The Economics of Open Source AI

Understand open source AI strategies: link security considerations, commoditizing complementary products, and ethical implications

Feb 11, 2026 8m 22s

Elizabeth Seger

Expert Insights

Elizabeth Seger, an expert in digital policy, examines the complexities of open-source AI, including its economic rationale and implications for businesses and consumers. Rather than acts of generosity, she characterizes the giving away of AI models as strategic moves by companies to build a user base and commoditize complementary products.

Hear Elizabeth unpack:

Why open source in AI isn’t a binary choice, but a spectrum spanning fully closed systems to models with varying levels of access to code, data, and documentation.
How some companies release powerful models for free to stimulate demand for complementary products—like specialized hardware or infrastructure that maximizes performance.
Why closed models can offer tighter control and guardrails, potentially limiting unintended societal consequences.
What “free” access often signals: a deliberate strategy to build ecosystems around proprietary tools and services.
And why users should be cautious—when a product is free, their data and attention may be the real asset, raising concerns around manipulation and targeted advertising.

Think about the advertising that's targeted at social media platforms; they have a lot of data about you. A McKinsey study, in partnership with Mozilla, examined why different businesses adopt open-source versus proprietary software and AI tools. At the very top of that list, both for adopting proprietary and open-source solutions, was safety and security. It's the same reason, and a lot of this is going to have to do with whether you have the internal expertise to properly use and maintain these tools.

Monterail Team Analysis

Here are some actionable takeaways drawn from Elizabeth Seger's insights that can provide effective strategies:

Understand the Spectrum: Recognize that open-source AI isn't binary, but rather a spectrum. From completely closed models to fully open, understand the implications of each point on the spectrum for your team and project.
Leverage Open Source Strategically: If considering open source AI, consider if it's part of a strategic move to boost demand for complementary products or services. Evaluate the effectiveness of this strategy in advancing your business goals.
Keep Security Front of Mind: Prioritize safety and security in the decision-making process when evaluating whether to adopt proprietary or open-source AI tools. Ensure you have the internal competencies to manage these aspects properly.
Build or Buy: Determine whether your team has the resources and skills to build and maintain an open-source system. If not, consider proprietary systems that manage these aspects for you.
Beware of Free Products: If a product or service is free, consider how your data is being used. Be aware of potential ethical issues related to data use, particularly in targeted advertising and emotional manipulation.
Continuous Learning: Stay updated with the evolving AI landscape to understand the implications of closed vs open models, their potential benefits, and the motives of companies offering free AI technologies.
Privacy Protection: Create safeguards to protect user data from unethical practices such as emotional manipulation. Seek to instill trust in users by being transparent with advertising technique,s if any.