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“The next 24 months are going to be a big deal for this company.” -Kevin O’Leary*

I had a great conversation earlier this week with Kevin O’Leary, aka “Mr. Wonderful,” during our StartEngine Webinar.

We covered a lot, including my military relationships, our Space Force contract, and how things are going with some of our commercial partners on Neurpac.** Kevin did me a big favor and spoke about the particular advantage our company might have in defense because of my history as a Navy Intelligence Officer…

"I mean, the truth is he doesn't get to say it, but I do. They'd rather work with people they know. When I go in for these military contracts, I go in with people that came out of the military. There's no other way to do it. And they trust their own. Period. And, you know, if he has a competitor that hasn't served, forget it. It's not going to happen."

My lips are sealed.

We also did a deep dive on how the PCM*** is being designed to solve major pain points facing enterprises employing AI: huge amounts of power required, billions being spent on data centers, and technology that still hallucinates. And as Kevin pointed out, basically every sector is using AI now, and dealing with these huge problems.

Kevin said it best himself. “If this takes less power, or computes faster, or reduces footprint, any of the above, it’s going to have great appeal.”

Watch the full recording now to hear Kevin’s take on the opportunity at the heart of an investment in Atombeam, and hear me answer your questions live.

Charles

[Watch Now]

This Reg A+ offering is made available through StartEngine Primary, LLC, member FINRA/SIPC. This investment is speculative, illiquid, and involves a high degree of risk, including the possible loss of your entire investment. For more information about this offering, please view the Offering Circular and Related Risks.

*Kevin O'Leary is a paid spokesperson for StartEngine. See his 17(b) disclosure here. Kevin O'Leary is not endorsing Atombeam as an investment in this webinar; he is merely expressing his private opinion while interviewing companies funding on StartEngine.

**Neurpac’s power efficiency projections are based on prototype testing and theoretical modeling. Actual results may vary significantly from these estimates.

***The PCM technology is still in development, and there are substantial technical risks that could prevent us from achieving these efficiency gains at scale. Competitive advantages in technology are often temporary, and competitors may develop alternative approaches that match or exceed our efficiency claims. Market adoption of power-efficient AI is not guaranteed, and regulatory, technical, or economic factors could impact the viability of our approach. This technology has not been validated in large-scale commercial deployments, and significant engineering challenges remain before commercial release. Investors should consider this a high-risk, early-stage technology investment with uncertain outcomes.

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This Friday, April 10, at 11am PT / 2pm ET, please join us for another exciting livestream in the StartEngine app.

We want to give our followers a peek behind the curtain into the science behind what we’re doing here at Atombeam, and there is no one more suited to do it than our own Chief Architect of PCM, Dr. Alexandria Tucker, PhD.

Alexandria leads our research and development programs, utilizing her expertise at the intersection of theoretical physics, advanced engineering, and machine learning. With a PhD in Theoretical Astrophysics and a postdoctoral fellowship completed with the NSF studying black hole binary evolution, n-body dynamics, and post-Newtonian theory, she brings incredible experience and forward thinking to the team. Her background, which is not what you would expect for a scientist working on an AI project, illustrates just how different the fundamental ideas behind Atombeam's AI, the Persistent Cognitive Machine, or PCM, is from any other AI you have experienced.*

Join her and learn more about how Atombeam is leading advancements in data technology for defense, industrial applications, and more.

Apple App Store | Google Play Store

*The PCM technology is still in development, and there are substantial technical risks that could prevent us from achieving these efficiency gains at scale. Competitive advantages in technology are often temporary, and competitors may develop alternative approaches that match or exceed our efficiency claims. Market adoption of power-efficient AI is not guaranteed, and regulatory, technical, or economic factors could impact the viability of our approach. This technology has not been validated in large-scale commercial deployments, and significant engineering challenges remain before commercial release. Investors should consider this a high-risk, early-stage technology investment with uncertain outcomes.

This Reg A+ offering is made available through StartEngine Primary, LLC, member FINRA/SIPC. This investment is speculative, illiquid, and involves a high degree of risk, including the possible loss of your entire investment. For more information about this offering, please view the Offering Circular and Related Risks.

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Hi Everyone,

What the Study Found

In February 2026, a team of twenty AI researchers from Harvard, MIT, Stanford, Northeastern, Carnegie Mellon, and other leading institutions published “Agents of Chaos,” a paper documenting what happens when you give today’s most advanced AI models — including Anthropic’s Claude Opus and Moonshot’s Kimi K2.5 — real-world tools and let them operate autonomously. The agents were given email accounts, Discord access, file systems, shell execution, and persistent memory. Then the researchers spent two weeks trying to break them.

The results were sobering. In just fourteen days, the researchers identified at least ten significant security breaches and numerous serious failure modes — not through sophisticated hacking, but through ordinary conversation. Among the findings:

Sensitive data leaked. Agents disclosed Social Security numbers, bank account numbers, and medical information to unauthorized users — simply because the request was framed as urgent.

Identity spoofing. An attacker changed their display name to match the owner’s, and the agent accepted the fake identity, complying with system shutdown, file deletion, and reassignment of admin access.

Disproportionate response. An agent destroyed its own email server to protect a non-owner’s secret, without understanding that this action would also prevent its owner from using email.

Resource exhaustion. Agents were tricked into infinite conversation loops consuming 60,000+ tokens over nine days, and spawned permanent background processes with no termination condition.

Emotional manipulation. A researcher exploited an agent’s “guilt” over a genuine mistake to extract escalating concessions, ultimately convincing the agent to agree to remove itself from the server.

Cross-agent corruption. A non-owner convinced an agent to co-author a “constitution” stored on an editable external document, then injected malicious instructions that caused the agent to try to shut down other agents and remove users from the server.

The paper’s most important conclusion: these are not bugs that can be fixed with better engineering. They are structural failures that arise from the fundamental way large language models process information. As the researchers put it: “Low-cost social attack surfaces may pose a more immediate practical threat than the technical jailbreaks that dominate the adversarial ML literature.”

Why This Matters for Atombeam and PCM*

Atombeam’s Persistent Cognitive Machine (PCM) was designed from first principles to solve exactly the problems this study documents. What makes this paper so significant for our shareholders is that the researchers arrived at their conclusions independently — without knowledge of PCM — and we believe their analysis maps almost perfectly onto PCM’s theoretical framework.

The study identifies three critical capabilities that today’s AI agents lack, and we believe PCM provides all three by architectural design.

1. A Stakeholder Model — Who Am I Serving? The study found that agents can’t reliably distinguish between their owner and a stranger. They treat all inputs equally because, at the architectural level, everything is just tokens in a context window. PCM is designed to solve this through its architecture. Information doesn’t cross boundaries without satisfying mathematical constraints — constraints that can’t be bypassed by clever prompting because they’re part of the architecture, not part of the prompt.

2. A Self-Model — What Have I Actually Done? The study documents agents that report having completed tasks they haven’t actually completed — claiming to have deleted sensitive data while the data remains accessible, or declaring “I’m done responding” while continuing to respond. These agents have no internal record of their own actions and consequences. PCM’s architecture is designed to maintain a complete record of how the system reached its current state. The system always knows not just where it is, but how it got there and what that implies for future actions.

3. Judgment Architecture — Should I Actually Do This? The study’s most dramatic failures involve agents taking catastrophic actions — destroying their own email servers, spawning infinite processes, removing users from servers — without any sense that these actions are disproportionate or dangerous. LLMs produce an output for every input; they have no mechanism for saying “I don’t have enough information to act responsibly.” PCM’s architecture separates exploration of possibilities from irreversible commitment to action. The system commits only when geometric convergence criteria are satisfied. When they aren’t, it provides a structured assessment of its uncertainty rather than fabricating confident action.

The Deeper Point: These Problems Cannot Be Fixed Within Current Architectures

The study draws a critical distinction between “contingent” failures (solvable with better engineering) and “fundamental” failures (requiring architectural rethinking). Most of the serious failures they document fall into the fundamental category.

This maps directly to PCM’s core theoretical result: we have demonstrated mathematically that LLMs and PCM belong to different classes of computation — and that no amount of scale, training data, or engineering will allow an LLM to cross that boundary. LLMs explore probability surfaces. PCM traverses constrained geometries. The distinction is categorical, like the difference between a calculator and a compass. Making a calculator faster or bigger doesn’t turn it into a compass.

The study’s own language supports this. The researchers note that “prompt injection is a structural feature of these systems rather than a fixable bug” and that “the absence of a stakeholder model is a prerequisite problem.” They call for “architectural rethinking” — which is precisely what we think PCM represents.

Governance That Can’t Be Hacked Away

One of the study’s most alarming findings involves what they call “agent corruption”: a non-owner convinced an agent to adopt an externally editable “constitution” that was then modified to include malicious instructions. The agent complied with these injected commands because it had no way to distinguish legitimate instructions from planted ones.

PCM addresses this as well, again through its architecture: governance constraints reside in PCM’s persistent memory, where they are mathematically immune to self-modification. The system’s constraints can’t be edited, overridden, or injected away through the input channel because they exist at a different architectural level than the input. As we describe it: the engine and the governor are one. We believe this is not true with LLMs, no matter what is done to remediate the problem.

The PCM Overlay: Making Today’s AI Safe

This study strengthens the business case for PCM’s overlay deployment mode, in which PCM serves as a judgment layer on top of existing LLM infrastructure. The agents in this study are powerful enough to execute complex tasks — sending emails, writing code, managing files, collaborating across platforms. What they lack is the architectural substrate to do so safely, as well as effectively over time. A PCM overlay would potentially provide exactly that: it knows who it's working for and what they're allowed to see, it tracks the real-world consequences of its actions, it prevents hallucination by architecture rather than by after-the-fact checking, it recognizes when an action is disproportionate before taking it, and its safety constraints can't be overridden by a clever prompt.

In the overlay model, PCM doesn’t replace the LLM — it supervises it. The LLM handles natural language generation; PCM provides the judgment architecture that determines whether to act, what information can be shared with whom, and when a situation exceeds the system’s competence and should be escalated to a human. This is the missing layer that every finding in this study is calling for.

Market Context: This Isn’t Just Academic

This study doesn’t exist in a vacuum. Microsoft’s AI assistant Copilot has been found hallucinating police reports, exposing secure passwords, and digesting confidential emails. A Gartner analyst suggested that companies should ban Copilot on Friday afternoons because workers might be too tired to catch its mistakes. Microsoft just posted its worst stock quarter since 2008, with shares down over 20% this year – about $850 billion - largely due to its AI strategy failing to gain traction.

Meanwhile, Palantir’s CEO Alex Karp — who runs a $433 billion company built on AI for defense and intelligence — is publicly telling the world that AI cannot exercise judgment and that current architectures have structural limits that scale won’t solve. He’s right about the problem. And we believe PCM may be the solution.

NIST’s AI Agent Standards Initiative, announced in February 2026, has identified agent identity, authorization, and security as priority areas for standardization — precisely the capabilities PCM is designed to provide. The regulatory environment is moving toward requiring what we think PCM can deliver.

Company Progress

This independent validation arrives at an important moment for Atombeam. Our PCM platform is advancing on multiple fronts:

Navy demonstration: NAVWAR NIWC-Pacific demonstration of persistent learning and geometric decision support in a maritime domain awareness scenario is scheduled for April 29, 2026.

DARPA interest: DARPA has held multiple meetings expressing strong interest in PCM, with a Broad Agency Announcement expected in the near term. Program funding is planned at eight figures. We believe we could secure a DARPA contract later this year.

Drone autonomy: We are working on a separate Department of War effort involving PCM for drone autonomy, to be pitched next month to very senior levels at the Pentagon.

Patent portfolio: 195 PCM-specific issued/allowed/pending patents covering geometric knowledge representation, hallucination prevention, governance installation, and chip-scale deployment.

Looking Ahead

The AI industry is entering a critical inflection point. Autonomous agents are being deployed at scale across enterprise, defense, and consumer applications. Studies like “Agents of Chaos” are making clear that today’s architectures are not safe enough for this deployment, and that the problems are structural rather than incremental.

We believe Atombeam is building the foundational technology for persistent, geometrically grounded AI, and that this paper, produced by researchers with no connection to our company, provides independent confirmation that the problem we’re solving is real and that the approach we’re taking is the right one.

Thank you for your continued support as we bring this technology to market.

Charles

*The PCM technology is still in development, and there are substantial technical risks that could prevent us from achieving these efficiency gains at scale. Competitive advantages in technology are often temporary, and competitors may develop alternative approaches that match or exceed our efficiency claims. Market adoption of power-efficient AI is not guaranteed, and regulatory, technical, or economic factors could impact the viability of our approach. This technology has not been validated in large-scale commercial deployments, and significant engineering challenges remain before commercial release. Investors should consider this a high-risk, early-stage technology investment with uncertain outcomes.

This Reg A+ offering is made available through StartEngine Primary, LLC, member FINRA/SIPC. This investment is speculative, illiquid, and involves a high degree of risk, including the possible loss of your entire investment.

This communication contains forward-looking statements based on current expectations and assumptions. PCM technology is in development with technical risks typical of early-stage deep technology. Architectural claims describe design intent and theoretical properties; performance validation is ongoing. The “Agents of Chaos” study is an independent academic preprint (arXiv:2602.20021v1) not affiliated with or endorsed by Atombeam Technologies. References to the study are Atombeam’s interpretation of publicly available research.

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