The War Between Open Source, Open Weight, And Closed AI Models
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The War Between Open Source, Open Weight, And Closed AI Models
Timothy Prickett Morgan
Timothy Prickett<br>Morgan
Co-Editor, Co-Founder, The Next Platform
Published<br>thu 13 Aug 2026 // 17:41 UTC
Ask yourself these questions, assuming you have a serious medical condition and your doctors are going to be using GenAI to scan your images and test results to discover the breadth and depth of your condition and to recommend treatment.<br>First, do you want that AI based on a closed source, proprietary model from OpenAI or Anthropic, an open weight model from Meta Platforms, Moonshot AI, Alibaba, DeepSeek, OpenAI, Google, Mistral, or the countless models being offered up on Hugging Face? Or do you want a fully open source model like the Nemotron 3 models from Nvidia or the now ancient Llama 3 and 4 models from Meta Platforms? Do you want a model made by a US company only?
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Second, do you want that model to have the largest number of input parameters and output weights as possible or do you want a streamlined model with less data and fewer weights?<br>These are good questions, but you will not be making these choices. Medical software suppliers and healthcare companies will be doing that for you. But what are the right answers?<br>Having grown up in the proprietary software era of the 1980s and the Unix, Linux, and Windows Server revolutions of the 1990s, I see the value of both proprietary system and application software and the rise of open source. I also know the value of creating custom and inhouse application software, which dominated the first several decades of corporate computing and which is still done by hundreds of thousands of large corporations around the globe. Control your code, control your corporate fate.<br>But the bias I have is towards commercially supported or, in the case of the hyperscalers and cloud builders, self-supported open source software once that open source software rises to the same level of functionality as the closed source, proprietary alternatives.
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This is what has happened in operating systems and their related middleware stacks over the decades. Back when I was a cub reporter there were probably on the order of 25 different datacenter hardware platforms and maybe twice that number of operating systems, and over the years this has collapsed down to X86 and now Arm iron with a smattering of IBM z mainframe and Power systems machinery, and IBM’s platforms run the last supported Unix (AIX) and last supported proprietary platform (IBM i) in midrange and enterprise systems, and its mainframes run proprietary z/OS and sometimes older stuff in partitions as well as Linux. (Power Systems run Linux too, obviously, and not just Red Hat Enterprise Linux.)<br>And while Windows Server footprints are shrinking out there, I do not think usage is for the core back office and front office applications upon which that platform jumped from our desktops to our datacenters two and a half decades ago. This is more a statement about how many cores you can cram in a server chassis these days and the rise of data analytics and now AI, which runs exclusively on Linux. But, that said, even Microsoft is embracing Linux, having used Linux underneath its SONiC Linux distro for the past decade and having created the Azure Linux distro for its cloud this May.<br>Application software is a more varied story. Back in the early days of computing in the 1960s through the 1980s, most large enterprises controlled their own back office application code, starting with basic accounting and evolving into MRP systems then full-blown ERP systems.<br>But about the time data analytics on that commercial data started taking hold, the first wave of SaaS hot at the same time commercial ERP suites got good enough for companies with a huge amount of technical debt in their applications – and no easy way to deal with the Y2K bug as well as move to modern Internet technologies – said to hell with it and tossed out their code and modified third party applications from the likes of SAP, Oracle, and Microsoft and changed their businesses to meet that code as much as changed that code (where possible) to meet their business.
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They traded control for speed, and are betting the application software providers can add features and provide security that they would have trouble doing in house.<br>Those deploying net-new AI applications or augmenting their existing applications with AI are facing the same choices, and those running third party applications seem inclined to wait for their software partners to add the AI features. There are those, however, who want to own their AI stack and wrap around these existing applications rather than gut them – and spend more money for more functionality – much as they did with adding web technologies to their ERP, SCM, and CRM suites. Either way, you pay. You...