he hard-won lessons learned in developing its offering -- and the fact that many of these lessons sound a lot like what we find in classic management texts:

LESSON 1: DELEGATION MATTERS.

When Anthropic analyzed what factors lead to excellent performance by Claude Research, it turned out that the best agentic systems weren't necessarily built on the best or most expensive AI models. Rather, like a good human manager, they need to excel at breaking down and distributing tasks to their digital workers.

Unlike human teams, agentic systems can enlist as many AI workers as needed, onboard them instantly and immediately set them to work. Organizations that can exploit this scalability property of AI will gain a key advantage, but the hard part is assigning each of them to contribute meaningful, complementary work to the overall project.

In classical management, this is called delegation. Any good manager knows that, even if they have the most experience and the strongest skills of anyone on their team, they can't do it all alone. Delegation is necessary to harness the collective capacity of their team. It turns out this is crucial to AI, too.

The authors explain this result in terms of 'parallelization': Being able to separate the work into small chunks allows many AI agents to contribute work simultaneously, each focusing on one piece of the problem. The research report attributes 80 per cent of the performance differences between agentic AI systems to the total amount of computing resources they leverage.

Whether or not each individual agent is the smartest in the digital toolbox, the collective has more capacity for reasoning when there are many AI 'hands' working together. In addition to the quality of the output, teams working in parallel get work done faster. Anthropic says that reconfiguring its AI agents to work in parallel improved research speed by 90 per cent.

Anthropic's report on how to orchestrate agentic systems effectively reads like a classical delegation training manual: Provide a clear objective, specify the output you expect and provide guidance on what tools to use, and set boundaries. When the objective and output format is not clear, workers may come back with irrelevant or irreconcilable information.

LESSON 2: ITERATION MATTERS.

Edison famously tested thousands of light bulb designs and filament materials before arriving at a workable solution. Likewise, successful agentic AI systems work far better when they are allowed to learn from their early attempts and then try again. Claude Research spawns a multitude of AI agents, each doubling and tripling back on their own work as they go through a trial-and-error process to land on the right results.

This is exactly how management researchers have recommended organizations staff novel projects where large teams are tasked with exploring unfamiliar terrain: Teams should split up and conduct trial-and-error learning, in parallel, like a pharmaceutical company progressing multiple molecules towards a potential clinical trial. Even when one candidate seems to have the strongest chances at the outset, there is no telling in advance which one will improve the most as it is iterated upon.

The advantage of using AI for this iterative process is speed: AI agents can complete and retry their tasks in milliseconds. A recent report from Microsoft Research illustrates this. Its agentic AI system launched up to five AI worker teams in a race to finish a task first, each plotting and pursuing its own iterative path to the destination. They found that a five-team system typically returned results about twice as fast as a single AI worker team with no loss in effectiveness, although at the cost of about twice as much total computing spend.

Going further, Claude Research's system design endowed its top-level AI agent -- the 'Lead Researcher' -- with the decision authority to delegate more research iterations if it was not satisfied with the results returned by its sub-agents. They managed the choice of whether or not they should continue their iterative search loop, to a limit. To the extent that agentic AI mirrors the world of human management, this might be one of the most important topics to watch going forward. Deciding when to stop and what is 'good enough' has always been one of the hardest problems organizations face.

LESSON 3: EFFECTIVE INFORMATION SHARING MATTERS.

If you work in a manufacturing department, you wouldn't rely on your division chief to explain the specs you need to meet for a new product. You would go straight to the source: the domain experts in R&D. Successful organizations need to be able to share complex information efficiently both vertically and horizontally.

To solve the horizontal sharing problem for Claude Research, Anthropic innovated a novel mechanism for AI agents to share their outputs directly with each other by writing directly to a common file system, like a corporate intranet. In addition to saving on the cost of the central coordinator having to consume every sub-agent's output, this approach helps resolve the information bottleneck. It enables AI agents that have become specialized in their tasks to own how their content is presented to the larger digital team. This is a smart way to leverage the superhuman scope of AI workers, enabling each of many AI agents to act as distinct subject matter experts.

In effect, Anthropic's AI Lead Researchers must be generalist managers. Their job is to see the big picture and translate that into the guidance that sub-agents need to do their work. They don't need to be experts on every task the sub-agents are performing. The parallel goes further: AIs working together also need to know the limits of information sharing, like what kinds of tasks don't make sense to distribute horizontally.

Management scholars suggest that human organizations focus on automating the smallest tasks; the ones that are most repeatable and that can be executed the most independently. Tasks that require more interaction between people tend to go slower, since the communication not only adds overhead, but is something that many struggle to do effectively.

Anthropic found much the same was true of its AI agents: "Domains that require all agents to share the same context or involve many dependencies between agents are not a good fit for multi-agent systems today." This is why the company focused its premier agentic AI feature on research, a process that can leverage a large number of sub-agents each performing repetitive, isolated searches before compiling and synthesizing the results.

All of these lessons lead to the conclusion that knowing your team and paying keen attention to how to get the best out of them will continue to be the most important skill of successful managers of both humans and AIs. With humans, we call this leadership skill empathy. That concept doesn't apply to AIs, but the techniques of empathic managers do.

Anthropic got the most out of its AI agents by performing a thoughtful, systematic analysis of their performance and what supports they benefited from, and then used that insight to optimize ho
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whoever is on the receiving end of this AI-fueled deluge can't deal with the increased volume. What can help is developing assistive AI tools that benefit institutions and society, while also limiting fraud. And that may mean embracing the use of AI assistance in these adversarial systems, even though the defensive AI will never achieve supremacy.
Balancing harms with benefits

The science fiction community has been wrestling with AI since 2023. Clarkesworld eventually reopened submissions, claiming that it has an adequate way of separating human- and AI-written stories. No one knows how long, or how well, that will continue to work.

The arms race continues. There is no simple way to tell whether the potential benefits of AI will outweigh the harms, now or in the future. But as a society, we can influence the balance of harms it wreaks and opportunities it presents as we muddle our way through the changing technological landscape.

This essay was written with Nathan E. Sanders, and originally appeared in The Conversation.

EDITED TO ADD: This essay has been translated into Spanish.

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Prompt Injection Via Road Signs

[2026.02.11] Interesting research: "CHAI: Command Hijacking Against Embodied AI."

Abstract: Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, also create new security risks. In this paper, we introduce CHAI (Command Hijacking against embodied AI), a new class of prompt-based attacks that exploit the multimodal language interpretation abilities of Large Visual-Language Models (LVLMs). CHAI embeds deceptive natural language instructions, such as misleading signs, in visual input, systematically searches the token space, builds a dictionary of prompts, and guides an attacker model to generate Visual Attack Prompts. We evaluate CHAI on four LVLM agents; drone emergency landing, autonomous driving, and aerial object tracking, and on a real robotic vehicle. Our experiments show that CHAI consistently outperforms state-of-the-art attacks. By exploiting the semantic and multimodal reasoning strengths of next-generation embodied AI systems, CHAI underscores the urgent need for defenses that extend beyond traditional adversarial robustness.

News article.

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Rewiring Democracy Ebook is on Sale

[2026.02.11] I just noticed that the ebook version of Rewiring Democracy is on sale for $5 on Amazon, Apple Books, Barnes & Noble, Books A Million, Google Play, Kobo, and presumably everywhere else in the US. I have no idea how long this will last.

Also, Amazon has a coupon that brings the hardcover price down to $20. You'll see the discount at checkout.

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3D Printer Surveillance

[2026.02.12] New York is contemplating a bill that adds surveillance to 3D printers:

New York's 20262027 executive budget bill (S.9005 / A.10005) includes language that should alarm every maker, educator, and small manufacturer in the state. Buried in Part C is a provision requiring all 3D printers sold or delivered in New York to include "blocking technology." This is defined as software or firmware that scans every print file through a "firearms blueprint detection algorithm" and refuses to print anything it flags as a potential firearm or firearm component.

I get the policy goals here, but the solution just won't work. It's the same problem as DRM: trying to prevent general-purpose computers from doing specific things. Cory Doctorow wrote about it in 2018 and -- more generally -- spoke about it in 2011.

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Upcoming Speaking Engagements

[2026.02.14] This is a current list of where and when I am scheduled to speak:

I'm speaking at Ontario Tech University in Oshawa, Ontario, Canada, at 2 PM ET on Thursday, February 26, 2026.
I'm speaking at the Personal AI Summit in Los Angeles, California, USA, on Thursday, March 5, 2026.
I'm speaking at Tech Live: Cybersecurity in New York City, USA, on Wednesday, March 11, 2026.
I'm giving the Ross Anderson Lecture at the University of Cambridge's Churchill College at 5:30 PM GMT on Thursday, March 19, 2026.
I'm speaking at RSAC 2026 in San Francisco, California, USA, on Wednesday, March 25, 2026.

The list is maintained on this page.

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Since 1998, CRYPTO-GRAM has been a free monthly newsletter providing summaries, analyses, insights, and commentaries on security technology. To subscribe, or to read back issues, see Crypto-Gram's web page.

You can also read these articles on my blog, Schneier on Security.

Please feel free to forward CRYPTO-GRAM, in whole or in part, to colleagues and friends who will find it valuable. Permission is also granted to reprint CRYPTO-GRAM, as long as it is reprinted in its entirety.

Bruce Schneier is an internationally renowned security technologist, called a security guru by the Economist. He is the author of over one dozen books -- including his latest, Rewiring Democracy -- as well as hundreds of articles, essays, and academic papers. His newsletter and blog are read by over 250,000 people. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation, AccessNow, and the Tor Project; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.

Copyright (C) 2026 by Bruce Schneier.

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