Spotify Rewrote the Role of Engineer

OpenAI just made Codex faster. Not smarter. Faster. It launched a lightweight version powered by a dedicated chip. The goal isn’t better answers. It’s instant ones. And that changes how developers behave. Also alongside, Spotify revealed something surprising on its earnings call. Its best developers haven’t written code since December. They’re not slacking. They’re directing AI instead. And that may be the future of software engineering.

In today’s post:

• Spotify’s best engineers stopped coding

• AI just entered the farm

• OpenAI just optimized for speed

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UPGRADE

AI just changed what a developer actually does

Spotify shared something unusual on its earnings call this week. Its top engineers haven’t written code since December. Five months ago, that would sound absurd.
Today, it sounds strategic. And maybe inevitable.

Here’s everything you need to know:

• Spotify co-CEO Gustav Söderström said their best developers haven’t typed code since December.

• Instead, engineers use an internal AI system called Honk.

• From Slack on a phone, they can ask Anthropic’s Claude Code to fix bugs or ship features.

• The updated app can be reviewed and merged before they reach the office.

• Spotify shipped 50+ features in 2025, including AI playlists and audiobook matching tools.

• They are also building a music-behavior dataset competitors cannot easily replicate.

• That dataset improves every time their models retrain on real listener behavior.

Notice the shift. Developers are no longer typing syntax. They are directing systems. The leverage moved from hands to judgment. AI won’t replace great engineers.
It will expose average ones. When code becomes cheap, taste becomes scarce. Knowing what to build matters more than building it. The real question isn’t whether AI writes code. It’s who decides what gets written.

POLICY

Agriculture is becoming a software problem

The US National Science Foundation made a quiet move this week. It announced a new AI program for agriculture. It didn’t sound dramatic. No flashy demo. No billion-dollar headline.

Here’s everything you need to know:

• National Science Foundation launched the AI-ENGAGE initiative to modernize farming with AI.

• The program invests $2.4 million across six international research projects.

• It operates under the Quad partnership between the US, India, Australia, and Japan.

• Researchers from at least three Quad nations collaborate on each project.

• The goal is smarter crop management, climate resilience, and resource efficiency.

• AI will help farmers predict yields, manage soil, and reduce waste.

• Agriculture is shifting from instinct-driven to data-driven decision making.

This is bigger than it sounds. For decades, farming relied on experience and tradition.
Now it relies on models and machine learning. That changes who holds leverage. The farmer with better data wins. The nation with better models scales. AI in agriculture won’t feel futuristic. It will feel incremental. But incremental improvements in food systems compound fast. The real shift isn’t automation. It’s intelligence layered onto every acre. And once that starts, it won’t stop.

RESEARCH

The real AI race is about latency, not intelligence

OpenAI launched a lighter version of Codex this week. It’s called GPT-5.3-Codex-Spark. At first glance, it sounds incremental. A smaller model. A faster response.

Here’s everything you need to know:

• OpenAI released GPT-5.3-Codex-Spark for rapid coding workflows.

• The model is powered by a dedicated chip from Cerebras.

• That chip is the Wafer Scale Engine 3 with four trillion transistors.

• Spark is built for low latency and real-time collaboration.

• It complements the heavier Codex model for deeper tasks.

• Sam Altman teased the release before launch.

• Cerebras recently raised $1 billion at a $23 billion valuation.

This isn’t about making models smarter. It’s about making them feel instant. When AI responds in milliseconds, behavior changes. Developers stop batching requests. They start thinking alongside the model. That’s a different product entirely. A slightly weaker model that answers instantly often beats a smarter one that lags. The future of AI tools won’t just be judged on output quality. They’ll be judged on how fast they disappear into your workflow. And once that happens, you won’t think about using AI. You’ll just think.

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