contingency plans to assure the success of their businesses - yet again!

It was an inspirational moment - and a challenging one.

With all the broad macro economic shifts, startups are pursuing their core markets much differently in 2023 than in 2022. To illuminate those trends, I’ve constructed a brief, 28 question go-to-market survey here..

My goal is to understand how startups have evolved their sales, marketing, customer success, and cash management from the go-go days of early 2022 to 2023. Then to publish the results & answer questions about them at an upcoming Office Hours.

As machine learning becomes core to every product, engineering teams will restructure.

In the past, the core engineering team & the data science/machine learning teams worked separately. The core engineering team ships the product & focuses on reliability.

For most companies, the data science team analyzed data & wrote machine learning models to support the business functions : sales, marketing, customer support. These teams operated downstream of the data warehouse. But they are movin’ on up to work on products in front of the data warehouse.