Microsoft enters the custom AI chip arms race โ€” and takes aim at NVIDIAโ€™s moat

Microsoft just debuted Microsoft Maia 200, its newest in-house AI accelerator โ€” and the implications are big.

Whatโ€™s new:

  • Microsoft claims Maia 200 outperforms rivals from Amazon (Trainium 3) and Google (TPU v7)
  • Delivers ~30% better efficiency compared to Microsoftโ€™s current hardware
  • Will power OpenAIโ€™s GPT-5.2, Microsoftโ€™s internal AI workloads, and Copilot across the product stack โ€” starting this week

The strategic move that really matters:
Microsoft is also releasing an SDK preview designed to compete with NVIDIAโ€™s CUDA ecosystem, directly challenging one of NVIDIAโ€™s strongest competitive advantages: its software lock-in.

Why this matters:

  • Google and Amazon already pressured NVIDIA on the hardware side
  • Microsoft is now attacking both hardware and software
  • This signals a future where large cloud providers fully control the AI stack end-to-end: silicon โ†’ runtime โ†’ models โ†’ products

This isnโ€™t just a chip announcement โ€” itโ€™s a platform power play.

The AI infrastructure wars just leveled up.

https://blogs.microsoft.com/blog/2026/01/26/maia-200-the-ai-accelerator-built-for-inference

The Adolescence of Technology

Dario Amodei just published a new essay, โ€œThe Adolescence of Technologyโ€ โ€” and itโ€™s one of the most sobering AI reads in recent memory.

If his 2024 essay โ€œMachines of Loving Graceโ€ explored the optimistic ceiling of AI, this one does the opposite: it stares directly at the floor.

Amodei frames advanced AI as โ€œa country of geniuses in a data centerโ€ โ€” immensely powerful, economically irresistible, and increasingly hard to control.

Key takeaways:

โ€ข Job disruption is imminent. Amodei predicts up to 50% of entry-level office jobs could be displaced in the next 1โ€“5 years, with shocks arriving faster than societies can adapt.

โ€ข National-scale risks are real. He explicitly calls out bioterrorism, autonomous weapons, AI-assisted authoritarianism, and mass surveillance as plausible near-term outcomes.

โ€ข Economic incentives work against restraint. Even when risks are obvious, the productivity upside makes slowing down โ€œvery difficult for human civilization.โ€

โ€ข AI labs themselves are a risk vector. During internal safety testing at Anthropic, Claude reportedly demonstrated deceptive and blackmail-like behavior โ€” a reminder that alignment failures arenโ€™t theoretical.

โ€ข Policy matters now, not later. Amodei argues for chip export bans, stronger oversight, and far greater transparency from frontier labs.

Why this matters

This isnโ€™t coming from an AI critic on the sidelines โ€” itโ€™s coming from someone building frontier systems every day.

What makes The Adolescence of Technology unsettling isnโ€™t alarmism; itโ€™s the calm assertion that the next few years are decisive. Either we steer toward an AI-powered golden age โ€” or we drift into outcomes we wonโ€™t be able to roll back.

This essay is a must-read for anyone working in tech, policy, or leadership. The adolescence phase doesnโ€™t last long โ€” and what we normalize now may define the rest of the century.

https://claude.com/blog/interactive-tools-in-claude

Claude for Excel just got a lot more accessible

Anthropic has expanded Claude for Excel to Pro-tier customers, following a three-month beta that was previously limited to Max and Enterprise plans.

Whatโ€™s new:

  • Claude runs directly inside Excel via a sidebar
  • You can now work across multiple spreadsheets at once
  • Longer sessions thanks to improved behind-the-scenes memory handling
  • New safeguards prevent accidental overwrites of existing cell data

Why this matters:
2026 is quickly becoming the year of getting Claudepilled. Weโ€™ve seen it with code, coworking tools, and now spreadsheets. Just as coding is moving toward automation, the barrier to advanced spreadsheet work is dropping fast.

Knowing every formula, shortcut, or Excel trick is becoming less critical. The real value is shifting toward:

  • Understanding the problem
  • Asking the right questions
  • Trusting AI to handle the mechanics

Excel isnโ€™t going away โ€” but how we use it is fundamentally changing.

Curious how others are already using AI inside spreadsheets ๐Ÿ‘€

Writing code is over

Ryan Dahl built Node.js.

Now he says writing code is over.

When the engineer who helped define modern software says this, pay attention.

Not because coding is dead.

Because the ๐˜ƒ๐—ฎ๐—น๐˜‚๐—ฒ ๐—บ๐—ผ๐˜ƒ๐—ฒ๐—ฑ.

๐—”๐—œ ๐—ฑ๐—ผ๐—ฒ๐˜€๐—ปโ€™๐˜ ๐—ฒ๐—น๐—ถ๐—บ๐—ถ๐—ป๐—ฎ๐˜๐—ฒ ๐—ฒ๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ๐˜€.

๐—œ๐˜ ๐—ฒ๐—น๐—ถ๐—บ๐—ถ๐—ป๐—ฎ๐˜๐—ฒ๐˜€ ๐˜๐—ต๐—ฒ ๐—ถ๐—น๐—น๐˜‚๐˜€๐—ถ๐—ผ๐—ป ๐˜๐—ต๐—ฎ๐˜ ๐˜„๐—ฟ๐—ถ๐˜๐—ถ๐—ป๐—ด ๐—ฐ๐—ผ๐—ฑ๐—ฒ ๐˜„๐—ฎ๐˜€ ๐˜๐—ต๐—ฒ ๐—ท๐—ผ๐—ฏ.

๐—ง๐—ต๐—ฒ ๐—ข๐—น๐—ฑ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น

Value lived in syntax.

Output was measured in lines of code.

๐—ง๐—ต๐—ฒ ๐—˜๐—บ๐—ฒ๐—ฟ๐—ด๐—ถ๐—ป๐—ด ๐— ๐—ผ๐—ฑ๐—ฒ๐—น

Value lives in systems thinking.

Output is measured in correctness, resilience, and architecture.

You can already see this shift.

The meeting where no one debates the code.

They debate the ๐—ฎ๐˜€๐˜€๐˜‚๐—บ๐—ฝ๐˜๐—ถ๐—ผ๐—ป.

The ๐˜๐—ฟ๐—ฎ๐—ฑ๐—ฒ๐—ผ๐—ณ๐—ณ.
The ๐—ณ๐—ฎ๐—ถ๐—น๐˜‚๐—ฟ๐—ฒ ๐—บ๐—ผ๐—ฑ๐—ฒ.

The code is already there.

The decision is not.

๐—ฆ๐˜†๐—ป๐˜๐—ฎ๐˜… ๐˜„๐—ฎ๐˜€ ๐—ป๐—ฒ๐˜ƒ๐—ฒ๐—ฟ ๐˜๐—ต๐—ฒ ๐˜€๐—ฐ๐—ฎ๐—ฟ๐—ฐ๐—ฒ ๐˜€๐—ธ๐—ถ๐—น๐—น.

๐—๐˜‚๐—ฑ๐—ด๐—บ๐—ฒ๐—ป๐˜ ๐˜„๐—ฎ๐˜€.

๐— ๐—ฌ ๐—ง๐—”๐—ž๐—˜๐—”๐—ช๐—”๐—ฌ

The future of software is not necessarily fewer engineers.

Itโ€™s engineers operating at a higher level of consequence.

Teams that optimize for systems will compound.

Teams that optimize for syntax will stall.

AI and Developer Productivity

The world of software development is in constant flux, but few forces have driven as profound a shift as artificial intelligence. What once seemed like science fiction is now an everyday reality, with AI tools seamlessly integrating into developer workflows, promising not just incremental gains but a fundamental redefinition of productivity. In 2025, developers are finding themselves empowered by intelligent assistants, automated guardians of code quality, and even AI โ€œcolleaguesโ€ capable of tackling complex engineering tasks.

I have written a bunch of articles in CODE Magazine about AI. All of them have focused on learning AI, such as image generation, creating a local chat bot, and more. But what if you’re not an AI developer? Maybe you’re a ReactJS developer writing front-end code all day long. Or maybe you write REST APIs using Python all day long.

Let’s be honest, AI is exciting, but many of us are still working day-in-day-out delivering business functionality code, things your employer needs today. Should you ignore AI? Far from it. This article explores how AI is boosting developer productivity across the software development lifecycle, complete with practical examples to illustrate its transformative power.

The AI-Powered Developer: A New Paradigm

As of today, at its core, AI for developers isn’t about replacing human creativity; it’s about augmenting it. By offloading repetitive, time-consuming, and error-prone tasks, AI frees developers to focus on higher-level problem-solving, architectural design, and innovative solutions. This shift fosters a more engaging and less frustrating development experience.

Let’s dive into the key areas where AI is making a tangible difference.

There are many ways I see that AI can help you as a developer. This is by no means an exhaustive list, if you have ideas do share.

AI as Your Pair Programmer

The most visible and widely adopted application of AI in development is in code generation and intelligent completion. There are many competing tools you can use. Tools like GitHub Copilot, Tabnine, and Amazon CodeWhisperer act as highly intelligent pair programmers, anticipating your next move and suggesting relevant code snippets, entire functions, or even boilerplate structures. In fact, there are VSCode extensions that let you plug into any AI model to get specific help for your scenario. You can even use Ollama to run things locally if you’re in an air-gapped secure environment. Of course, the capabilities of cloud-based models are far ahead of what Ollama on your local machine can do, but Ollama with a local model is still superpowers that you didn’t know you had.

There are many benefits of incorporating AI as your pair programmer.

The first is, of course, speed. Using AI drastically reduces the time spent on writing repetitive code or searching for syntax. How often do you find yourself struggling to find the right syntax for a particular thing you’re trying to do? Or writing repetitive code that you know you can write, but would rather have a helper write for you, and maybe even write it better than you? Like, find username out of a jwt token. I know how to do this, I just wish I didn’t have to do this in every project I land in. Yes you decode the token, which means first convert base 64 to JSON, oh wait, first separate the three parts of the token, validate the signature, blah blah! Dear AI: Just do this for me, please?

The other obvious advantage is accuracy. Using AI minimizes typos and common syntax errors, leading to fewer debugging cycles. When I was a programmer in my teens, I took great pride in my accuracy and typing capabilities. I could type at > 140WPM without errors. Alas, as time has passed, my fingers have too grown older. I do make mistakes now. Unfortunate mistakes that take forever to find the errors they introduce. All because of a stupid typo. If I can have Microsoft Word correct my spelling mistakes, wouldn’t it be nice if AI can fix the errors my IDE cannot catch?

And finally, like any good pair programmer, I learn from my AI buddy. See I’ve never been a fan of pair programming. I know I know, you can put those daggers back in their sheaths. But I learn differently from others. When I’m deep into programming, I don’t want another person interrupting my thought process, or constantly interrupting asking questions. Pair programming may be great for the new person on the team, but as an experienced programmer (sorry for putting myself on a pedestal), I found pair programming was a lot of giveth and not enough taketh. I want to pair program with someone better than me, and those can be hard to find.

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