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As machine learning becomes core to every product, engineering teams will restructure.

In the past, the core engineering team & the data science/machine learning teams worked separately. The core engineering team ships the product & focuses on reliability.

For most companies, the data science team analyzed data & wrote machine learning models to support the business functions : sales, marketing, customer support. These teams operated downstream of the data warehouse. But they are movin’ on up to work on products in front of the data warehouse.

Over the past week, I put ChatGPT to the test, enlisting it as copy-editor for a forthcoming blog post.

Here was my process:

• I dictated a blog post outline into a Google document
• I prompted ChatGPT to revise the post for clarity & pasted the text
• I copied ChatGPT’s response, edited it some more & consulted ChatGPT for three rounds just like John McPhee

So, does a robot copy-editor work in practice?

In August, I wrote about why Omni’s technology reinvents BI once more. The Omni founding team hails from Looker & Stitch. They’ve set out to solve the problems customers faced with the previous generation of BI.

Today, I can show you what this means in practice because Omni is commercially available.

Anyone who has managed a larger BI deployment has faced the challenge of managing hundreds, perhaps thousands of metrics. As more users calculate figures, consistency across teams becomes a company-wide challenge.

The FTC reportedly will sue to stop Adobe from acquiring Figma. Similar questions have surrounded Microsoft’s acquisition of Blizzard; The US Department of Justice seeks to unwind the Google/DoubleClick merger; British regulatory bodies forced Facebook to reverse its Giphy acquisition. Visa abandoned plans to buy Plaid after facing a suit.

We’re entering a period wherein governments regulate large technology businesses more actively. Startups evaluating M&A have additional factors to consider.

I served on the boards of two companies that plodded through regulatory review. Looker & Kustomer each spent roughly a year waiting for approval.

In CloudFlare’s latest earnings report, the management team highlighted the strength of enterprise buyers within their customer base.

I wondered if this were broadly true. Do public software companies with largely enterprise customer bases benefit from superior growth to their peers with mid-market or SMB focuses?

Enterprise & Mid-Market public companies have seen a relatively constant decline in growth rates through the last six years. SMB businesses benefited from a post-Covid surge when the US re-opened - a phenomenon that seems to abate with time.

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

CloudFlare’s annual growth rates haven’t slowed in the 5 quarters, unlike Microsoft, Google, & Amazon’s growth rates.

Amy Cortese published “Venture Capital, Withering & Dying” in the New York Times on Oct 21, 2001. I came across it during a Google search & reading through the article. It’s a powerful reminder of history’s rhymes.

With some market indexes off 50 percent or more and with uncertainty growing after the terrorist attacks on Sept. 11, investors have become much more discriminating.

So far this year, 29 venture-backed companies have tried initial offerings, compared with 252 in 2000. There are also fewer mergers and acquisitions.

During this recession, startups will fall into one of four categories which I wrote about in June.

But, I missed something in this post : the advantages are compounding, which is the reason the biggest will continue to win share.

It’s also the reason changing strategies can be expensive.

This is the time in the startup cycle when big balance sheets become strategic advantages.

First, companies with bigger balance sheets can sustain higher monthly burn rates, which fuels growth.

I’m watching public company earnings to identify early weaknesses in the software market. Yesterday, Google & Amazon announced earnings which completes the picture. Growth will continue to slow this year.

A year ago, AWS, GCP, & Azure averaged 44% annual growth. Today, that figure has dropped to 27%. Amazon also guided to lower growth rates in the future.

Amazon: We expect [customer] optimization efforts will continue to be a headwind to AWS growth in at least the next couple of quarters. So far in the first month of the year, AWS year-over-year revenue growth is in the mid-teens.

In his most recent earnings conference call, Microsoft’s Satya Nadella said “Every product of Microsoft will have some of the same AI capabilities to completely transform the product.” Today, Microsoft Teams launched AI features in Teams for a fee. Teams will summarize meetings, create chapters in those meetings, extract tasks, translate in real time, & develop templates for future meetings.

Incumbents have lept onto advances in generative machine learning more aggressively than any trend in recent technology history. Mobile, cloud, social - startups led each of those waves.