It’s my independent research project, inspired by my and my family’s needs, that uses advanced AI models to map which public companies and major crypto networks are most likely to create outsized value in the AI era, then bundles that work into clear, data-driven updates for serious retail investors.

I borrow the phrase from Emad Mostaque’s “The Last Economy”: a world where AI commoditizes cognitive work and scarce resources shift to compute, energy, data, and distribution. For investing, that means I focus less on straight-line extrapolations of current earnings and more on who controls those AI-era bottlenecks and flywheels between now and 2030.

I’m not selling hot tips, trading alerts, or hypey narratives; I apply a consistent methodology and framework (“The Last Economy”) across 100+ symbols, and cover both equities and liquid crypto in a single, transparent research stack rather than running a chat room or signal service. I’m not paid to promote any asset and declare my holdings.

It’s just me: a retired tech exec and engineer who grew up in the 70s/80s, studied Computer Science, spent decades building products and infrastructure in and around Silicon Valley, and happened to make life-changing early investments in names like AMZN and NVDA; I’m now financially independent, investing my own capital, and using this project to think rigorously about the AI transition.

It would not be hard to "beat the market" if one hypothetically knew the future. My own experience of Amazon’s and NVIDIA’s products made their futures clear to me. I now use Next Arc to more systematically map the AI-era opportunity set — who stands to gain, who risks being left behind, and how growth and risk stack up across sectors and assets; I personally use that map to inform my portfolio, but the promise of Next Arc is clarity and structure around the 2030 landscape, not a guarantee of market-beating returns.

You shouldn’t trust anyone blindly, including me; what I offer is a clear methodology, consistent scoring, visible disclosures of my own holdings, and a non-sensational, tech-optimistic lens grounded in both quantitative data and first-principles thinking about AI and “The Last Economy” — your job is to treat it as one thoughtful input into your own decision-making, not a substitute for it.

I track ~100–120 public companies across AI chips, cloud, robotics, biotech, energy, sensors, and frontier tech, plus major liquid crypto networks like Bitcoin and Ethereum; it’s a curated universe biased toward technologies that matter in an AI-intensive world.

I was already starting to diversify my AI oriented holdings when I heard about "The Last Economy". The thesis reflected a lot of my own thoughts of the inevitable future. As a result, most of the assets in the universe happenm to be those I was already holding. Each time I hear about something new, I research it and determine if it makes sense to include. They’re included if they play a meaningful role in the AI stack—compute, autonomy, energy, data, or distribution—or if they have asymmetric exposure to Last Economy tailwinds; I exclude assets with poor liquidity, low relevance, or too little transparency for consistent analysis.

The full universe is re-run weekly using the latest models, filings, news, and macro signals; large moves in fundamentals or narratives typically surface the same week.

It’s an AI-driven estimate of potential enterprise-value expansion by 2030 under realistic but transformative scenarios; the buckets help compare how much value creation each asset might capture in a Last Economy world, not precise price targets.

Each risk dimension (execution, financial, competitive, regulatory, and dependency risk) is scored separately using structured prompts and quantitative inputs; the platform then blends them into a single composite that captures fragility rather than volatility.

Crypto is treated as decentralized economic infrastructure, so the focus is on network effects, security budgets, fee growth, roadmap credibility, and ecosystem momentum rather than revenue, margins, or analyst forecasts; the scoring framework is adapted but comparable.

Financials come from public filings and market data; qualitative signals come from curated news, earnings calls, and technical documents; scenario exploration and scoring use GPT-5 grade models plus custom prompts shaped around The Last Economy thesis.

The heavy lifting (scenario generation, scoring, and narrative synthesis) is done by frontier models, but I design the prompts, sanity-check outputs, tune the methodology, and set the universe and weighting logic; it’s the human-guided research that I would do for myself (but better now others are using it).

Yes, if a model output doesn’t feel right then I’ll experiment with engineering or prompting fixes then rerun with clearer constraints; transparency matters, so I correct errors rather than letting the pipeline drift.

In my mind Access is for those new to retail investing who perhaps want to just "dabble". Plus makes sense once you are a little more comfortable with data driven investing. Portfolio is for people like me, experienced tech investors who actively take a portfolio approach and are always following the market. The Free tier is a place for folks to see some of the data and understand if upgrading is right for them.

Free members get access to monthly updates with a subset of simplified commentary covering a small mix of broadly known and somewhat more experimental assets. At each paid tier, the depth of the data and the number of assets covered goes up. At the Portfolio level you get weekly access to full analysis PDFs and CSV files for inclusion in your own models.

Portfolio unlocks all ~100–120 symbols, every data table and CSV, the most detailed data, and the full interactive screener; it’s the complete research stack I personally use to inform my own thinking.

Free, Access and Plus are updated around the 28th of each month. Portfolio is updated around the 7th, 14th, 21st and 28th of each month. Sometimes I will delay the analysis if there is a big raft of earnings being released.

I have turned on the "free trial" flag within Patreon so you should be able to try for a week. At the same time you can join at the Free memebrship level. Finally, the web site includes recent PDFs of all the data from each of the membership tiers so you can see specifically what you would have access to.

You join via Patreon, and your tier automatically unlocks gated pages on nextarcresearch.com through Patreon OAuth; the site checks your membership level and reveals the appropriate tier’s content.

Yes. You control everything through Patreon; upgrades, downgrades, and cancellations take effect through the Patreon site according to their standard practices.

Access gives a smaller curated subset (currently about 15), Plus expands that significantly (about 35), and Portfolio gives the full 100–120-symbol universe; the list evolves as new AI-era assets become meaningful or irrelevant.

Use Next Arc to get one perspective where each company or network sits in the AI-era landscape — who has tailwinds, who faces structural risk, and which names merit deeper investigation.

Yes — the research helps you understand what’s inside those ETFs and which specific names are most relevant, if you choose to explore individual positions.

The PDFs are convenient for those looking for an offline accessible document. The CSV files are convenient for those who might be using a spreadsheet to model their investments. You can decide if either of those matches your needs.

You could glance at the week-over-week score changes, risk movements, and narrative shifts; using that to understand how narratives and risk assessments are shifting.

They are a little tongue-in-cheek experiment. You can use them to explain to your non-investing friends/relatives what the hypothesis for a company is.

This is research only — no trading signals, no financial advice; I show one prospective map, you decide the route.

Yes; Portfolio membership and above includes downloadable CSVs so you can sort, filter, and explore the universe in your own models.

No — it’s research, not advice. I don’t know your risk tolerance, time horizon, or personal circumstances; use this as one input among many.

Yes — I disclose my major holdings and relevant biases so you can understand where my incentives or experience might influence the framing. I am not paid to promote any asset.

Implied multiples reflect potential in a plausible AI-era scenario, not a forecast; real-world outcomes depend on execution, competition, capital cycles, and plain luck.

I track week-over-week changes and evolving scores, but this isn’t a performance-ranked “buy/sell” system; the point is to understand trajectories, not run a backtestable signal service.

I rerun the models with updated context and adjust the scores or narratives each week; the weekly refresh ensures the universe evolves with new data rather than freezing old assumptions.

Macro risk sets the backdrop, but company-specific resilience matters more in an AI-driven world; the framework blends both, but emphasizes structural advantage over short-term cycles.

Models can overweight recent narratives, misread technical details, hallucinate connections, or underappreciate real-world constraints like supply chains, physics or regulation.

It’s scenario-driven, not predictive; it depends on public data and AI interpretation; and it’s better at mapping strategic positioning than timing markets — useful for long-term thinking, not short-term trading.

Because both are competing for relevance in the same AI-era economy — compute, trust, coordination, payments, and digital infrastructure — and a realistic 2030 map needs to show how these systems interact rather than treating them as separate worlds.

I focus on fundamentals: security budgets, fee growth, ecosystem adoption, credible roadmaps, and economic sustainability; token price is a noisy reflection of that, not the core object of analysis.

They feed directly into the risk scoring — regulatory fragility, supply changes, and governance forks all increase uncertainty and are incorporated as structural risks rather than treated as short-term volatility.

AI models are the engine of The Last Economy — they compress cognitive labor, shift value to compute and energy, and reshape competitive dynamics; the models I use also help illuminate which companies are best positioned to ride that shift.

They are direct extensions of the same trend: intelligence becoming cheap and embodied; I treat them as high-conviction frontier categories where breakthroughs in autonomy, wetware, and physical AI could create new dominant players.

Yes — I skip projects with weak fundamentals, low liquidity, unclear governance, or hype-driven roadmaps; if I can’t reasonably model its trajectory in a Last Economy context, it doesn’t go in the universe.

I review the universe continuously and make adjustments as technologies mature or become irrelevant; meaningful additions or removals typically happen every few months, not weekly.

Yes — as the AI landscape evolves, I refine prompts, weights, and inputs; major changes are explained in the weekly update notes so you always know what shifted and why.

I never see your Patreon credentials; authentication happens directly between Patreon and the website via OAuth, and the site stores only the minimum non-personal metadata needed to confirm your membership tier.

I post on Patreon with each refresh cycle for each membership tier.

I am constantly making UI improvements or extending the data available. These small changes are reflected in the changelog. As Patreon membership increases and more funds become available I expect to scale to more assets and to more complex analyses by a broader range of models to benefit from their collective wisdom.

You can message me directly on Patreon — I review all requests and add new symbols if they genuinely fit the AI-era framework and have enough transparency to analyze properly.