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AI adoption is slower than expected in many spaces. Some of the reasons are straightforward, but others are more subtle.

Most leaders wants to inject AI into their business to develop a competitive advantage. There are four challenges.

1. The first challenge is understanding the technology’s ability. Because the capabilities evolve so quickly, it’s hard to keep up. If PhDs in the domain are rushing to understand the capabilities reading papers every week, how are business leaders meant to grok the state of the art?

Also, because the systems are non-deterministic, they are unpredictable. The pace of innovation, the early understanding of AI internals, & the non-determinism compound to create doubt.

It’s time for the 2024 Annual Theory Go-to-Market Survey. This is a brief 28-question survey.

Our goal is to understand how startups have evolved their sales, marketing, customer success, and cash management over the last four years by comparing these results to those through the go-go years of 2020 and beyond.

We will publish these results and answer questions about them at upcoming Office Hours.

What does it take to go public? Has it changed over the last 15 years?

We gathered data on the US venture-backed software companies that went public between 2010 & today. We corrected the trailing 12 months’ revenue at the time of IPO for inflation & plotted the data.

Before 2018, only one company IPOed with more than $200m in revenue. In fact, the median revenue at IPO at $90m. Today, the median revenue at IPO is $189m (corrected for inflation), more than double.

2012 was the year of the Seedpocalypse. Also called the Series A Crunch, a fear gripped Startupland : raising a Series A. Two years later, this indigestible excessive bolus of fundraising rounds hit the Series B market & Series Bs became the most challenging round to raise.

Whenever there are “too many” of fundraises of one type, the next round becomes the hardest to raise.

In 2024, the Series A Crunch has returned. Software companies that have achieved the previous era’s milestone, $1m or more in ARR, face a challenging Series A market. Why is this happening again?

I was chatting with a friend of mine about the advent of robotic surgery and he was lamenting the challenges associated with training younger doctors. Before robotic surgery, medical surgeons stood shoulder to shoulder alongside seasoned surgeons operating. Today, the head surgeon manipulates a robot independently while students watch through a window or video.

A lot has been written about training AI. But what about training humans?

Shouldn’t the same pattern reverberate through the work that we expect the next generation of AI to automate, including paralegal functions, accounting, computer programming, and sales development?

Databricks revealed some sensational growth this week, as they did last year. Exiting this quarter to $2.4 billion annual run rate, the company’s revenue growth is accelerated year-over-year by 10 percentage points.

Net dollar retention is a major driver of growth at 140%, which is top decile. The table above shows the other data points that we’ve collected through their press releases in the last two years.

What stood out to me in yesterday’s Apple announcement wasn’t the headline, but the subtitle.

“Setting a new standard in privacy.”

For privacy to become one of the leading selling points of software, competitive dynamics & user preferences have evolved.

The mantra repeated over the last 20 years on the internet has been privacy is dead. Users simply don’t care. People are willing to trade their privacy for free & targeted experiences.

One line of software can impact a billion rows of data.

A short SQL statement can delete a table, reformat date into the US format, or compute the average quota attainment by account executive by region over a company’s history.

As data has become a critical component of analytics & production systems, data engineers require more sophisticated tools to manage their data transformations.

Last week, public software markets suffered significant compression. MongoDB fell 24%; UIPath fell 36% ; Salesforce fell 15% ; Workday was down 11%.

Weaker revenue projections tend to cause sell-offs.

These large drops aren’t unprecedented. In 2016, valuations fell 57%. Is it different this time?

Growth rates have changed meaningfully. The 25th, 50th, & 75th percentiles for public growth rates have halved in the last 18-24 months. The grey bar indicates Covid ending & marks the beginning of the slide.

If I asked you, “When someone turns in a work assignment, how accurate is it? 80%, 90%, 95% or perhaps 100%?”

We don’t think this way about coworkers’ spreadsheets. But we will probably think this way about AI & this will very likely change the way product managers on-board users.

When was the last time you signed up for a SaaS & wondered : Would the data be accurate? Would the database corrupt my data? Would the report be correct?