A Story About a Recipe, a Car Engine, and the Future of AI - Open Weights

The Cloud Moment All Over Again

I remember when "the cloud" was this fuzzy concept people threw around in meetings. It felt abstract, a little overhyped, and for a while, it was hard to grasp what it meant for a business day-to-day. It took time for the strategy to catch up with the technology.

I'm getting that same feeling now as the term "open weights" floods the AI world.

This past week, OpenAI, the company behind ChatGPT, released a couple of new AI models using this "open-weights" approach. This wasn't a move made in a vacuum. They are responding to intense pressure from competitors like Meta, with its Llama models, and innovative players like France's Mistral AI, who have been champions of a more open approach. The race is on, and the nature of competition is changing.

Because it's such a pivotal shift, I wanted to share my perspective on it, using a couple of analogies that help me cut through the noise.

What Are "Weights" Anyway?

First, let's briefly discuss what "weights" are. Imagine the most complex recipe you can think of, with thousands of ingredients. The "weights" are the final, precise measurements of every single one of those ingredients, perfected over countless trials to create a Michelin-star dish. In AI, these are the tuned parameters that represent the model's learned knowledge.

So, what does it mean when a company "open-weights" their model?

The Ferrari Engine Analogy

I've found the best analogy is a high-performance car engine. Think of a company like Ferrari spending billions of dollars and decades of research to design, build, and test a world-class engine.

With an open-weights model, it's like they're giving you the finished, perfectly tuned engine, right off the assembly line.

You can take that engine and drop it into a car you're building in your own garage (run it on your own hardware, which is great for privacy). You can even tweak the fuel mixture or adjust the timing to get better performance at your local altitude (fine-tune it for your specific business needs). You get all the power and performance of their finished product, ready to go.

But here's the crucial part, and where the analogy gets important: they are not giving you the factory blueprints, the secret metallurgical formula for the pistons, or the multi-billion dollar robotic assembly line they used to build it. All of that—the training data, the training code, the core architecture—remains proprietary.

That's the fundamental difference between "open weights" and true "open source." You get the powerful result, but not the secrets of its creation.

Two Waves Heading Our Way

For me, this creates two huge, simultaneous waves that are heading our way.

Wave 1: Incredible Opportunity

Suddenly, every smart developer, every small startup, and every university research lab has a Ferrari engine to work with. Think about the possibilities:

• A small e-commerce shop can now fine-tune a world-class model on its own sales data to create a hyper-personalized shopping assistant
• A medical research team on a tight budget can use it to analyze complex genomic data
• It allows for an explosion of creativity from the people closest to the real problems—the ones who could never afford to build the engine from scratch

Wave 2: Considerable Risk

But the second wave is one of considerable risk, and frankly, it's the one that has my focused attention. If the creative kid in the garage gets the engine, so does the person who wants to weaponize it.

Handing over this much power without the full safety manual or control over its use is a massive gamble. We could see:

• A surge in sophisticated misinformation that is nearly impossible to detect
• Bad actors probing these models for weaknesses to help them write malicious code
• It puts the responsibility of safety, ethics, and security squarely on the shoulders of those of us who choose to use it

What Should We Be Doing?

As I see it, this isn't a "wait and see" moment. The questions I'm asking my team and my clients have changed. They now include:

1. What's Our 'Small-Batch' AI Opportunity?

Where can we use one of these powerful, pre-built models to solve a highly specific problem through fine-tuning, without needing a massive AI budget?

2. How Do We Prepare Our Security for This?

If we bring a model in-house to run on our own systems, is our infrastructure ready to handle it securely and protect our proprietary data?

3. What Is Our Ethical Framework?

Do we have a clear, documented policy on how we will use powerful AI tools, ensuring we don't cross lines with customer data or contribute to misinformation?

Navigating the Gray Area

We're in this new, exciting, and admittedly unnerving phase of AI. It's not a simple "good vs. bad" story. It's about navigating a complex gray area.

As leaders, our job is to be smart enough to seize the opportunity, but wise enough to build the guardrails.

I'm truly excited to see the amazing things people will build, but I believe the conversation must be just as much about responsible implementation as it is about raw innovation.

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Patrick Phillips

AI/ML Strategy through Lean | Transformation Leader | Author of The Augmented Enterprise | Agile & Lean Practitioner