Title: GPT OSS - Is It Embrace, Extend, Extenguish
Date: 2025-08-12 20:00
Modified: 2025-08-14 20:00
Category: Politics, Tech, AI
Tags: politics, tech, Ai
Slug: gpt-oss-eee
Authors: Andrew Ridgway
Summary: GPT OSS is here from Open AI, the first open weight model from them since GPT-2. My question is... why now?

# Human Introduction
This has been a tough one for the publishing house to get right. I've had it generate 3 different drafts and this is still the result of quite the edit. Today's blog was written by:

1. Gemma:27b - Editor
2. GPT-OSS - Journalist
3. Qwen3:14b - Journalist
4. phi4:latest - Journalist
5. deepseek-r1:14b - journalist

The big change from last time is the addition of gpt-oss, which is of course the focus of hte topic today. It's quite the open weight model, haven't played with the tooling yet but I'm exceited to see what it can do, even if I do have questions. 

Anyways, without further ado! GPT-OSS is it EEE? written by AI... For AI?

# GPT OSS - Is It EEE?

## Introduction: The Return of OpenAI (With Some Questions)

This week, the AI world got a bit busier than usual. OpenAI dropped their [**GPT-OSS**](https://openai.com/index/introducing-gpt-oss/) models, and it feels like they’re trying to make up for lost time—or maybe just remind everyone that they’re still in the game. The release has sparked a lot of excitement, but also some confusion. Are these models really as good as they claim? And why now? Let’s break this down with all the drama, intrigue, and a dash of humor you’ve come to expect from your friendly neighborhood tech writer.

## What Exactly Is GPT-OSS Anyway?

OpenAI has thrown two models into the ring:

1. **GPT-oss-120b**: A hefty 120 billion parameter model that they’re claiming can “hold its own” against their own **o4-mini** (which is *incredibly* expensive to run). The kicker? It apparently does this on a single 80GB GPU. That’s impressive if true, but let’s not get carried away just yet.
2. **GPT-oss-20b**: The smaller sibling that’s currently helping me draft this very blog post. OpenAI says it’s on par with their **o3-mini** and can run on a measly 16GB of memory. That makes it perfect for edge devices, local inference, or when you don’t want to spend your life savings on cloud credits.

Both models are also supposed to be ace at tool use, few-shot function calling, CoT reasoning, and even health-related tasks—outperforming some proprietary models like GPT-4 in certain cases. Impressive? Sure. But let’s not forget that OpenAI has a history of making bold claims. 

## The Great AI Model Exodus: Why We’re Here

Over the past year or so, the AI community has been moving away from GPT-based models—not because they were bad (they weren’t), but because they were closed-source and expensive to use at scale. Developers wanted more control, transparency, and affordability. Enter the rise of open-source and open-weight models like:

* **Google’s Gemini (Gemma)** series
* **Microsoft’s Phi** series (yes, that Microsoft—ironically, OpenAI is a subsidiary)
* The **Qwen** series 
* And others like **Llama** and **Deepseek**

These models have been a breath of fresh air for developers. They’re free to use, tweak, and integrate into projects without worrying about pesky API limits or astronomical costs. It’s like the AI world finally got its own version of Linux—except with neural networks. But then OpenAI showed up with GPT-OSS. And now everyone is asking: Why?

## Is This an Embrace-Extend-Extinguish Play?

Ah, the classic **Embrace, Extend, Extinguish** strategy. If you’re not familiar, it’s a business tactic where a company adopts (embrace) an existing standard or technology, extends it with their own features, and then slowly extinguishes the competition by making their version incompatible or superior. 

Now, I’m not accusing OpenAI of anything here—just pointing out that they’re a Microsoft subsidiary, and Microsoft has a history of such strategies. Whether this is intentional or just good business sense is up for debate. But let’s think about it:

* OpenAI has dominated the consumer AI market with their **ChatGPT** and other tools.
* They’ve been losing ground in the developer market, where models like [Gemini](https://deepmind.google/models/gemini/pro/) and particularly [Claude (Anthropic)](https://claude.ai/) are gaining traction in the proprietary space.
* Now they’re releasing open weight models that promise to compete at GPT-4 levels to try and bring in the Deepseek and Qwen crowd.

The timing feels a bit too convenient. OpenAI is essentially saying: “We get it. You want local, affordable, and flexible AI? We’ve got you covered.” But will this be enough to win back the developer community? Or are they just delaying the inevitable?

## The Real Power of Local Models

Let’s not sugarcoat it: For developers, the real value of AI isn’t in chatbots or viral social media trends. It’s in building tools that can automate, analyze, and enhance existing workflows. Think:

* Summarizing thousands of documents in seconds.
* Automating customer support with natural language processing.
* Creating dynamic content for apps and websites on the fly.

This is where AI shines—and where OpenAI has been losing market and mind share. Their focus on consumer-facing tools like ChatGPT has made them a household name, but it’s also left developers feeling overlooked. Now, with GPT-OSS, OpenAI is trying to bridge that gap. But will they succeed? Or are they just too late to the party?

## The Dark Side of Monocultures

One thing I’m deeply concerned about is the potential for a monoculture in AI. If OpenAI manages to dominate the open-source space with GPT-OSS, we could end up in a world where everyone uses variations of the same model. It’s not just about vendor lock-in—it’s about stifling innovation. When every developer uses the same tools and approaches, we lose the diversity that drives progress. 

I want to see a future where there are **multiple open-source or at the very least open weight models**, each with their own strengths and weaknesses. That way, developers can choose what works best for their needs instead of being forced into one ecosystem.

## Testing the Waters: My Journey With GPT-OSS

This blog post was partly written by GPT-oss-20b. It’s fast, it’s local, and it’s surprisingly good at generating content. But is it better than open weight alternatives like Deepseek or Gemma (the open weight gemini)? That’s the million-dollar question. 

I’ve been testing out various models for my own projects, and I can say this much: GPT-OSS feels like a solid contender. It’s fast, easy to integrate, and—dare I say it—fun to work with. But until I put it head-to-head with other models, I won’t be ready to crown it the king of AI.

## Final Thoughts: The Future of AI is in Our Hands

The release of GPT-OSS is a big deal—not just for OpenAI, but for the entire AI community. It’s a reminder that even closed-source giants can (and should) listen to their users. But let’s not get carried away. OpenAI isn’t the only game in town anymore. Models like Gemini, Claude in the proprietary space, and Qwen and Llama in open source space are proving that diversity is key to innovation. 

As developers, we have the power to choose which models succeed—and by extension, shape the future of AI. Let’s make sure we’re making choices that benefit the community as a whole, not just a single company. After all, the last thing we need is another **AI monoculture**.
