Aakash Gupta published Product Requirements Documents (PRDs): A Modern Guide.
credit: Wired
There at the top of the post is a photo of the 2007 APM class, coincidentally my class (I’m there on the right), departing San Francisco for a trip that would take us to Tokyo, Beijing, Bangkok, Bangalore & Tel Aviv. Steven Levy, a reporter, traveled with us & documented the trip.
If we needed another exclamation point on the tremendous growth opportunity, NVidia’s earnings punctuated the euphoria with gusto.
Revenue grew 88% in a quarter, nearly doubling. The company had projected $11b & exceeded projections by $2.5b or 26%.
The Data Center business, which sells to clouds & consumer companies drove nearly all of the growth, surging 141% quarter-over-quarter to $10.3b & nearly 3x annually.
The post-Covid surge in venture capital pushed valuations & activity to record breaking heights. Since then, investing activity dropped precipitously.
When a collapse follows a surge, mean reversion suggests behavior should revert to a reasonable baseline. By building a linear model we can hazard a guess of when that might be.
The blue line shows actual Seed, A, & B activity by quarter. The red is a linear model based on data from 2010 to 2018 that predicts activity rates for each financing series of US & Canadian software companies. [1]
The Fed no longer predicts a recession. Economic data is turning more positive, eg housing starts exceeded forecasts. 80% of public companies beating earnings estimates - three percentage points higher than the 5 year average.
On the other hand, enough uncertainty permeates the market to depress prices. Public software companies’ share prices have fallen 10% on average in the last 30 days dotted with positive notes like the stabilization cloud growth rates for Microsoft & Google.
Classical computer programming is about rules. What does a program do when a user inputs 4 + 7? Or a credit card number? Or clicks on a button. I spent years as a programmer translating human rules into rules the computer would understand.
One day, with enough experience, the number of exceptions to rules dawns on a programmer. It’s no different than english.
Imagine writing a text-to-speech program :
Home & tome rhyme. So does comb. But not come.
Carta released their state of the market report. A few data points stood out to me
Downrounds constitute 20% of all rounds, up 2x from historical norms. Bridge rounds account for 38% of all rounds in Q2. Cooley reported 2.9% of rounds recapitalized the company. Recaps effectively delete the previous cap table & start a new one.
These are all signs of the harder fundraising times.
The glass-half full view is that 80% of all rounds are flat or up.
What will a venture capital turnaround feel like? Will it be gradual or sudden? What will change the sentiment in the market?
In 2008, I had just become a venture capitalist. Three months later, Lehman fell & the Global Financial Crisis started. CNBC played in the lobby of our office & I remember watching domino tip domino into a cascading collapse.
Activity slowed to a crawl during the subsequent & I tried to understand their impact on private markets for the first time.
If a startup raised a top quartile Seed round, Series A, B, & C, they typically would have grown headcount by about 6% in the last twelve months. The headcount growth rate for all other companies? About double at 12%.
Here’s the catch : the difference isn’t statistically significant.
In fact, comparing the headcount growth rates across top quartile fundraisers to bottom 3 quartile fundraisers, the result is the same. No statistically significant difference in headcount.
Earlier today, New Relic announced its sale to Francisco Partners & TPG for $6.5b.
The acquisition is notable for two reasons.
First, it accelerates the momentum within the technology buyout space.
At its current pace, technology buyout volumes of venture-backed technology companies will tie or exceed the ten year high, charted in 2022 of about $20b.
PE buyouts provide 2023’s slower M&A market liquidity & activity, perhaps will begin to spur strategic/corporate acquirers into action.
AI will automate 25-50% of white collar work including data analysis. Does that mean will data teams shrink in size?
On the contrary, while AI can automate some work, it will also demand much more from data teams.
Typical tasks - writing SQL & charting data - will become mostly automated. This ease of use will enable data teams to focus elsewhere : on the sea of demand for data coming from the edges of the company.