Posts

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.

With this data, we should be able to draw some broader conclusions about the shift from growth to efficiency & determine if the buyer behavior changes in the private market parallel those in the public market. In addition, we should be able to draw some initial inferences about the impact of AI on sales teams.

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.

Tobiko is that solution.

Tobiko enables modern data transformations by calculating only what’s needed.

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?

NVIDIA’s growth is an index on the growth of AI. “Compute revenue grew more than 5x and networking revenue more than 3x from last year.”

Data center revenue totaled $26b, with about 45% from the major clouds ($13b). These clouds announced they were spending $40b in capex to build out data centers, implying NVIDIA is capturing very roughly 33% of the total capex budgets for their cloud customers.

“Large cloud providers continue to drive strong growth as they deploy and ramp NVIDIA AI infrastructure at scale and represented the mid-40s as a percentage of our Data Center revenue.”