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Snowflake announced Arctic, their open 17b model. The LLM perfomance chart is replete with new offerings in just a few weeks. Overall knowledge performance is asymptoting as expected. It’s hard to discern the most recent dots.

One thing stands out from the announcement - the positioning of the model.

“The Best LLM for Enterprise AI”

Snowflake focuses on the model’s enterprise performance : SQL generation, code completion, & logic. This push will be echoed by others as models start to specialize.

On Friday May 3rd at 9:30 Pacific time, Office Hours will host Jordan Tigani, CEO of MotherDuck.

Jordan’s path through data starts with being the founding tech lead on Google Big Query to SingleStore’s Chief Product Officer to MotherDuck’s CEO.

He wrote the Big Data is Dead, explaining that a power law applies to most companies’ data.

One of the most innovative things about DuckDB & MotherDuck is the ability to start querying with local data & then use the cloud when necessary. This also works in reverse.

In “Do software companies actually have good margins?”, Benn Stancil makes a case for a counterintuitive point : Software companies are much less profitable than they might seem.

Why?

Because the research & development costs associated with software should be part of their cost of goods sold. Software companies don’t invest once in R&D & then sell copies of the software as we did in the 90s on CDs.

It’s software-as-a-service. So the costs of developing & maintaining the software are ongoing.

Ethereum generated $370m in profit on $825m in revenue for about a 45% net income margin.

The chart above shows both the historical performance & also explains how web3 blockchains like Ethereum generate revenue & profits.

How does Ethereum compare to other software companies?

If Ethereum were to trade on the New York Stock Exchange or the NASDAQ, it would top the net income margin (%) charts, with Microsoft, Adobe and Veeva thereafter.

Over the weekend, Tobi, the founder and CEO of Shopify, discussed the major reason investors passed on Shopify in the early days : market size.

Good supercut about why Shopify worked https://t.co/pw92KutpEn

— tobi lutke (@tobi) April 14, 2024

I remember that financing round, & I remember having the same concern, & making the same mistake. Living in the valley & driving on 101, the billboards & logo-adorned headquarters of successful companies provide a daily infusion of all the mistakes in I’ve made in guessing how a company or a market might evolve.

Recently, I was at the library & stood in front of a book that might be a good read. I fired up Gemini, asked it for a plot summary & a digest of the reviews before deciding to read it.

So I started a list : what are the ways I use AI that are new in the last year?

I often publish tables within these blog posts. I write posts in markdown, a language that requires making tables in a unique format that looks like this

First Round’s Product Market Fit Framework adds dimension & texture to a concept we all use - Product Market Fit (PMF).

PMF isn’t attained permanently, but sustained over time as companies & complexities grow. As they scale, startups move up-market, expand into new geographies, add new product lines, & develop new customer acquisition motions.

These complexities are initially linear but eventually multiply. The idea is best illustrated by example.

Looker is one of the companies highlighted 🤗 & a portion of their journey is detailed here :

On April 18th at 9:30am Pacific time, Office Hours will host Evan Cheng, founder & CEO of Mysten Labs, creator of SUI.

Evan & the Mysten team were instrumental in the creation of Meta’s high-performance blockchain techology before leaving to start Mysten.

There are few key ideas Mysten espouses about blockchains that have led to dramatic improvements in performance & usability.

First, the Move language & virtual machine form the basis of the smart contract layer.

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted.

Traditionally, product development followed a linear path.

A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent.

However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior.

Situational Management is a framework for deciding how to manage a report depending on their skill level & motivation. 

Where does AI fit today?

The answer is easy. It’s in the micromanage category where the motivation is high but the skill is low.

The computer is relentless in wanting to users’ questions or complete their code. But the skill level is relatively low - about the same as a high-school student.