Welcome to the fifteenth edition of The AI Native Engineer by Zencoder, this newsletter will take approximately 5 mins to read.
If you only have one minute, here are the 5 most important things:
The biggest story in AI this week isn't a new model; it's a supply chain bottleneck. Reports from Davos and recent quarterly earnings confirm that a global memory shortage is reshaping the AI economy.
For the first time in 30 years, Nvidia is skipping an entire year of gaming GPU releases. The anticipated "RTX 50 Super" series has been shelved. Why? Because the operating margins for AI chips sit at 65%, compared to 40% for gaming. Nvidia is funneling its limited supply of HBM (High Bandwidth Memory) exclusively into the "AI Supercomputing" sector.
As compute becomes a scarce resource, the industry is pivoting from "Brute Force" to "Cognitive Density." DeepSeek’s R1 success has triggered a massive re-evaluation. Engineers are now being tasked with packing more "reasoning" into smaller, cheaper architectures. If you can’t buy more GPUs, you have to make the ones you have 10x smarter.
While hardware is constrained, software is exploding. ChatGPT Atlas and Perplexity Comet are redefining the web. We are moving from "Tabs" to "Tasks." Instead of navigating a portal, you provide an intent ("Book my London flight under $500"), and the browser agent handles the forms, auth, and checkout.
Try out Claude and OpenAI new models in Zenflow - using Blast Mode, compare the output by each model for the same task and be the judge to their performance.
Capital is moving into "Physical AI"—the nervous systems of factories and the defense of airspaces.
Waymo raises $16 billion investment round.
Lawhive, a startup using AI to reimagine the general practice law firm, raises $60 million.
ElevenLabs raises $500m Series D.
Business identity startup Duna raises €30m.koko
Before we had 50-layer "Deep Learning," we had the SNARC (Stochastic Neural Analog Reinforcement Calculator).
In 1951, Marvin Minsky and Dean Edmunds built the first artificial neural network. It didn't run on silicon; it was built with 3,000 vacuum tubes, motors, and clutches. The "network" consisted of 40 "neurons" designed to simulate a rat learning its way through a maze.
Whenever the "rat" made a correct turn, the operator would push a button to strengthen the magnetic clutches (the "weights"). It was the first physical implementation of Reinforcement Learning. 75 years later, the same principle—strengthening a connection based on a successful outcome is exactly how modern models learn to "reason."
Reflection: Minsky’s "neurons" were physical clutches you could touch. Today, they are floating-point numbers in an H100. Does the loss of "physicality" make it harder to trust the reasoning process?
Inspired by the latest trends, this session focuses on the "Architecture Theme"—moving beyond experimental chat into resilient, multi-agent systems.
Every team works differently - your workflows should too.
In this session, we’ll show you how to build Zenflow workflows that mirror your team’s real process, not force you into rigid templates. You’ll learn how to design custom steps, define artifacts, and orchestrate agents in a way that fits how your engineers actually ship.
We’ll walk through a practical, end-to-end example showing how teams use Zenflow to move from specs to execution with consistency, visibility, and control—without slowing down velocity.
What you’ll learn:
- How to design custom Zenflow workflows around your team’s process
- Defining steps, artifacts, and approvals that agents actually follow
- Turning one-off prompts into repeatable, scalable workflows
- A live Zenflow walkthrough with a real-world use case
🎯 Who it’s for: Engineering leaders, platform teams, and developers scaling AI workflows
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