Netflix invented a new role for their data team : the Media ML Data Engineer.
Unstructured data is fundamentally different. It’s multimodal & contains derived fields like embeddings, captions, & transcriptions. It’s also at least 80% of the world’s data & essential for the field of AI.
This new role highlights how one of the most important companies within the data ecosystem has evolved to promote multimodal data as core. Software engineering & data engineering are fusing.
The familiar rhythms of work are shifting beneath our feet. For the past fifteen years, we’ve lived in a strange plateau, standardizing workflows & settling into predictable patterns. The tools remained largely the same, the processes became routine, & many of us found comfort in that stability.
But something fundamental has changed.
Many of these processes can be automated, improved, & reimagined. It’s hard for anyone who has been trained for years to work one way & then be asked to do it differently.
I set out to write a very different post : one with the thesis that venture capital holding periods were lengthening relative to private equity.
But the data violates the narrative!
Conor Quigley at PitchBook ran an analysis on Theory’s behalf to answer the question: How many years from the first funding round to exit on average between PE & VC?1
The answer is it’s about the same : 5 years.
On September 4 at 5-9pm PDT in Berkeley, Hamel Husain will be leading a conversation featuring Claire Vo, Greg Ceccarelli, and me talking about how to achieve Mihaly Csikszentmihalyi’s flow state with AI.
Hamel Husain is a machine learning engineer with over 20 years of experience. He has worked with companies such as Airbnb & GitHub, which included early LLM research used by OpenAI for code understanding. He has also led & contributed to numerous popular open-source machine-learning tools & is currently an independent consultant helping companies build AI products.
Since launching EvoBlog internally, I’ve wanted to improve it. One way of doing this is having an LLM judge the best posts rather than a static scoring system.
I appointed Gemini 2.5 to be that judge. This post is a result.
The initial system relied on a fixed scoring algorithm. It counted words, checked readability scores, & applied rigid style guidelines, which worked for basic quality control but missed the nuanced aspects of good writing.
AI coding assistants like Cursor & Replit have rewritten the rules of software distribution almost overnight.
But how do companies like these manage margins? Power users looking to manage as many agents as possible may find themselves at odds with their coding agent providers.
Let’s create a hypothetical million user AI coding company & play around with some numbers.
Let’s assume this company has four pricing plans: $20 per month, $50 per month, $500 per month, & $1,500 per month. We assume a 1% conversion rate for the first two plans, a 0.5% conversion rate for the $500 per month pricing plan, & 0.1% for the $1,500 plan.1
Groupon grew from a staff of a few dozen to over 350. Revenue and bookings also grew swiftly, and the company was valued at over $1 billion after just 16 months in business, the fastest company ever to reach this milestone.[20]
A little-known Midwest company called The Point became the fastest-growing company ever up until that age.
By developing a collective coupon, the idea that if a certain number of people agreed to buy a product or a service, all of them would receive a discount, the Point surged on explosive viral growth.
One of the hardest mental models to break is how disposable AI generated content is.
When asking me to generate one blog post, why not just ask it to generate three, pick the best, use that as a prompt to generate three more, and repeat until you have a polished piece of content?
This is the core idea behind EvoBlog, an evolutionary AI content generation system that leverages multiple large language models (LLMs) to produce high-quality blog posts in a fraction of the time it would take using traditional methods.
GPT-5 achieves 94.6% accuracy on AIME 2025, suggesting near-human mathematical reasoning.
Yet ask it to query your database, and success rates plummet to the teens.
The Spider 2.0 benchmarks reveal a yawning gap in AI capabilities. Spider 2.0 is a comprehensive text-to-SQL benchmark that tests AI models’ ability to generate accurate SQL queries from natural language questions across real-world databases.
While large language models have conquered knowledge work in mathematics, coding, and reasoning, text-to-SQL remains stubbornly difficult.
Perplexity AI just made a $34.5b unsolicited offer for Google’s Chrome browser, attempting to capitalize on the pending antitrust ruling that could force Google to divest its browser business.
Comparing Chrome’s economics to Google’s existing Safari deal reveals why $34.5b undervalues the browser.
Google pays Apple $18-20b annually to remain Safari’s default search engine¹, serving approximately 850m users². This translates to $21 per user per year.
The Perplexity offer values Chrome at $32b, which is $9 per user per year for its 3.5b users³.