Across resource extraction, industrial development, and environmental remediation, the underlying problem is not a lack of concern or action. It is the absence of durable, shared knowledge infrastructure. Data is scattered across jurisdictions, corporate archives, regulatory filings, local records, and personal memory. Much of it is incomplete. Much of it is quietly lost over time.

Each generation ends up rediscovering the same sites, the same constraints, and the same unresolved questions—without access to the reasoning, trade-offs, and conditions that shaped earlier decisions.

This is not a failure of intent.
It is a failure of memory.

Observation Before Intervention

The Global Resource & Industrial Observatory (GRIO) was conceived in response to this gap.

GRIO is not an action organization.
It is not an advocacy group.
It is not a consulting firm.

It is an observatory.

An observatory exists to document, contextualize, and preserve information across time. Its value is not in speed, influence, or authority—but in continuity. GRIO applies that logic to industrial systems and resource-related knowledge, where decisions often outlast the institutions that made them.

In fields shaped by long time horizons—mining, energy, manufacturing, remediation—the consequences of past choices can persist for decades or longer. Yet the documentation that explains those choices rarely survives intact.

GRIO begins from a simple premise: before acting, systems must be understood. And understanding requires memory.

The Problem Is Not Urgency, but Discontinuity

Most modern institutions operate on short cycles:

• Political cycles span years.

• Corporate strategies shift with markets.

• Projects are funded, completed, and archived.

Industrial and environmental systems do not follow these timelines.

A site developed in the mid-20th century may only become a remediation priority generations later. Regulatory frameworks change. Companies dissolve. Agencies reorganize. The original context—why something was built, what constraints existed, what alternatives were considered—is gradually erased.

This creates a structural discontinuity:

• Knowledge is fragmented across time.

• Responsibility shifts across institutions.

• Decisions are made without access to prior reasoning.

GRIO exists to reduce that loss—not by intervening, but by remembering.

A Public Knowledge Infrastructure

GRIO is not a single project or platform. It is a framework for long-term public documentation.

Current initiatives under GRIO include:

• A national mapping of mineral and energy assets

• An observatory focused on industrial legacy and circular recovery

These are not products. They are components of a broader public knowledge infrastructure—designed to be maintained, expanded, and inherited over time.

The goal is not to centralize authority, but to stabilize context. To ensure that future researchers, policymakers, planners, and communities are not forced to reconstruct the same information repeatedly from scratch.

This work is intentionally quiet. It favors accuracy over immediacy, documentation over narrative, and persistence over visibility.

What GRIO Does Not Do

Clear boundaries are essential for institutional trust.

GRIO does not approve projects.
It does not advocate for specific outcomes.
It does not assign blame or authority.
It does not replace regulatory or planning processes.

Its role is narrower—and more durable.

GRIO exists so that decisions, wherever they are made, can be informed by deeper historical and systemic context.

Why a Nonprofit

Some systems are not suited to venture timelines or market incentives.

The time horizon of industrial memory extends beyond typical funding cycles. The value of preserved context compounds slowly. Public trust matters more than growth metrics.

GRIO was established as a nonprofit for a simple reason: some institutions should compound knowledge, not revenue.

This structure allows the observatory to prioritize long-term stewardship over short-term optimization, and public value over proprietary advantage.

An Invitation, Not a Call to Action

GRIO is not built on the assumption that a single organization can hold all relevant knowledge. Quite the opposite.

Much of what matters already exists—in archives, in records, and in the experience of people who worked within these systems. What is often missing is a place for that knowledge to be held, preserved, and made legible across time.

If you have worked in resource development, industrial operations, remediation, planning, or policy, your knowledge likely matters more than you think.

GRIO is designed to hold that knowledge quietly and carefully.

This is not a sprint.
It is a structure meant to last.

Once an agent moves from advising to acting, payment stops being a checkout experience and becomes part of the execution path. That shift sounds small, but it changes everything about what kinds of markets can exist and who gets to participate in them.

x402 Is Built for Agent Payments

The simplest way I’ve come to frame x402 is this: it’s not designed for people to pay each other. It’s designed for agents to pay automatically, inside the execution path, for small units of work. That matters because the internet already has countless “micro” tasks—data queries, API calls, single-use tools, one-off templates—that are economically awkward under traditional payment rails. When the minimum viable transaction cost is too high, we’re forced into blunt models: subscriptions, bundles, ad-driven attention, or enterprise contracts.

x402 points in the opposite direction: pay-per-use, on demand, task-sized settlement. And that’s why the most important question for x402 isn’t “does the protocol work,” but “where does it become the default behavior”—a place where agents don’t just demo payments, but pay repeatedly because the workflow naturally demands it.

Builder Network is where agent decisions are dense

Builder Network, in my mind, is not a content platform. It’s a work network: long-term, repetitive, and outcome-oriented. Builders keep shipping, updating, iterating, and following up. On the other side, investors, ecosystem operators, and partners do the same: they source, filter, research, maintain relationships, and make a thousand micro-decisions that compound over time.

That’s exactly the kind of environment where agents become more than a chat interface. Agents start to run the workflow. And once agents run the workflow, economic actions appear everywhere—small, frequent, contextual payments that are too granular for humans to manage manually, but perfect for agents to execute programmatically. In that sense, Builder Network is naturally one of the most payment-fragmented, decision-dense environments you can build.

Three payment surfaces inside Builder Network

In Builder Network, I see three primary payment surfaces—simple enough to explain, but powerful enough to shape the whole economy.

First: Agent Pay. This is the functional layer. Agents pay for external data and research, for specialized tools, and for services that sit outside the core product. A sourcing agent might buy a single query. A diligence agent might pay for a dataset snapshot. A “context” agent might pay for an enriched profile. These are not subscription moments; they’re execution moments.

Second: Agent-to-Agent services. As tasks get decomposed, agents don’t just call APIs—they call other agents. One agent specializes in background checks, another in summarization, another in pattern detection. Builder Network becomes the orchestration layer where these micro-services coordinate. And orchestration implies settlement: each agent gets paid for its specific unit of work, not for a seat license or a bundled contract.

Third: micro-payments for content, tools, and templates. Some assets are genuinely valuable but not worth subscribing to—one memo template, one checklist, one niche tool used once. A pay-per-use rail makes these markets real, instead of forcing everything into bundles and subscriptions.

These three surfaces are the “natural” x402 territory inside Builder Network. They create stable, repeated, real-workload transactions—exactly the kind of pattern that looks like a path to product-market fit without needing to evangelize the protocol itself.

Endorsements: when payment becomes judgment

The second major pillar is what I’d call endorsement—real people supporting builders with real money, typically stablecoins. Superficially it resembles “tipping,” but functionally it’s closer to a reputation and credit system. It borrows the visibility of a Product Hunt vote, the immediacy of creator tipping, and the global composability of onchain payments—but it goes further because it’s a costly signal.

A like costs nothing. A vote costs nothing. A small payment, even $10 or $50, is different: it means someone is willing to bear a loss if they’re wrong. That’s why it’s informative. Over time, endorsement isn’t just “who’s popular,” but who is consistently supported by many independent supporters, across time. It becomes a legible, auditable layer of credibility—especially meaningful for early builders who aren’t ready for institutional checks but still deserve real backing.

This is also where stablecoins matter. If support is meant to be global—anyone supporting any builder anywhere—fiat rails and platform credits quickly become constraints. Stablecoins make the support portable, spendable, and settlement-native for agents as well.

Incentives: turning contributions into a flywheel

Finally, there’s incentives: rewards for the people who help the network stay alive—contributors who fill in profiles, verify updates, and refer builders or users. This isn’t a gimmick; it’s recognizing that network quality has real behavior behind it, and that behavior should be compensated in a way that scales. Whether that reward is stablecoins, crypto, or a platform credit usable for Agent Pay, the intent is the same: keep the system maintained without relying on goodwill alone.

Incentives are a double-edged tool. If they are too extractive or too easy to game, they distort behavior and collapse into farming. The goal is not to maximize activity, but to reinforce actions that measurably improve the network. Long-term, the cleanest version of this is outcome-aligned: payment only occurs when something real happens—when a referral becomes an active contributor, when a data update proves correct, when an agent-delivered result meets a clear success condition.

As the system matures, it becomes important to distinguish between operational incentives and long-term ecosystem support.

While core incentives remain part of the platform’s economic model, complementary structures—such as foundations—can emerge to support broader, cross-platform initiatives, including fellowships, research, and ecosystem-wide grants.

This separation is not about adding governance overhead. It is about durability. Systems that decouple short-term incentives from long-term support structures are more resilient and capable of sustaining broader participation over time.

If there’s a bigger takeaway, it’s this: x402 makes task-sized settlement viable, and Builder Network provides a place where that settlement is not optional—it’s simply what execution looks like. When we combine that with stablecoin endorsements and pragmatic incentives, we don’t just add “payments” to a product; we start designing an economy where work, support, and credibility can compound.

I’ve been continuously thinking and exploring different paths to get us there. And as large models have continued to evolve, this direction has felt like the natural outcome: making AI Agents a core part of the product. Building a personalized AI Agent for every user—an agent that understands their context, goals, and workflow—has become the natural next step.

In this note, I want to share how I’m thinking about AI Agents as Builder Network’s intelligence layer—and why I believe this layer will shape the product more than anything else.

Why AI Agent?

If you’re in venture—especially in tech—you can’t ignore AI. As models improve, I believe vertical Agents become a new productivity form. I’ve spent years building context in the venture ecosystem, and I’m now building Builder Network as the underlying network graph—the “soil” a venture-native Associate Agent can grow in. That’s why I see AI Agents and Builder Network as a perfect match.

For years, software helped teams document and organize work—but the underlying workflow largely stayed the same. As Agents move into the execution path—where signals are gathered, synthesized, and acted on—the workflow itself begins to be redefined. So in the future, organizations will become leaner—fewer people, higher output. The gap is filled by more capable Agents.

Long term, I don’t think companies will need HR to manage large headcount the way they used to. They’ll need something like AR (Agent Resources)—the function responsible for sourcing, deploying, and managing Agents across the team.

Back when I was at Uphonest Capital and Press Start Capital, a core part of my work was sourcing promising builders—spending hours every day digging through X, LinkedIn, offline events, and hackathons to find the right builder. It was incredibly time-intensive, and the efficiency and ROI weren’t always great. We also ran multiple incubation fellowships—and whether you’re an accelerator or a VC, at the end of the day, it’s all about finding people.

Builder Network’s capital-side and ecosystem-stakeholder users are the archetypal group that has to spend enormous time collecting and analyzing large volumes of information.

Builder Network already provides a rich layer of builder data—but in practice, teams still need people to browse, filter, categorize, analyze, and form first-pass investment judgments on top of it. That’s a huge amount of work, typically handled by interns, analysts, and associates.

In today’s market, headcount is often one of the biggest costs. An AI Agent can take over much of that workload—while effectively training a loyal “team member” that never churns, compounds context over time, and keeps getting better.

A next-generation Associate AI Agent can absorb this time-intensive work, so humans can focus on what actually matters: building high-trust, high-signal relationships with builders.

I view 2025 as “year one” for Agents—similar to the early internet era—when small teams can still generate outsized outcomes. The two keys are flexibility and execution velocity.

The earlier you start, the more compounding advantage you build—data, distribution, feedback loops, and a better-trained vertical Agent. This category rewards momentum.

What Is the AI Agent in Builder Network?

Our mission is to use AI to replace a large share of the time-consuming, low-leverage work in the venture workflow—sourcing, information gathering, first-pass diligence, and memo drafting, including the hours spent to browse, filter, categorize, and analyze builder data into an initial investment view.

Today, the product is a conversational Associate Agent—an Assistant Agent that understands a fund’s strategy, runs continuous, strategy-aligned diligence on builders, and accelerates deal execution. It’s built for the venture ecosystem broadly: builders, L1/L2 infrastructure teams, angel investors, VC funds, incubators, and accelerators.

We’re not building a traditional chatbot—and we’re not building traditional SaaS either. We’re building an object-specific, goal-driven Assistant Agent that moves deals forward and produces the analysis, evidence, and memos behind its recommendations. It understands who you are and what you’re trying to achieve, then runs 24/7 to source strategy-matched builders, filter and analyze their signals, and package everything into a decision-ready view—so the human simply makes the call.

Over time, it becomes a personalized Associate: it learns your thinking patterns, investment thesis, and founder preferences. With persistent memory and continuous learning, it compounds—getting increasingly partner-like in how it selects and evaluates builders. I believe Assistant Agents will become the next-generation production and management layer for venture.

We want every player in the global venture ecosystem—L1/L2 infrastructure teams, angel investors, VC funds, incubators, accelerators, and builders—to have an Agent and become a super-individual in the new era. Over the next decade, this will happen. We intend to be one of the earliest teams to make it real.

The Learning Loop Behind the Agent

Building an Agent for venture, I think the hardest part isn’t the model. It’s the loop. Builder sourcing and evaluation aren’t centralized workflows—they’re distributed cognition. The real edge comes from thousands of micro-decisions: which filters you use, who you click into, who you save, who you message, who you follow up with, and who you pass on immediately.

So we’re not treating the Agent as a “chat feature.” We’re treating it as a new interaction layer—one that captures feedback at the exact moment a decision gets made. The key point is straightforward: venture is one of the few domains with unusually strong outcome signals.

Make an intro or don’t. Follow up or drop. Invest or pass. These aren’t engagement metrics. They’re closed-loop decisions.

The product will learn from two classes of signals: First, explicit decisions. The Agent produces a builder brief and a memo; the user responds with action—shortlist, pass, reach out, follow up. Over time, the system learns your thresholds, your taste, and your firm’s decision style. The Associate Agent gets less generic and more like you. Second, implicit behavior. Which filters you default to. Where you spend time. Which sections of a profile you repeatedly check. What patterns consistently precede a “yes.” The system should learn your operating cadence without adding work to your day.

Given enough cycles, the Agent should move from “general” to “personal.” Not more talkative—more predictive, more opinionated, and more outcome-driven. That also informs the business model. In the near term, we’ll start with subscription plus usage-based pricing. Over time, we expect this to evolve into a pay-per-use model, where Agents are paid directly for the work they perform—searching, analyzing, drafting, and executing—using machine-native payment rails like x402. Long term, pricing should be outcome-aligned, because the point isn’t to sell conversations. The point is to help close deals.

Context Graph: Why Agents Compound Judgment, Not Just Automation

What compounds over time isn’t just model quality—it’s context.

In venture, decisions are rarely rule-based. They’re driven by precedent, exceptions, pattern recognition, and judgment accumulated over years. What matters isn’t just what decision was made, but why it was made in that specific context: what signals were considered, what trade-offs were accepted, and what historical analogies informed the call.

As Agents move into the execution path of venture workflows—sourcing, filtering, diligence, follow-ups—they gain a unique advantage: they can capture decision traces at the moment decisions are made. Every “reach out or pass,” every “follow up or drop,” every “invest or don’t” becomes a durable signal, not just an action.

Over time, these decision traces form what I think of as a Context Graph: a living, queryable record of how judgment is actually applied across builders, markets, and strategies. Not a static database of facts, and not a model’s chain-of-thought—but a compounding map of how decisions happened and why they were allowed to happen.

This is where Builder Network becomes more than a discovery layer. By sitting directly in the venture decision loop, it becomes the natural substrate for this Context Graph to emerge. As the graph grows, the Assistant Agent doesn’t just get faster—it gets more opinionated, more predictive, and more aligned with how each investor and firm actually thinks.

That’s how an Agent moves from generic assistance to genuine judgment support—and why, over time, context becomes the real moat.

The Industrial Legacy & Circular Recovery Observatory

Canada’s industrial history did not end when facilities shut down or companies disappeared.

It continues to shape land, water, industrial structure, and communities today—yet much of this history no longer exists as a coherent, publicly accessible body of knowledge.

Across the country, mines, smelters, refineries, processing plants, and other industrial sites have been built, expanded, transferred, closed, dismantled, and repurposed. Company names changed. Ownership structures shifted. Regulatory frameworks evolved. In the process, information was fragmented across institutions, time periods, and formats, gradually losing continuity.

In many cases, what disappears is not the industrial activity itself—but our ability to understand it as an ongoing structure.

When Industrial History Loses Its System

Industrial activity does not truly end at decommissioning. Even when physical infrastructure is removed, industrial legacy often persists—embedded in geology, water systems, land-use constraints, environmental monitoring requirements, or redevelopment conditions.

Yet this history is rarely held within a unified, durable knowledge system. Relevant information is scattered across federal and provincial records, historical archives, environmental assessments, technical reports, corporate disclosures, and local context. No long-lived public infrastructure exists to assemble, compare, and maintain this information over time.

This absence is not merely a lack of data. It is a gap in public knowledge infrastructure.

When industrial history cannot be traced systematically, long-term research, cross-regional comparison, and intergenerational decision-making become structurally constrained.

The issue is not only what happened—but whether we still retain the capacity to understand it.

Industrial Legacy Is Not a Historical Category

Industrial legacy is often treated as a heritage or archival concern. In practice, it is ongoing.

Former industrial sites continue to intersect with land-use planning, environmental governance, remediation strategies, redevelopment pathways, and community-level conditions. Decisions made decades ago still shape present constraints, risks, and opportunities.

This work is not about assigning responsibility or reopening historical disputes. It is about restoring continuity of knowledge.

Without a stable documentation framework, industrial sites are addressed as isolated cases. Patterns remain invisible. Lessons are not carried forward. Long-term costs—environmental, regulatory, and institutional—compound quietly.

Why an Observatory

The Industrial Legacy & Circular Recovery Observatory is designed as a long-term public knowledge infrastructure.

An observatory does not advocate, regulate, or intervene. Its function is to observe, document, and preserve context over time—making information durable, referable, and intelligible across institutional and generational boundaries.

Much like meteorological or astronomical observatories, its value does not lie in immediate conclusions, but in continuity. It provides a stable foundation upon which research institutions, government bodies, environmental professionals, and policy actors can operate—without prescribing outcomes or positions.

Professional Data Work and Long-Term Maintenance

The data work behind an observatory is, by nature, a long-term professional undertaking.

Information may originate from regulatory filings, archival sources, environmental and technical reports, legacy corporate documentation, and verified contextual input. Each source requires careful curation: attribution, boundary-setting, and ongoing revision where new information emerges.

This approach emphasizes methodological consistency, extensible classification, and realistic assumptions about maintenance over time. Data is not “completed”—it is accumulated, corrected, and sustained.

This is what allows the Observatory to function as a mature non-profit public initiative, rather than a time-bound campaign or a one-off dataset.

Part of a Larger Public System

The Industrial Legacy & Circular Recovery Observatory is one project within a broader non-profit framework.

That framework approaches public affairs through infrastructure-building rather than single-point intervention. It prioritizes systems that can evolve, persist, and be maintained across technological cycles and generations.

This perspective enables engagement with complex, long-horizon challenges—where continuity matters more than speed, and structure matters more than visibility.

From this standpoint, the significance of the Observatory lies not in how much it covers today, but in whether it can remain reliable ten or twenty years from now.

Closing

Industrial records decay naturally over time. Files are archived, formats become obsolete, institutions reorganize, and firsthand knowledge fades. Waiting for completeness often means losing context.

Visibility precedes decision-making.
Documentation precedes action.

Observatories do not create reality.
They ensure that reality is not forgotten.

Seeing what still exists is the starting point for any responsible, long-term approach to circular recovery and industrial legacy.

Recently, I listened to an episode of Tucker Carlson’s podcast titled One of America’s Biggest Gold Wholesalers Exposes the Most Common Gold Scam Enslaving the Country.

The guest, Christopher Olson, one of the largest gold wholesalers in the United States, speaks with striking candor about a disturbing reality: a significant portion of American retirement savings is being quietly eroded by what is, in many cases, a perfectly legal gold scam.

This podcast goes far beyond gold itself. Gold is used as a lens to examine much larger questions about the modern financial system. It prompted me to reflect more broadly on gold and silver, precious metals and commodities, minerals and mining, inflation, Bitcoin, the fiat currency system, and the role of the Federal Reserve.

The conversation begins with an unusual anecdote. Tucker recalls that a gold company once offered him $20 million to promote their gold products. The offer felt inherently suspicious. Gold is a commodity—its price is set by global markets. Why would anyone need to pay such an extraordinary marketing fee to sell gold?

The answer, Olson explains, is simple: it isn’t really about gold. These companies sell “commemorative” or “collectible” coins at prices often double the underlying value of the gold itself, marketing them as safe, retirement-friendly investments. The product is legal, the markup is hidden, and the cost is borne almost entirely by unsuspecting retirees.

Inflation as a Distribution Story

What struck me is that this kind of “legal scam” isn’t just a story about gold marketing. It reflects a deeper mechanism that shows up everywhere in the modern financial system: inflation is often less about prices rising, and more about who gets the new money first.

Olson frames it in a way I found unusually clear. When new money enters an economy, the earliest recipients—those closest to the source of issuance—can spend it before the rest of society has adjusted its expectations. The market hasn’t yet received the “signal” that there is excess money in circulation, so prices don’t immediately change. In that window, early recipients effectively gain purchasing power that others don’t. That gap—quiet, uneven, and hard to see in real time—is where inflation’s most consequential effects often come from.

He illustrates it with a simple thought experiment. Imagine everyone living on a small desert island with a stable economy and relatively stable prices. Overnight, a magical event doubles the amount of money in everyone’s bank account—evenly, for everyone. If everyone understood this collectively, they’d immediately realize that the prices of everything should also double. Doubling money doesn’t double goods, services, or real wealth—it only doubles the claims on that wealth. In that scenario, no one truly gains an advantage, because everyone’s purchasing power changes at the same rate.

Now change one detail: instead of everyone receiving new money, only a small group does. Prices won’t instantly adjust, because the broader market can’t detect what just happened. The new money enters quietly through a narrow channel, and the people who receive it first can buy real goods and services as if nothing changed—while everyone else discovers the consequences later, through higher prices and a weaker currency. Olson describes it as a kind of “hack”: allowing some participants to acquire claims on real wealth that they didn’t earn, simply because they were positioned closer to the point of money creation.

That’s why this isn’t just a macroeconomic theory—it’s a distribution story. And it’s also why he makes a remark: gold is the original “cryptocurrency.” Not because it’s digital, but because it is hard to manufacture, difficult to counterfeit, and resistant to arbitrary expansion. In a world where money can be created by fiat, the appeal of “hard money”—whether gold, or today’s Bitcoin—starts to make psychological and financial sense.

Where Does Gold Come From?

If inflation is a story about distribution—who gets purchasing power first—then it’s easier to see why people keep returning to gold. But before treating gold as an abstract hedge, it’s worth grounding it in something simple: gold is a physical commodity with a real supply chain. It is mined, refined, standardized, vaulted, and traded through a few highly concentrated channels.

On the market side, price discovery tends to cluster around a handful of hubs. In the U.S., COMEX (CME Group) in New York anchors much of the futures market. In Europe, the wholesale physical market is organized around the London Bullion Market Association (LBMA)—a standards-and-settlement network best known for “good delivery” bars. In Asia, the Shanghai Gold Exchange plays a comparable role.

On the supply side, newly mined gold is only part of the picture. Annual mining output expands total above-ground supply by roughly 1–2%, but most gold already exists—and it doesn’t disappear, it circulates. A large share of supply comes from existing holdings being sold, recycled, or reallocated through the secondary market.

This is where refineries matter. Refineries bridge the messy physical world and the standardized financial one: they take dore from mines and scrap from the secondary market, and convert it into trusted, tradable bars that meet institutional specifications. In practice, refineries don’t just process metal—they turn physical gold into financial-grade gold.

So when people say they’re “buying gold,” they’re not only buying a metal. They’re buying into a system of extraction, standardization, custody, and market structure—a physical network that sits quietly underneath modern finance.

Fiat Money, the Federal Reserve, and the Hidden Tax

Gold’s supply chain is slow, physical, and constrained. Fiat money is the opposite: it can be expanded by policy, routed through institutions, and injected into the economy through narrow channels.

Olson argues that the ability to create artificial credit or money concentrates power in the hands of a relatively small set of actors: central banks, primary dealers, and the institutions closest to issuance. When governments can finance themselves by expanding credit and monetizing debt, they don’t always have to ask citizens to pay directly through visible taxation. Instead, they can impose something quieter: a hidden tax on everyone who holds the currency.

And because the U.S. dollar is not just America’s currency but a global one, that hidden tax doesn’t stop at U.S. borders. It is effectively spread across households, businesses, and governments worldwide—anyone whose savings or reserves are held in dollars.

This is why the gold standard still matters in these conversations—not as nostalgia, but as a concept of constraint. Under the post–World War II system, the dollar was pegged to a fixed amount of gold, giving holders of dollars a clearer accounting of what a “dollar” was supposed to represent. The discipline wasn’t perfect, but the principle was simple: money creation had a hard reference point outside politics.

Once that constraint is removed, money becomes more elastic—and so does state capacity. The modern world has gained flexibility, but it has also normalized a system where purchasing power can be diluted without an explicit vote.

In that light, gold’s role becomes clearer. Gold doesn’t “solve” the fiat system. But it does something uniquely uncomfortable: it measures. It acts as a barometer of confidence, a check on narratives, and a reminder that not all forms of money are created—or expanded—equally.

Conclusion

Taken on its own, the “commemorative coin” scam looks like a niche story—just another case of aggressive marketing and opaque markups. But the longer I sat with the episode, the more it felt like a microcosm of something larger.

At its core, the scam works because of asymmetry: one side understands pricing, liquidity, and incentives; the other doesn’t. Inflation often works the same way—not merely as rising prices, but as a quiet transfer of purchasing power to those closest to money creation. And gold, for all the mythology around it, is ultimately just the opposite of that: a hard, physical asset with real constraints, moving through a real supply chain, priced through a concentrated market structure.

Gold doesn’t fix the fiat system. Bitcoin doesn’t either. But both attract attention for the same reason: when trust in institutions weakens, people look for forms of money that feel harder to bend—assets with clearer rules, clearer limits, and fewer hidden levers.

If there’s one lesson I’m taking from Olson’s story, it’s not “buy gold.” It’s simpler—and more uncomfortable: in finance, what matters most is not the label on the product, but who controls the pricing, who controls the channel, and who bears the hidden costs. That’s true for collectible coins, for inflation, and for the monetary system itself.

Over the past year, I’ve watched a new force emerge in Canada’s conversations about economic growth, innovation, and national strategy — Build Canada.

It is not a political party, not a think tank, and not a traditional nonprofit.

It is closer to a civic movement initiated by builders, centered on technology, execution, and action.

What Is Build Canada?

Build Canada positions itself as:

“A non-partisan civic movement that connects, equips, and amplifies high-agency builders who want to make Canada more prosperous.”

Its core principles are:

• Canada’s systems are not broken — they simply lack people pushing them forward.

• Civic participation should mean more than voting; it should include building, creating, opening data, and offering solutions.

• Public policy and public discourse need more entrepreneurial perspective + technical capability + data transparency.

If traditional politics runs on debate, elections, and media, Build Canada runs on

builders, data tools, and open collaboration.

What Build Canada Is Doing

Build Canada’s core contributions can be grouped into three categories:

1) Improving Public Information Flow Through Technology

Over the past year, they have built multiple civic-tech tools, including:

• Trade Barriers Tracker – tracks interprovincial trade barriers

• Canada Spends – visualizes federal spending

• Election Promise Tracker – monitors campaign commitments

• Government Progress Monitor – measures federal progress in real time

These tools share several characteristics:

• Data is open and transparent

• Designed for the general public

• Simple, intuitive visualizations

• Updated quickly, without being slowed by government processes

→ In essence, they address the problem:

“Government data may be public, but ordinary citizens can’t actually use it.”

2) Mobilizing High-Agency Builders

Build Canada’s most important resource isn’t data — it’s people:

• Entrepreneurs

• Startup founders

• ESG analysts

• Doctors

• Teachers

• Journalists

• Lab & University research groups

• Community organizers

• Cultural leaders

… These individuals may not care deeply about politics, but they care deeply about one question:

“Can Canada do better?”

Build Canada brings these people together and organizes them to participate in national-level issues — a form of civic organization that hasn’t existed in Canada for decades.

3) Shaping the Public Narrative Through Data and Real-World Experience

Build Canada is introducing a different way of talking about national issues:

• Policy is not something only politicians can discuss

• The experience of entrepreneurs belongs in public debate

• National growth is everyone’s responsibility

• Data should be understandable to all citizens

• Public decisions need a more accurate factual foundation

They have even invited entrepreneurs to write policy memos and publicly released them during the federal election.

This is innovative, somewhat risky, and extremely important — because it is changing how public policy is produced in Canada.

The Real Issue Behind Build Canada: The “Execution Gap” in National Governance

Canada’s core challenges are not:

• a lack of money,

• a lack of policy,

• or a lack of resources.

The real problem is:

slow execution, fragmented policies, unclear accountability, and insufficient long-term planning.

Build Canada is essentially trying to address the tension between:

the execution gap within government
vs
the action capacity within the builder community

Their goal is to structure, formalize, and digitize this civic action capacity —
and make it part of public governance.

Behind this effort are three underlying assumptions:

1. Canada’s institutions allow — and even welcome — bottom-up reform

This is an advantage.

2. Government alone cannot handle the full complexity of modern problems

This is reality.

3. Entrepreneurs and technologists are becoming new protagonists in public problem-solving

This is the trend.

The Potential Impact of Build Canada

1) On Public Policy: Making Solutions More Important Than Criticism

In the past 20 years, Canada’s public conversations have been filled with criticism but very few solutions. Build Canada shifts this dynamic by bringing a “builder’s perspective” into the policy arena.

Instead of abstract debates, discussions on housing begin with supply, construction efficiency, and permitting systems. Conversations about economic growth center on entrepreneurial ecosystems, capital flows, and talent mobility. Innovation policy is examined through regulatory frameworks, experimentation space, and technology adoption.

The result is a policy environment that becomes more action-oriented, data-driven, technically grounded, and solution-focused — a meaningful departure from the status quo.

2) On the Political Landscape: Strengthening Non-Partisan Civic Power

For the first time, Canada is seeing a civic movement that:

• can mobilize people nationwide,

• has the resources to build public tools,

• can shape public opinion,

• and is supported by a vibrant community of builders.

It is not a political party, but political parties will inevitably seek alignment with it. Its influence is likely to rise quickly in the coming years.

3) On Young People: Offering a New Way to Participate in Public Life

Traditionally, young Canadians participated in nation-building by joining the public sector, volunteering, donating, or voting.

Build Canada introduces a different way to contribute:

• building data tools,

• writing policy memos,

• creating open databases,

• developing civic AI,

• modeling policy impacts,

• analyzing federal budgets,

• and participating in a national builder network.

For many Canadians in their 20s and 30s — especially those in tech or engineering — this form of participation feels far more natural and meaningful.

Conclusion: Why Build Canada Deserves Our Attention

I believe Build Canada matters for several reasons:

1) It connects technology with public governance — a necessity of the 21st century.

The challenges of today require tools, data, and technical problem-solving. Build Canada bridges a gap that has long existed between civic issues and modern capabilities.

2) It restores data as a civic right.

Information becomes transparent, readable, searchable, and visualized — not buried in PDFs or scattered across government sites.

3) It brings the builder’s action-oriented mindset into national issues.

This is something Canada has not seen before: a movement that treats policy problems as solvable design challenges, not abstract debates.

4) It offers Canada a new national narrative.

“Canada can be better” is no longer just a slogan — it becomes an executable project.

This is an independent, non-political reflection from a builder’s perspective. The views expressed are solely my own and not those of any organization.

But the truth is:

Few Canadians can actually visualize where the mineral resources that power our economy—and will define our future—are located.

And that’s a problem.

Minerals like lithium, nickel, cobalt, copper, and rare earth elements are the backbone of clean energy, EV batteries, advanced manufacturing, and global supply chains. Canada is one of the world’s key suppliers—but the public visibility of this sector is fragmented, scattered, and difficult to navigate.

So I want to build something to help.

Today, I’m introducing the Canadian Mineral Resource Map:

a simple, open-access, interactive tool designed to help Canadians understand the geology, geography, and strategic importance of our mineral resources.

Why I Want to Build This

Canada has enormous potential in critical minerals — not just for economic growth, but for industrial development, technological advancement, and global leadership.

But the underlying data is:

• buried across federal and provincial sites

• highly technical

• hard for citizens to interpret

• lacking a unified national overview

Yet minerals are at the center of some of the biggest questions facing the country:

• Where are the opportunities for regional economic development?

• Which provinces dominate which minerals?

• Where are companies exploring, developing, and producing?

• How do we strengthen Canada’s role in global supply chains?

A modern country needs modern tools.

And that starts with visibility.

I wanted to create something simple, factual, accessible—something any Canadian can click through and immediately understand.

What the Tool Does

The Canadian Mineral Resource Map brings together public data from:

• Natural Resources Canada (NRCan)

• Provincial geological surveys

• Canadian Mining Journal

• Public company filings (NI 43-101)

• Industry and government reports

…and consolidates it into a single, intuitive interface.

You can explore:

• mineral types (gold, silver, copper, zinc, etc.)

• provinces and territories

• development stages

  • Exploration

  • Development

  • Production

Click on any deposit, and you’ll see:

• the project name

• the mineral(s)

• the operator

• its location

• and its current development status

You’ll also see national statistics at a glance—like how many gold or lithium deposits Canada has, and which provinces are most active.

It’s simple by design.

Public data should be understandable by the public.

Why Minerals Matter More Than Ever

The world is accelerating toward electrification, automation, clean energy, and geopolitical realignment.

Minerals are the backbone.

Countries that control reliable supplies of critical minerals will have an advantage in:

• clean energy competitiveness

• EV manufacturing

• battery cycle innovation

• national defense

• global trade influence

Canada is uniquely positioned:

We have the minerals. We have the land. We have the institutions.

But we don’t yet have the public literacy.

For many Canadians, natural resources feel like something happening “somewhere far away.”

This map is a small step toward making it visible, understandable, and relevant.

Public Data as Public Infrastructure

One belief drives this project:

Public data is a form of public infrastructure.

The more accessible and transparent it is, the more empowered a society becomes.

Tools like Trade Barriers Tracker and Canada Spends, built by Build Canada, show how powerful civic technology can be when it helps Canadians better understand government, policy, and national issues.

I hope this Mineral Resource Map contributes to that same mission.

It is:

• non-partisan

• open-access

• factual

• built for the public interest

My hope is that this becomes a foundation for deeper conversations about how Canada can lead in the next generation of economic development.

A Personal Note

I’ve always believed that builders can create public value—not just companies, but tools, ideas, and infrastructure that help people understand the world more clearly.

This project reflects a personal mission of mine:

to contribute to Canada’s future through work that is transparent, accessible, and grounded in data.

If you’d like to collaborate, build new tools together, or explore future public-good projects, feel free to reach out.

Thank you for reading — stay tuned for the official launch of the Canadian Mineral Resource Map.

The episode is based on two books:

• Autobiography of Andrew Carnegie

• Andrew Carnegie by David Nasaw

I have loved reading biographies since I was a kid, and this was the first time in a long while that I experienced one through a podcast instead of a book. As I listened, I took notes — fragments of ideas, turning points, sentences that stayed with me — and I wanted to shape those notes into this essay.

For me, Carnegie’s life is not merely the story of a poor Scottish boy becoming a millionaire. It is a study in how a young mind observes the world, responds to change, and learns to find a place inside forces larger than himself.

Hearing about the world he lived in, the experiences he had, and the decisions he made, I found myself reflecting on all kinds of things — history, society, culture, the humanities, life, and work.

I’m going to divide Carnegie’s life into two phases, using his investment in Adams Express as the turning point. It was the first moment he entered the world of capital, and it changed the trajectory of his life.

So in this essay, I’ll walk through both phases — what he experienced, what he chose, and what those moments made me think about.

From Childhood to Becoming Thomas A. Scott’s Right Hand

Carnegie’s story begins with a disruption. When he was young, the mechanical loom arrived and wiped out his family’s weaving trade. The first Industrial Revolution brought enormous growth, but it also displaced countless families — including the Carnegies.

It made me think about today. Nearly two hundred years later, what will AI or quantum computing do to our existing social structures? Will they reshape jobs, industries, and families the same way the mechanical loom reshaped Carnegie’s world?

After his father lost work, the Carnegies looked around Scotland and saw no path forward. So they made the bold decision to immigrate to the United States. In Carnegie’s early years, I could see the outlines of many immigrant stories — including echoes of my own. How a young outsider adapts to a foreign culture, learns its rules, survives its uncertainty, and slowly finds a place to stand.

Three humble jobs — but not a humble mindset

In America, Carnegie worked three extremely humble jobs: bobbin boy, messenger boy, and telegraph operator. But he approached these jobs very differently from the people around him. He wasn’t just working to earn a wage — he was studying, absorbing, preparing for a future he couldn’t yet name.

He treated small jobs as foundations for a larger future.

Two small stories that reveal his character

1. The messenger-boy interview

During his interview for the messenger job, he was hired on the spot — and he started work immediately. His uncle, who referred him, was still waiting outside, unaware that Carnegie had already begun his first shift.

Carnegie later wrote: “I think that answer might well be pondered by young men. It is a great mistake not to seize the opportunity.”

From this, I realized how he approached uncertainty: move quickly, reduce randomness, and avoid letting unpredictable factors block your path.

He never assumed time was on his side.

2. Remembering names

As a messenger boy, he memorized the names and faces of every businessman he met.
I’ve been reading Dale Carnegie’s How to Win Friends and Influence People recently, and there’s a chapter devoted to this exact idea: the importance of remembering names.

It’s a small detail, but small details often reveal about a person’s orientation.

Investing in himself outside of work

He joined debating societies and practiced public speaking. He discussed religion and politics with friends in Henry Phipps’s cobbler’s shop. All of these activities looked “unprofitable” on the surface, but they later became his most important abilities: persuasion, negotiation, relationship building, writing and staying composed in public.

The self-improvement you do when you’re young isn’t for the present—
it’s for some unknown position your future self may one day be asked to step into.

Becoming Thomas A. Scott’s assistant

Eventually, he caught the attention of Thomas A. Scott from the Pennsylvania Railroad. Carnegie took initiative, did things far beyond his job description, and earned Scott’s trust.

He later wrote: “The battle of life is already half won by the young man who is brought personally in contact with high officials; and the great aim of every boy should be to do something beyond the sphere of his duties—something which attracts the attention of those over him..”

By this point, he had already built a remarkably solid early career — not by luck, but by how he moved through the world.

Adams Express — His First Steps Into Capital and Ownership

One day, Scott asked if he wanted to invest in a new venture: Adams Express.
Carnegie mortgaged his family’s home and said yes. This was his first experience with capital returns, and it sparked a new line of thinking for him:

What does it mean to own something?
How does money create more money?

This was the true beginning of his life as a capitalist.

Entering new industries early

He stayed alert to emerging industries. When the oil industry was just beginning, he invested in Columbia Oil.

This reminded me to stay alert as new breakthroughs like AI and quantum computing emerge — to be sensitive to information and early signals.

Choosing ownership

At 29, Carnegie resigned from the railroad to focus on ventures he actually owned. He entered iron production and private bridge-building, relying on the relationships he had built over the years — especially within the railroad companies.

This shift was not just a career change. It was a change in identity: from worker to owner.

He did many things at once

He became a financial middleman for industrialists and bankers. He was a natural marketer, quick to understand trends. He built networks, hosted dinners, formed partnerships, and constantly absorbed industry information.

He didn’t just work — he positioned himself at the center of information, relationships, and opportunity.

Conclusion

From Carnegie’s story, I saw how a young man can start with almost nothing and still find a path toward something larger — not by luck, but by how he chose to see the world, how he positioned himself, and how he responded to the opportunities that appeared.

When I look back at the factors that shaped his rise, many of them are not tied to his era. They are patterns that remain true today, and patterns that I find relevant to my own life as well:

1. Choose the right circles and environments.

   The city you live in, the industry you enter, and the people you surround yourself with matter more than we admit.

2. Learn from people who are older, better, and more experienced.

   Being around those who have already walked further pulls you upward.

3. Have the courage to take risks.

   Avoiding risk is often the biggest risk of all.

4. Don’t let opportunities wait.

   When something opens, act quickly and do your best — hesitation creates uncertainty.

5. Don’t work just to work; work to learn.

   Every job is a chance to grow, to build skills, and to position yourself for what comes next.

6. Stay alert to new industries and new technologies.

   Enter early, stay curious, and keep your information senses sharp.

7. Build the ability to gather information and build relationships.

   Becoming someone who can connect people, read situations, and communicate with ease is its own kind of advantage.

These principles don’t guarantee success, but they shape the possibilities available to us.

And that, more than the wealth Carnegie eventually gained, is what makes his story worth learning from today.

What I have built

So, what exactly is the Onchain Builder Network?

If I had to put it in a single sentence, it’s:“A global, reward-incentivized builder network—community-sourced via Join/Refer—with AI-powered discovery so L1/L2s, VCs, and accelerators can quickly find the right early builders.”

It’s designed for L1/L2 teams, angel investors, VCs, incubators, accelerators, and founders who are looking for support.

I’ve been asking myself these questions: Why not use Twitter, LinkedIn, or other existing platforms? What options already exist, and what challenges do builders or VCs face when using them? Ultimately, what is the real pain point? I clarified my thoughts while drawing the illustration below. 👇

Imagine you’re a VC starting your morning sourcing routine. Instead of scrolling endlessly on Twitter or sifting through cold emails, you open the Onchain Builder Network. You tell your AI agent to set a hard bar — for example, founders with two successful exits who are now building in payments and based in Vancouver — or you apply filters such as city, stage, track, or chain manually. Instantly, the AI generates a ranked shortlist tailored to your preferences, complete with explainable scores showing why each founder is a fit. With one click, you can reach out directly. As you provide feedback, the system learns and fine-tunes your preferences. The result? More qualified leads each day, a higher reply-to-meeting rate, and hours of due diligence time saved.

The 7-Day building journey

The 7-day building journey unfolded pretty much as I had planned in the last article. Day 1 was about defining the needs. On Days 2 and 3, I focused on designing the full workflow and features. Days 4 to 6 were spent on rapid development using Lovable, and the final day was dedicated to testing the MVP.

Throughout the week, I posted daily updates on X to share my progress. Here’s the post from Day 1, and another from Day 6 when I wrapped up the basic feature development.

Learnings & Reflections

During this 7-day vibe-coding sprint, I learned three practical things.

• First, Lovable’s auto-generated UI often diverged from my hand-drawn wireframes and what I had in mind; next time I’ll import Figma designs instead of uploading sketches.

• Second, detailed prompts are everything—without a fully specified brief for Lovable, I burned a lot of time on rework. In the future, I’ll use a structured prompt template (see the template below) and have ChatGPT generate the brief.

• Third, I had ChatGPT create a QA test prompt for Lovable; otherwise, step-by-step manual testing wastes time and credits—even though running the tests in the command line showed a lot of errors. 😂

Even though the process didn’t follow my original plan exactly, AI still boosted my productivity in a big way. With Lovable, I was able to generate a simple MVP that clearly demonstrated my idea — and that was enough. As I kept learning how to use Lovable more effectively to produce an MVP I was happy with, it hit me: in the future, the most important skill for engineers won’t be writing code, but mastering AI tools.

At the same time, the solo founder + AI model places much higher demands on a founder’s overall skill set. You have to handle many things on your own — design, product, user acquisition — and most importantly, master and make full use of the latest AI tools. Still, I believe we’ll see more and more solo founders, empowered by this model, go on to build great companies and products.

Let’s circle back to the Onchain Builder Network. It all started with a sudden idea 💡that hit me while riding the Canada Line SkyTrain toward Waterfront. Of course, I know it wasn’t purely random — it came from years of accumulated experience as a builder, an incubator, a VC, and helping scale L1/L2 ecosystems.

I believe this network can be especially valuable for founders who have great ideas but lack resources and connections. Instead of wasting energy chasing funding or searching for the right people to help, they simply fill out their information and that’s it, let the network do the rest. This way, they can stay focused on building their product — while capital allocators and infrastructure partners come to them.

The Onchain Builder Network is essentially a builder marketplace. It’s like an AutoTrader designed for VCs: you’re not browsing cars, you’re browsing the right builders you want to back and build alongside.

What’s Next

My next step is to refine the MVP and make it truly usable. I plan to create a demo and write a simple user guide, then run some internal testing with close friends first. The One Week Vibe Coding series is still ongoing — and I’m excited to share what comes next. 🚀

This article mainly covers two things: first, why I’m doing the One Week Vibe Coding a Side Project challenge; and second, why the project I’m going to build is Onchain Builder Network.

Why I Decided to Do This

Humanity has gone through several industrial revolutions, each transforming productivity. To me, AI is the Fourth — and this one-week Vibe Coding sprint is my way of proving it.

Since 2005, Y Combinator has changed and led a new trend — investing in young tech founders. Over the past two decades, the “co-founder” model has gradually become the default in startups, and having a technical co-founder is now almost considered a must.

But now, AI has changed everything. We are entering a new era. Tools like ChatGPT, Grok, and others have already become part of many people’s daily routines, fundamentally shifting behaviors and workflows. These tools dramatically boost productivity, and many small to mid-sized companies — especially startups — no longer need to rely heavily on traditional human engineers. Instead, they are increasingly replacing those roles with AI.

I believe this will have a profound impact on the startup world. We are moving from the traditional co-founder model (business co-founder + technical co-founder) to a new model: solo founder + AI. In this model, AI helps the solo founder handle everything from early-stage ideas to building the MVP.

That’s why I’m starting this “One Week Vibe Coding a Side Project” experiment. Over the next 7 days, I’ll use Lovable to turn my idea into an MVP — proving that the solo founder + AI approach will become mainstream in the next decade.

Why Onchain Builder Network

Four years ago, my first startup was an online tutoring platform — essentially a network where tutors and students could register, find each other, and match based on their needs. Back then, I had no idea that four years later I would once again be building a network — this time, Onchain Builder Network.

But in reality, I’ve been quietly working on “connecting people” this whole time — whether by attending events, hosting my own, or simply having coffee chats. Over these four years, I’ve come to deeply understand the importance of networks.

Humans are inherently social, cooperative beings. Interaction and collaboration are written into our DNA — they are a fundamental rule of nature. If we were merely isolated individuals with no connections between us, the world as we know it would not exist. It is only through communication and collaboration — from the smallest tribe or local community to a nation, to the entire planet (even now as a “global village”) — that humanity can unlock its full potential and create value far beyond what any single person can achieve.

In the past two years, I’ve hosted numerous offline events in different Canadian cities — Vancouver, Calgary, Ottawa, and more (you can find them on my personal website). These events generally fell into two categories:

1. Startup networking events — connecting founders and VCs.

2. Onchain events — bringing more people onchain and supporting onchain builders in Canada.

Through these experiences, I’ve once again realized how important networks are — how finding and connecting with founders, VCs, and onchain builders is a universal need.

In organizing events and working on onchain projects, I’ve noticed that many Layer 1s, Layer 2s, and infrastructure projects need to attract more builders into their ecosystems. For example, I organized Canada’s first Base community meetup—supported by Base—and saw that builders want much deeper ecosystem support and engagement.

At the same time, as a VC, I constantly face the challenge of sourcing promising early-stage builders. In running hackathons and incubators, I’ve seen firsthand that finding, conducting due diligence on, and successfully inviting great builders is one of the biggest challenges for any VC or investor.

This is why I believe Onchain Builder Network is worth building — a dedicated space for connecting onchain founders, builders, investors, and ecosystems, helping them find each other faster and collaborate more effectively.

Plan

Team: Me + AI 😎

Step 1 Define Needs

• Deliverables: one-liner, target users, core pains, key use cases.

Step 2 Design Features & Workflow

• Deliverables: feature priorities, page map, core workflows.

Step 3 Prompt Development

• Goal: use small, focused prompts to automate the “busywork” .

Step 4 Testing

• Deliverables: test checklist, trial feedback, production URL.

Seven days. One small, real product. Let’s ship and see what one focused week can do. I’ll share the demo and lessons in Part 2 next week.

P.S. I designed all the illustrations myself—my first time doing the entire design in Figma. It took about four hours, and it was totally worth it. Shout out to myself !!