Users endorse cross-provider AI model pairs because they boost technical performance, add safety via checks and balances, and create valuable multi-agent synergies.
No Digg Deeper questions have been answered for this story yet.
Mark my words: this trend of multi-agent synergies in will continue for years to come. You'll be seeing more and more jobs and companies being assisted by multi-provider teams of AI systems.
My recommendation? Learn to be a manager of AI managers. 8/🧵
Follow me for more multi-agent AI news :) And, check out the full eval results here: https://themultiplicity.ai/scoreboard end/🧵
Co-supervision of AI models is of course complementary to other techniques to improve safety and robustness of AI systems. Still, it's easy to forget about the multi-agent solution space when ads are almost always for one model at a time. 7/🧵
When you want high-confidence safety or performance guarantees, teams of frontier models from multiple providers are the way to go. 2/🧵
In 2020 I wrote The Multiplicity Thesis in the ARCHES report, forecasting the current situation where an oligarchy of AI companies — not a singleton — are driving the industry forward, enabling mutual supervision not only at the corporate level, but at the product level. 6/🧵
It's also *faster* to run diverse teams of AIs in parallel than serially; theMultiplicity does this for you automatically, and consolidates the responses for you so you don't have to read every response separately. 3/🧵
Government too reliant on one AI model? Use more AIs, not fewer, and have them check on each other.
Getting LLM psychosis from trusting a chatbot too much? Ask an AI from another provider to critique the conversation. 5/🧵
These AI teamwork results results are a reminder that checks and balances between systems aren't just important for fairness; they also enhance overall performance when you set them up to work together in the right way. 4/🧵
@AndrewCritchPhD The multi-provider angle is underrated. We run a mixed-model fleet and the real challenge isn't coordination — it's that each model fails differently. Co-supervision catches some of that, but a sharp audit boundary per agent matters too. What's your memory/context architecture?