Issue #1
Welcome to the first edition of The AI Native Engineer by Zencoder, this newsletter will take approximately 5 mins to read. If you only have one, here are the 6 most important things:
When OpenAI or Anthropic train a model, the headlines shout about billions spent in GPU time. But once that model is live every user prompt, every API call, every agent thinking loop costs real money. That’s inference, and it’s quietly becoming the biggest line item in the AI economy.
Training is a one-time marathon, you feed terabytes of data to teach a model how to think. Inference, by contrast, is every sprint that happens after: each time the model “thinks,” “writes,” or “reasons” for you, that’s an inference.
As one Reddit user quipped:
“Every time you tweak a line and hit send, it could cost you $1 or $2 per second.”
Multiply that by thousands of users or agents running continuously, and inference turns from a rounding error into an operational crisis.
Even when costs look small say, $0.05–$0.25 per query, they multiply fast in agentic workflows. As another engineer wrote:
“The moment you chain 20 reasoning steps, your cost graph looks like a hockey stick.”
Yes, per-token prices are dropping. NVIDIA, AWS, and others are optimizing hardware and throughput. But three invisible forces are pushing total cost upward:
Volume: More agents, more users, more concurrent requests, it all compounds.
Complexity: Multi-agent reasoning chains that run 10–100× more inferences than single prompts.
Availability: CTOs are learning that 99.9% uptime and low latency mean over-provisioning expensive infrastructure.
A16Z called this the “LLMflation paradox”:
“We made each inference cheaper, but made 1,000× more of them.”
The smartest companies now treat inference like a performance budget, not an afterthought. A few notable trends are emerging:
Model optimization & quantization: Smaller, faster, cheaper models fine-tuned for task-specific inference.
Caching & token reuse: Systems that store high-frequency results or leverage partial inference to skip redundant compute.
Hybrid infrastructure: Teams like DeepSeek and Anthropic are deploying a mix of cloud + on-prem + edge to reduce latency and cost.
Mixture of Experts (MoE) architectures: These activate only parts of a model per query, cutting inference cost by up to 70%.
(IntuitionLabs: “DeepSeek Inference Cost Explained”
As one engineer put it on Reddit:
“The new game isn’t just building smarter models — it’s making them think cheaper.”
Sora videos are now stumping deepfake detection tools
Before Git and the Pull Request (which, surprisingly, is not a Git feature but a feature of platforms like GitHub built on top of Git), there was just... email.
The story of collaborative code starts with the early development of the Linux kernel. When Linus Torvalds needed to manage contributions from thousands of developers, he relied on a massive, complex system of patches sent over email. This process, while decentralized, was slow, cumbersome, and incredibly difficult to audit. Each patch had to be manually reviewed, applied, and tracked.
The proprietary version control system BitKeeper solved this problem for the Linux community for a few years, but when its free license was revoked in 2005, Linus was forced to create something new: Git.
Linus’s core design goal was not convenience, but speed and data integrity. He wanted a system where the "commit" was a cryptographic snapshot of the entire repository history, making it impossible to change old versions without notice. Git fundamentally changed the nature of development, formalizing the idea of a branch as a safe space for experimentation.
The Pull Request, invented by platforms like GitHub, leveraged this concept. It’s essentially a friendly ticket: "Please, pull my branch into yours." It transformed the manual, messy email-based patch exchange into a structured, collaborative review platform. This single UI innovation is what unlocked modern velocity.
Reflection: The PR moved us from a solo-developer world to a highly-collaborative one. With AI agents now able to review code, triage issues, and even write the first draft of fixes, what do you think will be today’s "Pull Request moment" for AI-native code collaboration?
Whether you're exploring Zencoder for the first time or already building with it, this is a great chance to connect, ask questions, and share feedback. We’ll chat about best practices, common challenges, and what’s new in the Zencoder ecosystem. Bring your questions, ideas, or just stop by to say hi — we’d love to hear from you!
Wed Oct 29th · 10:00 PM
Zencoder Community Office hours
October 29th, 2025 - Join here