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Yesterday, Databricks announced its intent to acquire Mosaic for $1.3b. Perhaps not coincidentally, Snowflake announced a deepened partnership with Nvidia to offer customers models & training on Nvidia’s Nemo platform.

Clouds are picking teams in one of the most important dislocations in software.

Cloud <- -> LLM Infrastructure
Microsoft <- -> OpenAI
Snowflake <- -> Nvidia
Databricks <- -> Mosaic
Google <- -> Anthropic
Oracle <- -> Cohere
Amazon <- -> HuggingFace

MotherDuck announced their product, managed DuckDB in the cloud, is available by invitation.

I have a raft of extra early invitations that I’ll distribute randomly on Monday. If you’d like to put your email in for a chance to receive one, just fill out this form.

DuckDB is one of the most powerful, lightweight, & fastest adopted analytical databases. MotherDuck extends DuckDB to the cloud. The video above with a french-accented Duck walks through the product with elan.

I think of marketing teams as hedge funds. Marketing teams develop a portfolio of different strategies to acquire leads.

Some days, content marketing works. A post challenging like this one hits HackerNews or a journalist covers the company.

Other days when content strategies struggle, a witty paid ad campaign entices some clicks & form fills. On some rainy Tuesday, the weekly webinar attracts an unusually promising audience.

Like an investment portfolio, any individual strategy may thrive one day & suffer the next. But as the effort broadens, leads compound at a steadier & steadier rate.

If a company adds AI to their pitch or nabs a .ai domain, will they raise money at a higher valuation?

Nvidia’s sudden surge to $1t in market capitalization in the public market suggests it could be the case.

The data tells a different story. I plotted the 75th percentile valuation of all US software companies by stage compared to AI companies over the last twelve years.

In the last month, Hubspot launched ChatSpot. Zendesk AI automates customer support. Salesforce launched AI Cloud. Adobe’s released Firefly. What do all of these launches & many other similar ones have in common?

The largest SaaS companies in the world have released English-language APIs. Anyone can write English to enter some input (e.g., “What are my top leads for this week?”) & receive an answer.

Previous APIs require learning programming languages to use & have been reserved for developers.

In the mid-2010s, every web1 company became a web2 company. This time it’s different. Many web3 companies will become web2 companies, too.

Why will web3 companies dress like SaaS mutton?

If the current pace continues, web3 startup fundraising will fall by 73% in 2023. The absence of those dollars will flatten & shrink the already modest addressable market for web3 software & infrastructure.

90% of startups have plans or have released an AI feature, 54% of those features will launch in 2023, but only 30% of companies are hiring new people to do it, according to Productboard’s survey.

These figures highlight three points:

1. AI has become an essential product component for most software companies.
2. Startups have aggressively prioritized these features on their roadmaps with 54% launching a feature this year.
3. AI deployment is sufficiently straightforward that a majority of teams won’t hire new experts to build them & will staff 1-2 people to launch them.

The last one is the most striking.

If we assume some basic productivity gains in a typical SaaS company from AI in the next 12-24 months, how much more profitable will the business be?

Sales development, content marketing, & software engineering strike me as the workstreams that will benefit immediately.

See assumptions here¹

I’ve been using large-language models (LLMs) most days for the past few months for three major use cases : data analysis, writing code, & web search¹.

Here’s what I’ve observed:

First, coding incrementally works better than describing a full task all at once. Second, coding LLMs struggle to solve problems of their own creation, turning in circles, & debugging can require significant work. Third, LLMs could replace search engines if their indexes contain more recent or evergreen data for summarization searches but not for exhaustive ones.

I’m watching public company earnings to identify early trends in the software market to inform startups’ plans for 2023. Earlier, Mongo DB announced earnings. I’m adding Mongo to the list of tracked companies for this series.

The economic slowdown which started one year ago has reduced growth rates by 31% across this group from 46% annually to 31% annually.