Thanks for the question - in brief, I'm trying to gather opinions as to whether M.I.N.D. (see below) is truly an effective metric to evaluate: "if AI capabilities keep improving and diffusing, how well positioned is this entity to capture second-order value from that process?".
M.I.N.D. / "Last Economy"
The "Last Economy" framing comes from Emad Mostaque's book of the same name and is a way of thinking about where long-run value concentrates when intelligence becomes abundant. M.I.N.D. is the operationalization of that idea from the book and positioned as a better "yardstick" than current metrics like GDP or other traditional, scarcity-oriented financial metrics. For background on the broader thesis, Emad has written and spoken about it publicly here: https://ii.inc/web/the-last-economy. [It's a quick read for those familiar with the AI space and IMHO an important and relatively accessible read for anyone planning to live in the future].
At a high level he outlines:
- Material: control over scarce physical inputs that AI depends on (energy, fabs, supply chains, hardware)
- Intelligence: leverage over computation, models, or inference at scale
- Network: data, ecosystems, distribution, or flywheels that compound usage
- Diversification: exposure across multiple AI value paths rather than a single bet
The specific choice to multiply the dimensions (rather than add them) is also from his formulation: it encodes the assumption that missing one leg meaningfully caps long-run alignment. That assumption is very much up for debate, but the better an entity (country, company, person etc.) can score along the dimensions the better prepared they are for the Last Economy future governed by more physical than metabolic processes, and the ability to convert energy into computation.
I do want to stress that this chart is my interpretation, not an official formulation.
Valuation tension / expectation saturation
I'm not trying to introduce a standard valuation metric here, and there isn't a single reference I'd point to. The idea is closer to a sentiment / expectation proxy than intrinsic value.
Concretely, I'm asking: how optimistic does current pricing appear relative to a longer-horizon narrative based on how well a company may thrive or suffer in The Last Economy scenario?
To keep it interpretable, I approximate that using:
- a relative long-term opportunity estimate (2030 horizon, directionally based on a creative, scenario driven process)
- divided by price position within the 52-week range as a proxy for how much optimism or skepticism is already expressed
It's intentionally blunt and debatable. I'm treating it as a secondary axis — useful for highlighting where narratives feel "fully priced" versus where they don't — not as a valuation model.
I realise there is a lot of context underlying my question. Thanks for your patience and interest.
OP, I built this chart as a way to stress-test AI narratives using a simple structural framework.
The “thing” I’m showing is the mapping itself: it separates two questions that often get conflated:
(1) how structurally aligned a public entity is with long-horizon AI value creation, and
(2) how much of that story already appears to be priced in.
The x-axis (M.I.N.D.) is a composite structural-alignment score (Material, Intelligence, Network, Diversification, inspired by the “Last Economy” framing). Scores are synthesized per entity after a skills/assets/capabilities analysis and a review of analyst research, using an LLM as a structured aggregation tool rather than an oracle.
Roughly speaking: Material captures control over scarce physical inputs, Intelligence reflects leverage over computation and models, Network captures ecosystem and data flywheels, and Diversification reflects exposure across multiple AI value paths.
The y-axis (valuation tension) is a rough proxy for expectation saturation. I’m treating it as a secondary signal; the primary thing I’m testing is whether structural alignment and narrative intensity decouple in interesting ways.
One weakness I’m actively unsure about is the M.I.N.D. formulation itself. Multiplying the four dimensions strongly penalizes any missing leg, which may or may not reflect how value actually compounds in AI systems. If that assumption is wrong, the framework will systematically mislead.
I’m especially interested in:
- whether these four dimensions are the right ones
- whether multiplication is the right way to combine them
- where this framework would clearly fail
I’ve been working on "Next Arc Research" — https://nextarcresearch.com - a wrapper around my curiosity to understand how AI, compute, and capital might change markets by 2030.
It’s not a trading tool or product. More like a weekly, machine-assisted research project. Each cycle I run analyses on 120+ public companies across semiconductors, cloud, biotech, energy, robotics, quantum and crypto. The framing is inspired by Emad Mostaque’s “The Last Economy” thesis — the idea that when intelligence becomes cheap, the physics of value creation start to look very different. I originally built it for myself and retail investors in my family but I figure it could have more general utility so prettied it up a bit.
The system uses large-model reasoning (GPT-5+ though I've also tested Sonnet, Gemini and Grok) combined with structured scoring across technology maturity, risk, competitive positioning, and alignment to AI-era dynamics. The output is static HTML dashboards, PDFs, and CSVs that track month-over-month shifts. I'm adding to it weekly.
Mostly I’m trying to answer questions like:
* Which companies are structurally positioned for outsized upside in The Last Economy?
* How should I deliver the research so that it would have been actionable to someone like me 30 years ago?
* What signals would help folks identify “the next NVIDIA” 5 years earlier?
The inference costs real $$$ so I've set up a Patreon that, hopefully, will allow me to scale coverage and extend the modelling and methodology. There is a free tier and some recent, complete example output on the web site. I'm also happy to gift a free month for folks willing to provide constructive feedback: https://www.patreon.com/NextArcResearch/redeem/CC2A2 - in particular I'm looking for feedback on how to make the research more actionable without drifting into "financial advice".
I don't collect any data but Patreon does for authentication and Cloudflare does to deliver Pages. The Last Economy is here: https://ii.inc/web/the-last-economy
My approach is to invest for the long term, diversify a year's worth of living expenses into assets inversely correlated to tech, hold a diversified portfolio across different tech-oriented future scenarios and attempt to take a systematic, unemotional approach in domains that I understand and where I have expert knowledge. Tech and NVIDIA in particular have seen very significant draw-downs in the past and yet have been (in my experience) good investments since I started in 2008. Specifically, NVIDIA has seen extended draw-downs in the 60% to 90% range multiple times in the past. You just have to be (financially and emotionally) prepared for the ride and don't imagine you can time them. Anecdotally, Berkshire plays the safe haven / inversely correlated role for me though I've not proved it. There are always segments in tech doing well so I research beyond the Mag 7 and infrastructure. See bio for my research. [My opinion and not investment advice].
M.I.N.D. / "Last Economy"
The "Last Economy" framing comes from Emad Mostaque's book of the same name and is a way of thinking about where long-run value concentrates when intelligence becomes abundant. M.I.N.D. is the operationalization of that idea from the book and positioned as a better "yardstick" than current metrics like GDP or other traditional, scarcity-oriented financial metrics. For background on the broader thesis, Emad has written and spoken about it publicly here: https://ii.inc/web/the-last-economy. [It's a quick read for those familiar with the AI space and IMHO an important and relatively accessible read for anyone planning to live in the future].
At a high level he outlines:
- Material: control over scarce physical inputs that AI depends on (energy, fabs, supply chains, hardware)
- Intelligence: leverage over computation, models, or inference at scale
- Network: data, ecosystems, distribution, or flywheels that compound usage
- Diversification: exposure across multiple AI value paths rather than a single bet
The specific choice to multiply the dimensions (rather than add them) is also from his formulation: it encodes the assumption that missing one leg meaningfully caps long-run alignment. That assumption is very much up for debate, but the better an entity (country, company, person etc.) can score along the dimensions the better prepared they are for the Last Economy future governed by more physical than metabolic processes, and the ability to convert energy into computation.
I do want to stress that this chart is my interpretation, not an official formulation.
Valuation tension / expectation saturation I'm not trying to introduce a standard valuation metric here, and there isn't a single reference I'd point to. The idea is closer to a sentiment / expectation proxy than intrinsic value. Concretely, I'm asking: how optimistic does current pricing appear relative to a longer-horizon narrative based on how well a company may thrive or suffer in The Last Economy scenario? To keep it interpretable, I approximate that using:
- a relative long-term opportunity estimate (2030 horizon, directionally based on a creative, scenario driven process)
- divided by price position within the 52-week range as a proxy for how much optimism or skepticism is already expressed
It's intentionally blunt and debatable. I'm treating it as a secondary axis — useful for highlighting where narratives feel "fully priced" versus where they don't — not as a valuation model.
I realise there is a lot of context underlying my question. Thanks for your patience and interest.