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Recently, Guy Podjarny, President of Snyk & I talked through an investor’s view of AI’s impact on security & data.

What’s changed with AI?

A reckless data security leak threatens the career of a head of data, just as much as a mismanaged security incident ends a CISO (chief information security officer)’s. Clorox, Uber, SolarWinds, Target, JPMorgan, Facebook, Equifax, & CapitalOne : All these CISOs have been fired in the last ten years for security issues.

I can remember at each career inflection point the person who took a bet on me.

When I joined Google it was Kim. When I moved to product, Marissa & Brian. Then to Redpoint, it was the early stage team : Geoff, Tim, Chris, & Scott. Lloyd & Frank at Looker for their Series A. Today, it’s the team & the LPs at Theory.

That’s the very beginning of the list.

Last week, OpenAI suffered management team infighting over the key direction of the company. The schism inside the company resulted in Sam Altman’s firing & Greg Brockman’s departure.

This outcome injects uncertainty in the market including key partners (Microsoft & investors) & employees about the company’s future.

Microsoft, recognizing risk with a key technology partner in one of their most important markets, stabilizes its future by hiring Altman, Brockman, & others. Microsoft has rights to the OpenAI models, has diversified its algorithm providers beyond OpenAI & counts among its staff several of the most critical people in AI.

How much does AI cost? It depends on a a few different dimensions, but for a typical use case where a user enters a query & expects about a 200 word response, the cost varies from 0.03c to 3.60c.

Different models cost different amounts. Also, the size of the context window is an important factor.

The context window is a fancy way of saying the quantity of background information the user can provide to the AI.

12 years of zero-interest rate policies & then a sudden increase to 5.5% woke us all up to the Wall Street mantra : Don’t fight the Fed.

I’ve been reading to understand the history of the Fed & found some surprising facts from these two books : The Repo Market & Broken Money.

The US Federal Reserve Banking system is about 115 years old. It was created after the Panic of 1907. In October of that year, companies couldn’t borrow money because a trading firm called the Knickerbocker Trust went bankrupt trying to corner the copper market.

Recently, I was on the HR Heretics podcast and we talked about the increasing efficiency of software companies (in addition to other topics including the implications of AI for executives, how to diligence a candidate, & what board members expect of their people leaders).

I realized it has been a decade since I’ve updated revenue-per-employee metrics.

Here are the trends across the group of 12 publicly traded software & infrastructure companies (which are the fastest growers or most-highly valued) over the last 5 quarters.

Vendr’s quarterly report on SaaS purchasing trends summarize the sentiment of the software market. Q2 was tough but Q3 is looking up.

The average sales cycle has lengthened from 32 to 44 days for net new software, a 37% increase.

Slower cycles & fewer new purchases : 19% of software purchases are net new in 2023 ; just 17% in Q3. Still about 1-in-5, but down from 30% last year.

At the IMPACT Summit yesterday, I shared our Top 10 Trends for Data in 2024.

1. LLMs Transform the Stack : Large language models transform data in many ways. First, they have driven an increased demand for data and are causing a complete architecture inside companies. Second, they change the way that we manipulate data. Analysts will use automated data analysis, and it will be an expected tool in every product : notebooks, BI, databases, etc.

If you’re curious about the evolution of the LLM stack or the requirements to build a product with LLMs, please see Theory’s series on the topic here called From Model to Machine.

On the heels of yesterday’s post, DataDog announced earnings . The company exceeded its plan for the quarter & raised the plan for next quarter.

DataDog leads the cloud monitoring & security space. They will generate about $2b in revenue this year. That they see strength in their customer behavior is a good sign for Startupland broadly.

Here are some takeaways from their call yesterday :

The cost-cutting activities that dominated the start of this year are plateauing, which should drive better predictability in bookings.

Six quarters ago, profitability became the most important factor to public software valuations.

14 public software companies have reported earnings in this quarter so far. These companies’ earnings surprise has been prodigious.

Earnings surprise is the difference between how much the market expected in earnings compared to actuals. Positive surprise means a better result than expected.

The average earnings surprise so far is 48%. Contrast that figure to 1% revenue surprise for the same companies.