The Floor Beneath the Floor

Transcript

This is The Judgment Layer, from Advisers Give Back — a nonprofit working to increase access to pro bono financial planning, pairing households who can't afford a financial planner with CFP professionals who volunteer their time. This show is about what's happening in AI — and what it changes if you're the one running the firm. Written and voiced with AI; directed by Matt Iverson-Comelo, executive director of Advisers Give Back. Nothing here is investment, legal, or compliance advice.

Here's this week's episode.

Picture a researcher at a cryptography conference — probably in a hotel ballroom with bad coffee — standing up during Q&A and saying something that nobody in the room wants to hear. The kind of thing where the audience laughs a little nervously, writes it down, and then tries to forget it. Matthew Green, a cryptography professor at Johns Hopkins University who studies the security of real-world systems, did something like that recently. Except he did it on social media, which means nobody got to forget it quietly. He said: there is a fifteen percent chance we functionally lose confidence in our existing public-key encryption algorithms. And then he added the part that makes it genuinely unsettling — not the probability, but the timing problem underneath it.

To understand why this matters, you need to know what public-key encryption actually does. When your firm sends client data over the internet, when your custodian authenticates your identity, when your document management system verifies that a signed agreement is genuine — almost all of that rests on mathematical problems that classical computers cannot solve fast enough to break in any reasonable timeframe. The security of the entire digital infrastructure that wealth management runs on is, at bottom, a bet that certain math stays hard. Public-key encryption is not a product you buy. It is the invisible layer underneath every product you use.

Green was quoted by Simon Willison, a developer and writer who runs a widely-read technology blog and tracks AI's implications for software and security. Willison flagged something specific in Green's framing that deserves attention. Green was not talking about a slow, gradual erosion of confidence in encryption. He was talking about a surprise — an unexpected AI-enabled mathematical breakthrough that cracks one of the hard problems public-key encryption relies on. And the terrifying thing, Green said, is not the crack itself. It is the speed mismatch. The speed at which AI can now produce surprises — genuine, discontinuous, paradigm-breaking surprises in mathematics and science — and the speed at which human institutions can replace cryptographic standards are, in his words, orders of magnitude different. You only survive a surprise like that if you have done the preparation in advance.

Here is what makes Green's framing non-obvious, and why it matters in a room full of people who run advisory firms rather than write cryptographic algorithms. The standard response to AI and security risk in the wealth management world is to focus on the social engineering layer — phishing emails that are better-written because AI wrote them, deepfake voice calls impersonating clients, synthetic identity fraud. Those are real and worth taking seriously. But they are all attacks on the human layer of your security stack. What Green is pointing at is something categorically different. It is a potential attack on the mathematical layer — the layer below the humans, the layer that your cybersecurity team does not control and cannot patch.

And here is the twist that even security-aware people tend to miss. A cryptographic break is not like a software vulnerability where someone discovers a bug and you download an update. A cryptographic break means that every piece of data encrypted with the old method — past, present, and future — is retroactively readable by anyone who has been collecting encrypted traffic and waiting. The intelligence community has a phrase for this: harvest now, decrypt later. Sophisticated adversaries collect encrypted data today on the assumption that the math protecting it will eventually fail. If Green's fifteen percent scenario arrives, the question is not what happens to your firm's security going forward. The question is what happened to every client communication, every signed document, every transmitted record over the past decade.

Now, the instinct is to say: fifteen percent is not that high. That instinct is wrong for a specific reason. In risk management, you should weight tail events not just by their probability but by the combination of probability, consequence, and your ability to respond in time. Green's point is that the response time problem is the whole issue. If the break comes as a surprise — and by definition, surprises are not announced — and if the transition to new post-quantum cryptographic standards takes years even with maximum institutional effort, then the fifteen percent number understates the actual risk exposure. It does not account for the gap between the moment of discovery and the moment your systems are protected.

The reason AI enters this story is that the same capabilities that make AI powerful for research — the ability to explore enormous combinatorial spaces, to find non-obvious connections across disciplines, to test millions of approaches faster than any human team — are precisely the capabilities that make AI potentially dangerous for mathematics problems that human researchers have spent decades trying to crack. Green is not describing a theoretical future. He is describing a realistic near-term scenario in which AI-accelerated mathematical research crosses a threshold that changes the security guarantees everything else depends on.

Now turn the lens. What does this mean for the firm you run in the next twelve to eighteen months?

The first thing it means is that your cybersecurity posture has a blind spot that almost no external assessment will flag. Most of the security audits, penetration tests, and compliance reviews that RIAs go through are designed to find weaknesses in the human and software layers — misconfigured access controls, unpatched systems, weak passwords, social engineering vulnerabilities. They are not designed to assess your firm's readiness for a cryptographic transition, because until very recently that was a problem for government agencies and defense contractors, not financial services firms. That has changed. The National Institute of Standards and Technology — the federal body that sets cryptographic standards in the United States — finalized its first post-quantum cryptographic standards in the summer of twenty twenty-four. The transition has officially begun. The question is whether your technology vendors and custodial partners are on a real timeline to implement them, or whether they are watching and waiting.

The second thing it means is about vendor dependency in a way that is easy to overlook. You probably do not control your cryptographic stack. Your custodian does. Your document management vendor does. Your portfolio management system does. Which means that your exposure to a cryptographic surprise is not really yours to fix — it belongs to the vendors you depend on. That is a due diligence question that almost no RIA is currently asking in a vendor review, and it should be. Not "do you have SOC 2 certification" — every vendor has that — but "where are you in the transition to post-quantum cryptographic standards, and what is your timeline for full implementation?"

The third implication is the one that runs deepest. Advisers make an implicit promise to clients that the information clients share is and will remain private. That promise has always rested partly on your firm's practices and partly on the mathematical infrastructure of the internet. For thirty years, the mathematical infrastructure part has been so reliable that it was invisible. Green's point is that the reliability of that infrastructure is now a variable, not a constant. Twelve to eighteen months from now, firms that have started treating cryptographic readiness as a fiduciary question — not a technology question, a fiduciary question — will be ahead of the firms that still think of security as a compliance checkbox.

The one-sentence version: the AI risk your security team is not watching is the one that runs underneath every tool your security team uses.

The first idea was about infrastructure — the invisible mathematical layer beneath everything. The second idea is about architecture — the structural question of how AI systems scale, and what the answer means for where the real leverage in your firm lives.

Start with an OpenAI researcher's comment, quoted this week in Understanding AI, the technology newsletter written by Timothy B. Lee, a journalist and computer scientist who covers AI with particular rigor. The researcher described a particular moment working with a new kind of AI system as "the most feel-the-AGI moment I had since reasoning models." That phrase — feel the AGI — is the kind of thing researchers use quietly among themselves and almost never say in public. It signals that something genuinely crossed a threshold. The thing that triggered it was not a new model. It was a new architecture for deploying models. Agent swarms.

To understand what a swarm actually is, and why it is different, you need to hold the current state of AI clearly in your head for a moment. For most of the past few years, the dominant story in AI scaling has been: make the model bigger, train it on more data, and it gets smarter. This is sometimes called the bitter lesson — a term from Richard Sutton, a computer scientist who argued that the history of AI shows that general methods that leverage computation consistently outperform methods that try to encode human knowledge. Ethan Mollick, the Wharton professor who studies how people actually use AI and writes the newsletter One Useful Thing, recently connected this history to what is now happening with agent swarms. The insight is that the bitter lesson has a sequel, and the sequel is about parallelism.

Here is what a swarm is. Instead of one AI model working through a problem sequentially — read the question, reason through it, produce an answer — you deploy many AI agents simultaneously, each working on a piece of the problem, able to check each other's work, able to specialize, able to run in parallel and then synthesize. Timothy Lee's piece in Understanding AI describes a specific technical architecture called Jev — named after a developer who published the approach — that OpenAI, Cloudflare, and Amazon have all reportedly replicated or adopted. The core insight of Jev is that the coordination overhead of running many agents together can be reduced dramatically with the right routing and verification design. When you solve the coordination problem, you unlock something that feels qualitatively different. Not a smarter individual AI, but a smarter collective AI — one that can tackle problems of a complexity and length that a single model simply cannot hold.

What the OpenAI researcher was describing when they said "feel the AGI" was not a system that suddenly became conscious or self-aware. It was a system that, for the first time, was able to work through a problem the way a small, well-coordinated team of humans works through a problem — with specialization, verification, parallelism, and synthesis — at the speed of software. The swarm doesn't feel like a better autocomplete. It feels like a colleague.

Now here is the non-obvious part, the thing that the swarm discussion almost always misses. Most conversations about AI agent swarms focus on what the swarm can do — the tasks it can complete, the speed it achieves, the complexity it can handle. That is the wrong question. The right question is what the swarm changes about where human judgment gets applied in a workflow. And the answer is counterintuitive. Swarms do not remove the need for human judgment. They concentrate it.

Think about why. A single AI model doing a task produces one output. You review it. The judgment is applied at the end. A swarm doing the same task produces a process — multiple agents working, disagreeing, checking, synthesizing — and the output of that process is more reliable than any single agent's output, but only if the task was framed correctly at the beginning and the synthesis is evaluated correctly at the end. The leverage points shift from the middle of the workflow to the edges. Someone has to define what the swarm is trying to accomplish with enough precision that the agents can divide the work intelligently. And someone has to evaluate whether the synthesized output actually answers the right question. Those two moments — task architecture and output judgment — are where human expertise concentrates, and they require a different kind of expertise than what the middle of the workflow required.

In the advisory context, the middle of the workflow is a lot of what junior professionals currently do. Research aggregation, document review, scenario modeling, draft preparation — these are the reps that have historically been how early-career advisers develop their judgment. The swarm displaces those reps. It does them faster and, on many dimensions, more reliably than a second-year associate. This is not a future scenario. The technology to build swarm-based workflows for financial planning tasks exists today, and the firms moving fastest on AI are deploying versions of it now.

But here is what is genuinely unresolved, and what makes this a twelve-to-eighteen-month question rather than a settled one. Nobody has yet figured out how you develop the judgment that belongs at the edges — task architecture and output evaluation — in someone who never spent years working in the middle. The developmental path that the financial planning profession has always relied on is a path that runs through the middle. You did the research, you built the plans, you saw what clients reacted to and why — and over time, through those reps, you developed the intuition that lets you recognize when an output is technically correct but wrong for this client. The swarm is about to eliminate most of those reps, and nobody in the profession has a consensus answer for what replaces them.

This is where the speculative question gets genuinely interesting for the firm you run. Twelve to eighteen months from now, the firms that have thought hardest about this will look structurally different from the firms that have simply added AI tools to their existing workflow. They will have redesigned what early-career development means — probably fewer individual task assignments and more deliberate work at the edges: framing problems, evaluating outputs, sitting in client meetings where judgment is applied out loud and visible to the people learning. The apprenticeship model, which the profession largely abandoned in favor of structured task pipelines, may be the thing that comes back. Not because it is sentimental, but because it is the only reliable way to develop the judgment that lives at the edge of a swarm workflow.

And there is an organizational implication that runs even deeper. If the leverage in AI-assisted work concentrates at task architecture and output judgment, then the most valuable thing you can do for the human beings in your firm is not give them AI tools to work with. It is give them a clear view of what excellent judgment looks like in those two moments, from someone experienced enough to model it. The swarm makes the experienced adviser's judgment more valuable, not less — but only if that judgment is visible and teachable, not trapped inside one person's head.

The one-sentence version: swarms don't flatten your talent hierarchy — they invert it, making the people at the top of the judgment curve the bottleneck everything else runs through.

Here is the thread that runs between these two ideas. The first idea is about infrastructure you do not control but depend on completely. The second is about leverage that is moving inside the firm — concentrating at the edges of workflows, at the moments of framing and judgment. What connects them is a single fact about this moment. The assumptions underneath the work of running an advisory firm are changing faster than the firm's governance structures can track. The cryptographic infrastructure beneath your client data. The developmental pipeline that produces the advisers who serve those clients. Both are being rewritten by AI's rate of surprise. Green's point was that you only recover from a surprise if you do the preparation in advance. That is not a cryptography lesson. It is a leadership one.