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Great companies transform a technology innovation into a go-to-market advantage.

In the last five major cycles (internet, social, mobile, cloud, web3), startups seized the new technologies of the era to create a distribution advantage.

Google led to search-engine optimization, which startups like Expedia & Zillow grew to become public companies. Mastering social distribution (sheep-throwing in the early days) on MySpace & Facebook’s propelled YouTube & Zynga, not to mention the social networks themselves.

I’ve been tinkering with LLMs.

First, I created a chatbot using all my blog posts. Then I created a model to produce blog entries based on my writing.

As I went through this process, I asked myself some questions along the way :

• Does the ML model ingest my posts & keep them? What if I’d like to remove them from the logs or training set or output for others?
• The blogbot often returned no answers for questions I thought would be straightforward. How can I ensure the response rate is 99%+ before launching?
• Which post is better : hand-written, ChatGPT, or custom-trained models? How would I judge? More views? Written in my voice with alliteration & rhetoric? Use of the & instead of “and”? Injects links to other related posts?
• When is a blogbot a better user experience than search? With search, I can know that I’ve plumbed the depths of the blog, looking at every relevant post for an answer. How do I do that with a bot?
• The latency for both is significant : 5 to 25 seconds depending on the query. Google observed 400ms increase in latency decreases traffic by 20%. Will users be more patient with bots than with search?
• What if I reimagined the home page of tomtunguz.com as a chatbot interface rather than a list of all posts? That UI would personalize the experience for each visitor & each session, but it would hamper browsing? Which is the more important use case?

I imagine many product teams are asking analogous questions about how to leverage these new models.

Which database generated a -$1.8b loss & within a year produced $130m in profits? Ethereum.

Last year, I compared MongoDB & Ethereum growth to draw a parallel between two major database companies. This year, web3 activity has fallen dragging revenues along for the roller coaster ride.

Ethereum revenues have collapsed 82%, which makes the earnings change that much starker. What other business suffered a loss of 80% of revenue & went from burning $2b per quarter to producing more than a hundred million in profit?

I asked ChatGPT about the numbers 1 & 4. Which one is bigger?

Sometimes, 1 was bigger. Othertimes, 4 was bigger. Sharon Zhou ran this experiment at scale to showing the order of yes & no matters in the response.

This is called a non-deterministic or stochastic answer. Similar inputs do not consistently produce identical outputs. The answers have inconsistent logic.

When a startup is born, founders lead sales. Design partners, pilots, or founder-led sales - they have many names - these customers work with a startup to solve a problem.

Conversations about contracts & pricing are kicked down the road until the customer sees value & the path to capture it.

Said another way, founder-led sales are sales engineering sales, not account executive sales.

As a company scales, founders transition sales to others. The default behavior is for founders to pass sales to account executives.

I’m watching public company earnings to identify early trends in the software market to inform startups’ plans for 2023. Yesterday, Microsoft & Google announced earnings. Amazon, Cloudflare, & Mongo announce later this week.

Google Cloud Platform (GCP) & Microsoft Azure had strong quarters with about 28% annual revenue growth each. The decline in growth rate from the last two quarters continues with each service seeing another 4 percentage point drop in growth rate each.

Software spending in 2024 should rise 12%, increasing from 9% last year according to Gartner. Overall spending will rise from $4.4t to $4.6t. Why doesn’t it feel that way?

Devices will be the only category to decline. CDW, a $20b distributor of software & hardware, announced earnings last week & their results echoed these. CDW has a significant government & education business.

Every company will be an AI company. Every startup needs an AI strategy - not just for fundraising or press appeal.

User expectations have changed. When one email composer window autocompletes sentences, every email product will need to follow. When one customer support bot provides a meaningfully better experience to answer questions, every competitor will match it.

ChatGPT & Midjourney have educated hundreds of millions that we can & should expect more from software. Now those teeming masses will demand it.

In The Figures that Will Move the Venture Capital Market in the Next 3-5 Years, I wrote about the correlation between interest rates & venture capital investing.

In the past two years, the correlation has grown stronger from -0.46 correlation to -0.51.

The correlation is strong enough to build a simple prediction of early stage venture capital activity in 2023.

For each of the 14 weeks, more people searched for ChatGPT than Taylor Swift according to Google Trends data.

You won’t find people outside of tech hubs googling for microservices or layer 2 blockchains or serverless databases with any great frequency. But Minnesotans & Idahoans & Vermontans are searching for ChatGPT.