Why The Next Wave of AI Will Make or Break Your Net Worth (Even If You Never Write a Line of Code)
Wealth Matters Open Claw Briefing
I woke up today in a fantastic mood but noticed something different about myself as I got dressed so I took a photo…
Why Network Effects and Power Laws Matter
The same mathematical laws that made social networks and search engines “winner-takes-most” are now turning loose software agents into economic super-hubs, and the people who learn to work with them early will own a disproportionate share of that upside.
At the very same time, this is the first major technology wave where you do not need to be a software engineer to participate; the scarce skill now is learning how to think like an architect, builder, or product manager of humans and agents working together.
When something has network effects, every new participant makes it more valuable for everyone already using it. Metcalfe’s Law is the classic shorthand: the value of a network grows roughly with the square of its users—double the participants and the potential connections (and thus opportunities) more than quadruple.
Power laws describe what happens next. Instead of value being spread evenly over a normal distribution, a small number of hubs (companies, platforms, agents, or people) attract the vast majority of connections, deals, and attention. You see this everywhere: a few cities dominate economic output, a handful of social platforms dominate attention, and a tiny fraction of funds capture most AUM.
Early movers that ride the network effect and keep compounding become very hard to dislodge, even when better “products” show up later.
What Changes with Agentic AI
Agentic AI is simply software that doesn’t just answer questions; it takes actions—sending messages, scheduling, executing trades under constraints, filing documents, writing copy, coding—on your behalf. These agents live inside messaging apps like whatsapp, telegram, signal, etc., trading systems, CRMs, document platforms, and back-office tools, quietly doing work 24/7.
When thousands or millions of agents start talking to each other—sharing data, code, skills, and workflows—you don’t just get a new “app.” You get a new kind of network where:
Each added agent can call more tools, join more chats, and connect more services.
Each useful workflow an agent learns can be reused, remixed, and scaled by others.
Each integration—a custodian, a CRM, a payment rail, a social or communications layer—becomes a shared road that every other agent can drive on.
That is classic network effects, but now the nodes are autonomous actors doing economic work, not just people scrolling a feed. Instead of “who has the most users scrolling,” the question becomes “which clusters of humans plus agents coordinate the most valuable work?”
Why This Matters to Business Owners and Investors More Than Any Other Force
For high-net-worth owners and allocators, agent networks will change where margins live and where moats form.
Operational leverage: AI agents are already capable of cutting operational and back-office costs in wealth management and financial services while improving speed and consistency.
New hubs: Platforms that host and coordinate many agents can become critical infrastructure, similar to cloud providers or custodians today, and may enjoy power-law economics.
Risk concentration: The same concentration that creates outsized winners also creates concentrated fragility—mistakes, hacks, or manipulation at a few powerful agent hubs can ripple through portfolios and counterparties.
For investors, this means a small number of agent ecosystems and infrastructure providers are likely to pull away from the pack in value, usage, and data advantage.
Capital will flow toward those networks, not just stand-alone “AI apps,” in the same way it gravitated toward dominant internet and cloud platforms. Governance, security, and alignment of those hubs will have a first-order impact on financial stability and systemic risk.
Ignoring this shift is like ignoring the rise of exchanges and custodians in the early days of electronic trading—you still see prices, but you don’t understand where the real power moved.
Why Wealth Advisors, RIAs, and Estate Planners Can’t Sit This Out
In wealth management, agents will not just draft memos; they will:
Continuously rebalance portfolios, implement tax-loss harvesting, and simulate client scenarios.
Orchestrate onboarding, KYC, compliance checks, and reporting across multiple systems.
Monitor client behavior and markets to flag risks, opportunities, and anomalies.
Firms that embrace this responsibly—human in the loop, clear guardrails, documented policies—gain two compounding advantages:
Productivity: The same headcount can manage more complex client situations, more entities, more jurisdictions.
Relationship depth: Advisors can spend more time on strategy, family dynamics, and complex planning because the “agent mesh” handles the grind.
Because agents learn from flows and feedback, there is a power-law dynamic here too: the advisors and firms who adopt early and at scale will accumulate better workflows, playbooks, and models, making them increasingly hard to catch.
One of the projects I am excited to spearhead is CMDR (pronounced “Commander” and based off the CMD—R keystroke that resets your system) https://mycmdr.com/ria and I’d love to give you a demo.
At the same time, regulators and researchers are warning that poorly governed agents can amplify mis-selling, market manipulation, or operational errors, especially in finance. Knowing how to use agents responsibly becomes part of the real fiduciary duty.
You Don’t Need to Be a Coder
Here’s the hopeful part: you do not need to learn to write code to participate in this shift. The barrier is no longer syntax; it is clarity of thinking and willingness to experiment. You need to become more of an architect and a builder of solutions to real problems. If you can describe a process clearly, break an outcome into steps, and define what “good” looks like, you already have the raw materials to direct powerful AI agents.
The scarce skill now is learning how to think like:
An architect – designing how humans and agents should interact, which systems they can touch, and what success looks like.
A builder – assembling tools, agents, and data sources into workflows that actually get things done.
A product manager – deciding which problems to automate first, setting priorities, measuring outcomes, and iterating.
This is true whether you are a business owner, a high-net-worth investor, a young person in your 20s, or a wealth advisor. The job is less “become a programmer” and more “learn to express your business logic, judgment, ethics, and intent in a way that agents can act on safely.”
Building Your Own Human+Agent Management Framework
Every compounding network has a window where it is still cheap to buy in—cheap in dollars, time, and career risk. Agentic AI is in that window now: powerful enough to be real, early enough that norms and playbooks are still being written. But beyond escape velocity and inevitable, whether we agree or understand it yet or not. The key is not to bolt agents on randomly, but to build a simple management framework for humans and AI together.
At a minimum, that framework should answer:
Roles and scope
What exactly can this agent do, and what is off-limits?
Which systems can it access (email, calendar, CRM, trading, documents), and with what permissions?
Guardrails and safety
What data must never leave certain systems?
What dollar, risk, or privacy thresholds require explicit human approval?
Review and feedback
How will you regularly audit what your agents are doing?
How will you give feedback so they improve on the tasks that matter?
Escalation and ownership
When something looks off, who is accountable for intervening?
How are edge cases and exceptions handled—by whom, and on what timeline?
You can start small and still be early. One agent helping clean up your inbox, summarize calls, or draft routine documents is enough to begin learning how to supervise, correct, and improve these tools. From there, you can graduate to more ambitious workflows—marketing funnels, research pipelines, or client-service processes—always with a human hand on the wheel.
Action Steps for Each Audience
To make this concrete:
Business owners and founders
Identify one non-critical but annoying process (status reporting, email routing, FAQs) and deploy a simple agent around it.
Write down the “job description” for that agent: inputs, outputs, quality bar, and what must always be escalated to a human.
High-net-worth investors and family offices
Ask every manager, GP, and advisor: “How are you using agentic AI today? What risks do you see, and what controls do you have in place?”
Internally, build a small research or monitoring agent that helps you track themes, managers, or tax issues, with human review required on anything actionable.
Wealth advisors, RIAs, estate planners
Start with back-office use cases where you can easily review outputs: prep work, document drafts, meeting summaries, basic modeling.
Develop an AI policy that covers data handling, client consent, supervision, and documentation, and treat it as an evolving part of your compliance program.
Students and people in their 20s
Learn to design prompts and workflows that agents can execute: “research this,” “draft that,” “summarize these,” “compare those” with clear constraints.
Practice chaining tools—spreadsheets, APIs, basic scripting—so you can orchestrate small “mini-firms” of agents working for you on projects, learning, and side hustles.
Look for or help to start a SLOCLAW FOUNDRY near your campus or in your city. If you are in the Central Coast come check out the first local SLOCLAW Foundry in San Luis Obispo, and rsvp here
This moment will absolutely trigger fear in some people, just as the early internet and mobile waves did. But it is also a rare reset in who gets to participate. You do not have to learn to code. You do have to learn to design outcomes, communicate them precisely, and manage a new kind of team.
Because this is a power-law game, the gap between people who spend the next 2–3 years learning to command agents and people who don’t will not be linear; it will be exponential in opportunities seen, assets managed, and leverage over their own time. The day you decide to learn how to responsibly engage with agentic AI is not just another tech skill choice; it is the day you choose which side of that curve you want to live on.
Keep clawing your way forward!
~Chris J Snook
Related Posts
Related Podcast Episodes
Endnotes
DataCamp – “Power Law: A Pattern Behind Extreme Events”
https://www.datacamp.com/tutorial/power-lawStanford – “Social Networks & Power Laws” (lecture notes PDF)
https://web.stanford.edu/class/cs124/lec/socialnetworks21.pdfNFX – “The Network Effects Bible”
https://www.nfx.com/post/network-effects-bibleMetcalfe’s Law (Wikipedia)
https://en.wikipedia.org/wiki/Metcalfe%27s_lawWall Street Prep – “Network Effects | Definition + Examples”
https://www.wallstreetprep.com/knowledge/network-effects/Trend Micro – “Viral AI, Invisible Risks: What OpenClaw Reveals About Agentic Assistants”
https://www.trendmicro.com/en_us/research/26/b/what-openclaw-reveals-about-agentic-assistants.htmlLakera – “OpenClaw, Skills, and the Lord of the Flies Problem: Why Agentic AI Is Becoming a CISO Nightmare”
https://www.lakera.ai/blog/openclaw-skills-and-the-lord-of-the-flies-problem-why-agentic-ai-is-becoming-a-ciso-nightmareVentureBeat – “OpenClaw Proves Agentic AI Works. It Also Proves Your Security Is Not Ready.”
https://venturebeat.com/security/openclaw-agentic-ai-security-risk-ciso-guideAleta – “The Rise of AI Agents in Wealth Management”
https://aleta.io/knowledge-hub/the-rise-of-ai-agents-in-wealth-managementKPMG – “Agentic AI Is Changing Wealth Management”
https://kpmg.com/us/en/articles/2025/agentic-ai-changing-wealth-mgmt.htmlRoosevelt Institute – “The Risks of Generative AI Agents to Financial Services”
https://rooseveltinstitute.org/publications/the-risks-of-generative-ai-agents-to-financial-services/NetSci – “Introduction to Power Laws” (video)