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Software development has operated within established boundaries for decades, with clear divisions between those who code & those who don’t.

But what happens when those boundaries dissolve overnight, & anyone can build functional applications for the price of a monthly streaming subscription?

For twenty years, professional software development meant specialized teams, lengthy sprints, & rigorous adherence to architectural best practices. Companies invested months in planning, weeks in development cycles, & substantial resources in quality assurance. The barrier to entry remained high, protecting established players & maintaining predictable market dynamics.

Another day, another earnings report with accelerating growth from AI. Snowflake earnings yesterday demonstrates yet again the impact of AI on a $4.4B revenue business.

Snowflake’s revenue has rebounded from a low of 26% to 32% quarter over quarter.

And once [customers] are on our platform, AI becomes a cornerstone of their strategy, powering 25% of all deployed use cases with over 6,100 accounts using Snowflake’s AI every week.

AI vendor revenue will double classic software in terms of new bookings this year.

This trend is so large it’s starting to have second-order effects.

MongoDB reported strong Q2 FY'26 results, delivering $591M in revenue with 24% year-over-year growth. AI is causing a second-order effect & a resurgence in growth in Atlas, the cloud-hosted version of MongoDB, which represents 74% of total revenue. We’ve seen a reacceleration within the hyperscalers already, but now the impacts are felt beyond.

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]

— Wikipedia

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.