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I asked an AI swarm to fill out a March Madness bracket — here’s what happened

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Imagine if a large team of 200 people could hold a thoughtful real-time conversation in which they efficiently brainstorm ideas, share knowledge, debate alternatives and quickly converge on AI-optimized solutions. Is this possible — and if […]

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Imagine if a large team of 200 people could hold a thoughtful real-time conversation in which they efficiently brainstorm ideas, share knowledge, debate alternatives and quickly converge on AI-optimized solutions. Is this possible — and if so, would it amplify their collective intelligence?

There is a new generative AI technology, conversational swarm intelligence (or simply hyperchat), that enables teams of potentially any size to engage in real-time conversations and quickly converge on AI-optimized solutions. To put this to the test, I asked the research team at Unanimous AI to bring together 50 random sports fans and task that large group with quickly creating a March Madness bracket through real-time conversational deliberation.

Before I tell you how the experiment is going, I need to explain why we can’t just bring 50 people into a Zoom meeting and have them quickly create a bracket together. Research shows that the ideal size for a productive real-time conversation is only 4 to 7 people. In small groups, each individual gets a good amount of airtime to express their views and has low wait time to respond to others. But as group size grows, airtime drops, wait-time rises — and by a dozen people it devolves into a series of monologues. Above 20 people, it’s chaos. 

So how can 50 people hold a conversation, or 250, or even 2,500? 

Hyperchat works by breaking any large group into a set of parallel subgroups. It then adds an AI agent into each subgroup called a “conversational surrogate” tasked with distilling the human insights within its local group and quickly sharing those insights as natural dialog with other groups. These surrogate agents enable all the subgroups to overlap, weaving local conversations into a single large conversation. And, it works, enabling groups of potentially any size to brainstorm, prioritize, debate and converge in real-time.

Hyperchat technology was invented not just to make communication and collaboration highly efficient at large scale, but to significantly amplify group intelligence. Progress has been rapid on this front, and already enterprise teams are using a commercial platform called Thinkscape® that enables hundreds of people to hold optimized deliberations in real-time.

But does hyperchat technology really make teams smarter (and can it predict March Madness outcomes)?

To test this in full public view, I asked the team at Unanimous AI to bring together 50 random sports fans in their Thinkscape platform and create a March Madness bracket. The resulting bracket was then  entered into the ESPN March Madness contest so we can track how well it does against 30 million other people. Remarkably, the bracket created by 50 random people is performing in the 99th percentile (top 1.4%) in the ESPN contest. Here are the stats:

Of course, anything can happen as the tournament continues this week, but so far, the collective intelligence created among this hyper-chatting group of fans is outperforming my expectations. This is not the first time this technology has surprised me.

In a 2024 study by researchers at Carnegie Mellon and Unanimous AI, groups of 35 people were asked to take standard IQ tests by hyperchat. Results showed that groups of random participants, who averaged an IQ of 100 (the 50th percentile) when working on their own, scored an effective IQ of 128 (the 97th percentile) when deliberating conversationally in the hyperchat platform. This is gifted-level performance.

In another 2024 study, groups of 75 people were asked to brainstorm together in real-time to solve a creative challenge. The groups did this multiple times, half using standard chat and half using hyperchat in Thinkscape. The groups then compared the experience and reported that when communicating via hyperchat, they felt more productive, more collaborative and surfaced better solutions (p

This technology has excited me for a long time, not just because it makes human groups smarter. It also has the ability to enable hybrid groups of human participants and AI agents to collaborate at unlimited scale, enabling optimized decisions that keep humans in the loop. Doing this requires the addition of a second type of AI agent to the hyperchat structure known as a “contributor agent.” These agents conversationally provide real-time factual content to support the ongoing human deliberation. The goal is to enable a hybrid collective superintelligence. 

This hybrid technique was first tested in a 2024 study that brought together groups of humans and AI agents to field fantasy baseball teams using a real-time hyperchat structure. The results showed that large collaborating groups found the hyperchat structure to be a highly productive means of deliberation, with 87% of participants expressing that it led to significantly better decisions.

Overall, conversational swarm intelligence is a powerful use of AI Agents that could radically transform collaboration by enabling real-time conversations among teams of potentially any size. Considering that the average Fortune 1000 company has more than 30,000 employees and has functional teams with hundreds of members, this could solve the longstanding bottleneck that has limited real-time deliberations to small teams. It is also an efficient way to leverage the power of AI in critical decisions while keeping humans in control.

The men’s March Madness tournament continues this week. Anything could happen, but I suspect the collective intelligence harnessed from those 50 random sports fans will do very well. We shall see…

Louis Rosenberg founded Immersion Corp and Unanimous AI.

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The top strategy so far is what one enterprise calls the “Cloud Team.” You assemble all your people with cloud skills, and your own best software architect, and have the team examine current and proposed cloud applications, looking for a high-level approach that meets business goals. In this process, the team tries to avoid implementation specifics, focusing instead on the notion that a hybrid application has an agile cloud side and a governance-and-sovereignty data center side, and what has to be done is push functionality into the right place. The Cloud Team supporters say that an experienced application architect can deal with the cloud in abstract, without detailed knowledge of cloud tools and costs. For example, the architect can assess the value of using an event-driven versus transactional model without fixating on how either could be done. The idea is to first come up with approaches. Then, developers could work with cloud providers to map each approach to an implementation, and assess the costs, benefits, and risks. Ok, I lied about this being the top strategy—sort of, at least. It’s the only strategy that’s making much sense. The enterprises all start their cloud-reassessment journey on a different tack, but they agree it doesn’t work. The knee-jerk approach to cloud costs is to attack the implementation, not the design. What cloud features did you pick? Could you find ones that cost less? Could you perhaps shed all the special features and just host containers or VMs with no web services at all? Enterprises who try this, meaning almost all of them, report that they save less than 15% on cloud costs, a rate of savings that means roughly a five-year payback on the costs of making the application changes…if they can make them at all. Enterprises used to build all of

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John Deere unveils more autonomous farm machines to address skill labor shortage

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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