A year ago, enterprises balked at the prospect of deploying AI. The dominant blocker : security. By using AI, would my company lose its data as employees passed sensitive queries to large language models?
Today, buyers are more familiar & have security options : deploying AI on virtual private cloud architectures, tools to delete data from cloud AI vendors, dedicated security tools for AI, & a panoply of open source alternatives.
We’ve been tracking the performance of publicly traded AI companies since the beginning of the year. Publicly traded companies with AI products or strategies trade at about twice the forward multiple of non-AI peers.1
Within the private markets, the same is true within the Series A. GenAI startup companies raise at about 1.5-2x the post-money valuations of all software companies.2 These businesses represent about 30% of Series As in 2024.
Kevin Scott, CTO of Microsoft, framed the opportunity in AI this way : work on problems that used to be impossible, but are now really hard.
My immediate reaction : what technical problems are now possible but still hard? What can computers achieve by themselves that two or three years ago would be intractable?
But, this point isn’t purely about technical innovation.
What business problems are newly solvable?
Nearly 10 years ago, I wrote a post about the minimum viable average contract value to justify a sales team. Most companies typically hire account executives & development reps at $15k or greater in contract value because the economics of lower ACVs aren’t viable for most.
If the Modern Data Stack is dead, what replaces it?
The Postmodern Data Stack.
Post-modernism is a broad movement that developed in the mid- to late 20th century across philosophy, the arts, architecture, and criticism which marked a departure from a belief in grand narratives & an acceptance of pluralism.
This idea is taking hold in data.
Two juggernauts, Snowflake & Databricks, are challenging the dominance of Google, Microsoft, & Amazon at the biggest scales of data.
In a recent Office Hours, Baris Gultekin, Head of AI at Snowflake & a friend from Google, shared his insights into Snowflake’s approach to AI. It was a great conversation to understand how one of the leading data companies is pushing AI forward.
The video for the session is here & the audio is here.
What does advertising look like in the world of AI? In a future where generative AI dominates search, the ten blue links & five sponsored ads disappear. What if they were invisible ads?
When a user executes a search query within a generative AI search engine, the query, for example, “best carbonated beverage for health-conscious individuals,” is combined with relevant documents typically web pages. These are both passed to the AI model into the context window. The large language model produces a result using the information in the context window.
Call it Service-as-a-Software. Call it AI agents or agentic systems. There’s a brewing idea that AI will complete human labor especially in white-collar work. What attributes of a market make it attractive to pursue? Those with three attributes :
Toil, labor market shortages, and margin pressure.
Toil is repetitive work : reviewing alerts, triaging leads, data entry. Necessary but not strategic.
Jobs laden with toil tend to be difficult to recruit for & retain. Turnover rates of 30-50% are common in these roles.
My first job as a teenager was a dishwasher in a local coffee shop, earning $6 per hour. With my first paycheck, I bought a portable CD player & a pair of yellow Sony Sport headphones to listen to music. I asked the manager for a better scrubber, different soap, & a three-part sink for rinsing, scrubbing, & rinsing again. Then I fell into a groove washing dishes.
Until the owner bought a commercial dishwasher. It changed the the way the kitchen worked. Within 90 seconds, the new Hobart washer cleaned & dried the plates, cups, & silverware that would have taken me 30 minutes. I spent my time loading & unloading instead of scouring : managing the inputs & outputs of the machine.
During the era of big data, data gravity was the core strategic imperative. Wherever the biggest dataset resided, customers ran their compute workloads that generated all of the profit and revenue growth for the last generation of data companies.
Today, the battle is for AI gravity.
Why? AI requires orders of magnitude more compute than other workloads, so there’s much more money & profit to be made serving customers running them.
As software startups begin to sell agentic systems, the procurement process will change. Unlike classical software, where the application either meets the criteria (price, integration into other software, particular features) or doesn’t, agentic systems operate on a performance continuum.
Here’s a recent evaluation table for Codestral, Mistral’s open-source code generation AI. All of these benchmarks are machine-generated : HumanEval & HumanEvalFIM are not human testers - but open-source projects that evaluate AI code.1