Top 10 in Tech - What to know for Week ending October 16, 2026

SaaS METRIC OF THE WEEK

I bet if you asked any founder their growth rate, you would probably get it instantly. But if you ask how long it takes to earn back what they spent winning a customer.........crickets. But that's the number investors check first. Median B2B SaaS is 16 months, top quartile under 6.

MANAGEMENT

Ever feel weird running a startup like it's a friggin' classroom? If you nodded your head, you're not alone. This piece unpacks "educational trauma" — why many leaders default to teacher mode, and how to build an adult-to-adult culture instead.

CAPITAL

Anyone who's raised here has heard the dreaded phrases: "Too early," "too crowded," "not in our thesis" - VC-isms. This article breaks down the _real_ reasons VCs say no. 10 rejection themes, decoded with some brutal clarity. A must-bookmark/read in prep for your next raise.

CODE REVIEW

Code review is DEAD! Here are 16 things Thorsten Ball (he literally wrote the book on building interpreters) believes about where software development is heading. A few that'll start a fight in your engineering channel: code review dies, unit tests might too, and most bugs won't be coding bugs - they'll come from asking for the wrong thing.

SCALE

Think $250K ARR per head is great? According to this ICONIQ report, the top AI companies (at $100M+ ARR) do $655K, top quartile is $890K. That's a $200M company running with about 300 people vs. 800. Also, at $100M+ ARR, the median grows 115%, keeping 90% of revenue before expansion, as it does at $655K ARR per employee.

INFERENCE

Tech word of the year probably - Tomasz Tunguz argues that AI inference is about to become the most important market in software, and that every app is about to become an AI reseller. Inference will hit about $350B by 2027, nearly 2x the database market. If AI usage starts eating half your revenue, what's left of your margin?. The main models are commoditizing though, prices are down 41%, and token use up 50% since July.

PRICING

Fast follow from #6, here's that margin squeeze from the inside. If you're pricing an AI product right now? Read this - the real unit economics for the AI product Candor: $2.50 to run an interview, $7 to buy one, and a free trial that costs him $68 a signup. The whole pricing model is in a public spreadsheet (you can copy). Oh but here is the gotcha nobody budgets for - upgrading the model made the product better but burned 25-35% more tokens.

TOP 100

a16z's Top 100 Consumer AI Apps is out, and this time it adds actual US card-spend data - which is key! Only 4.5% of US consumers pay for ChatGPT, Gemini, or Claude. The top 1% of payers average \~$903/month and outspend the entire bottom half combined.

MOAT

Another follow from #6 - if the models are commoditizing, what's left to defend? Dan Hockenmaier has a one-hour Moat test for ya! Find the compounding advantage - switching costs, network effects, economies of scale, or data. Speed, brand, a great team, and funding don't count (he calls those "fuel"). Then check what the team actually works on every day (not what the strategy deck says).

CASE STUDY

YOU SUCK - 12 companies whose first product flopped. YouTube was a video dating site, so few users that it turned to Craigslist. Slack was the internal chat tool inside a failed game called Glitch. Bill Kerr's roundup of first products that were dumpster fires.