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Last week at Saastr 2023, I had the privilege of hosting a panel with Maggie Hott, GTM leader at OpenAI, Sharon Zhou, cofounder & CEO of Lamini, & Jordan Tigani, founder & CEO of MotherDuck talking about the implications of AI for the software industry broadly.

Four themes resonated throughout the session.

First, large language models like GPT-3 are making AI accessible to the masses. These advanced models allow people to interact conversationally with technology. For example, instead of writing complex SQL queries, users can get database results by describing what they want in plain English. The upshot is that AI can now be utilized by domain experts, not just computer scientists, which opens up the markets for previously technical products to upwards of 10x the user population.

I recently stumbled across a book that profoundly impacted me over 20 years ago. Called the Art of Possibility by Rosemary Stone Zander and Benjamin Zander, conductor of the Boston Philharmonic Orchestra, there it was in the sharing library of a local cafe, nestled underneath a few spy novels.

There it was the bright yellow cover, a banner of optimism contained within the pages.

In the first chapter of book, the authors share a story that encapsulates one of the core themes. Two salesmen explore business opportunities in a remote part of the world to gauge the opportunity there.

I’ve been fascinated by dictation software for many years. Speaking has struck me as the most natural interface. It also happens to be at least 3 times faster than typing.

When OpenAI released their dictation model Whisper, I was keen to try it.

So I installed it on my MacBook Pro.

Over the last few weeks, I’ve been using it. As expected, it’s sensational. In fact, this entire blog post is dictated using Whisper.

Historically, software-as-a-service (SaaS) has been built on databases with structured data, as you might find in an Excel spreadsheet.

But the ability of large language models to extract insights from unstructured information changes this architecture : data repositories like data lakes are becoming essential parts of modern SaaS stacks.

For example, next generation marketing software might ingest structured data from Google Analytics and lead capture software as before. But it also might start to analyze customers’ behavior in online webinars, conversations with customer support and sales teams, and more - all of which is unstructured data stored in video & raw text & audio files.

Open source software has evolved from a movement into a big business. Gitlab, Confluent, Databricks, MongoDB, Elastic, Snyk, Hashicorp, Mulesoft, have become huge companies using open source software.

Across, just these they have generated about $100b in market cap.

Early on, open source pioneers viewed software freedom as an end unto itself. They encouraged permissive licensing so anyone could use, modify, and distribute their code.

Large-language models have transformed how millions interact with products : from customer support to code generation to legal document analysis.

These new engagement models invite users through a meaningfully different product journey. Every application now speaks English. Understanding user behavior is essential to building great products.

How should a product manager gauge the customer experience? Lots of new challenges arise :

• First, the models are non-deterministic which means sometimes the same question phrased a bit differently produces unexpected results.
• Second, users can input anything they like which can lead to model drift & unexpected outputs potentially including might include PII (personally identifiable information) or other sensitive content.
• Third, how should a PM think about the user journey through a conversation? What is a successful interaction? It’s no longer straightforward clicking through five screens.

Context.ai is a product analytics platform for LLM-powered applications. Context.ai gives product builders an understanding of how users engage with their products and visibility into product performance, so they can build better experiences.

On Friday, Klaviyo filed their S-1 - one of the first software companies to do so since the beginning of the economic downturn in late 2022. It’s a marvel of business with strong growth, great efficiency & the potential to reignite interest in SaaS IPOs.

Klaviyo is a customer data platform company which ingests marketing data & empowers their customers to automate their marketing with data & AI. The company raised $455m in venture capital, but has used only $15m, growing nearly entirely on profits.

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.

I remember landing in Tokyo, paying for the subway with a mobile phone system called Oyster, just a few weeks after the first iPhone launched. Today, that’s quotidian for millions, but not sixteen years ago.

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.

NVDA’s profitability is remarkable. Over the last year, the company’s operating costs haven’t increased more than 15% yet, net income (profit) has quadrupled. The company’s pricing power at work : limited supply & increased demand pushes prices higher.

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]