Are You Overlooking the Risks of 'Open Source Al'?

Is the gap between perception and reality in 'Open Source Al' putting you at risk? The answer may surprise you!

- 

Open source used to be simple with clear rules, well-known licences and strong community values.

Everyone knew what ‘open source’ meant and the ecosystem thrived because of that clarity. Then came AI, bursting onto the scene with hype, marketing buzz and a heavy dose of confusion.

When I see the terms ‘open source’ and ‘AI’ together, what I often see is messy: misleading claims, unclear definitions, and risky assumptions. This is not just a theoretical problem; I have watched startups and global companies alike realise too late that ‘open source Al’ often is not what they thought it was. The gap between perception and reality is growing, and the risks, especially around licensing, compliance, and transparency, are significant.

Why am I talking about this?

I speak about this because I see too many people in the industry gloss over the details. Developers, tech leads, and product managers often scroll past licence agreements, sign terms without reading them, and assume that they are safely operating in an open source environment.

From my experience, ignoring these details can be costly. With AI, the landscape has become more complicated. Definitions have blurred, licensing is trickier, and marketing noise makes it hard to know what ‘open’ really means.

The marketing versus the reality

Today, ‘open source AI’ is one of the hottest marketing terms in technology. But I have noticed that the label is often slapped onto tools, datasets, and models that do not actually meet the established criteria for open source.

I have heard people say things like:

‘I used a model from Hugging Face, so it is open source AI.’

‘I cloned a repository from GitHub, so it is open source AI.’

‘I trained a dataset from Kaggle, so it is open source AI.’