The Economic Singularity – AI, Crypto, and the Death of Industrial Business Models by 2040

Economy, Society, Technology, Uncategorised |

Introduction: The Cracks in the Old Machine

I remember the grind of running a retail clothing business in the early 2000s—sourcing finished products from wholesalers who had already handled the upstream work, navigating their markups at every turn, and stocking shelves with trends that often missed the mark. It was a linear beast: goods arrived pre-made, yet the process felt fragmented, with delays in shipments and mismatches between what wholesalers offered and what consumers truly wanted—clothes that served not just utility, like warmth or durability, but deeper psychological needs for expression, status, or belonging. Back then, it felt efficient enough, a product of the industrial era’s assembly-line logic. But looking back, those chains were riddled with inefficiencies, relics of a system built for scale over precision.

To put this in context, the clothing journey typically begins with raw materials—natural fibres like cotton harvested from fields or synthetic ones like polyester derived from petroleum. These are spun into yarns, woven or knitted into fabrics, then dyed, printed, and finished for texture and colour. Manufacturers take over next: designing patterns, cutting fabric, sewing pieces into garments, and adding details like buttons or zippers. Once complete, wholesalers step in, buying in bulk, storing inventory, and distributing to retailers like my operation, who handle the final push—marketing, display, and sales to consumers. Each stage adds layers of cost, time, and potential waste, from overproduction to unsold stock.

Today, as we stand in 2025, technologies like artificial intelligence and cryptocurrency are exposing those cracks for what they are: fundamental flaws in a model that’s no longer sustainable. These aren’t mere tools to patch the old machine; they’re a horizontal force that slices across industries, dismantling vertical silos from the ground up. AI brings predictive intelligence and automation, while crypto enables seamless, trustless coordination through blockchain. Together, they don’t add efficiencies—they rewrite the rules, collapsing middlemen and transforming rigid supply chains into fluid, decentralised networks. What begins as incremental gains, like AI optimising inventory or crypto streamlining payments, escalates into a complete overhaul, where exponential growth outpaces linear legacies.

This shift points to what we can call an economic singularity: a tipping point where scarcity-based economics gives way to abundance-driven ecosystems. Linear models, rooted in industrial hierarchies, falter as networks amplify value through connections—think Metcalfe’s Law, where utility squares with every added participant. Accelerants like autonomous humanoid robots and the relentless evolution of compute power turbocharge this process. Robots physicalise AI’s agency, handling production and delivery with human-like dexterity but without fatigue, while compute advancements—doubling capacity every few months—compress timelines, enabling universal intelligence that redefines labour and value creation. By 2040, this convergence could end the debt cycles that prop up old systems, fostering flywheels of abundance where human behaviour, not capital constraints, drives demand.

In this essay, we’ll explore this transformation step by step: starting with why AI refuses to “fit” into industrial models, moving to the exponential logic of networks over chains, examining the role of robots and compute as catalysts, and finally navigating the implications for identity and agency. Along the way, we’ll balance what’s already unfolding—empirical trends in AI investments and crypto markets—with speculative visions of a post-singularity world.

Yet, amid this upheaval, a question lingers: in a reality where machines manage the churn of daily needs, what happens to the essence of human identity—the drive to define ourselves through what we create, consume, and connect?

Section 1: The Illusion of Additive Tech – Why AI Doesn’t “Fit” Industrial Models

Building on those cracks in the industrial machine we glimpsed in the introduction, let’s examine why emerging technologies like AI and crypto fail to simply enhance existing business models. The temptation is to view them as upgrades—tools that slot neatly into the familiar gears of production and distribution. But this is an illusion, one that masks a deeper rupture. These technologies demand a return to first principles, interrogating the very foundations of how value flows in an economy built for a different era.

Consider the traditional vertical business model, a staple of the industrial age. It stacks layers in sequence: raw inputs at the base, processing in the middle, and consumption at the top. In clothing, as we outlined earlier, this means fibres transformed into fabrics, then garments, before wholesalers and retailers bridge the gap to buyers. Each step relies on forecasts, inventories, and human oversight, introducing friction—overproduction leads to landfills, mismatched trends result in returns, and global supply lines amplify vulnerabilities like shipping delays or tariffs. The system prioritises scale, assuming scarcity in resources and labour, but it often falls short of meeting human needs precisely. Clothes aren’t just functional; they tap into psychological drives—signalling identity through style, evoking comfort for emotional security, or conferring status in social hierarchies. Yet, the linear chain dilutes this, turning personal expression into a game of approximation.

Now, imagine layering AI onto this setup. At first glance, it seems additive: algorithms predict demand more accurately, reducing stock waste by 20-30 percent in some retail trials, or chatbots handle customer queries to cut service costs. Crypto might streamline payments, enabling faster cross-border transactions without banks’ fees. Empirical trends back this—AI adoption in supply chains has grown 40 percent annually since 2020, while crypto’s transaction volumes in e-commerce hit hundreds of billions by mid-2025. But here’s the pivot: these gains are superficial, a prelude to breakdown. AI doesn’t conform to the vertical silo; it operates horizontally, infusing intelligence across the entire ecosystem. Agentic AI—systems that autonomously plan and execute tasks—doesn’t optimise the chain; it erodes it. In clothing, an AI agent could scan a user’s body via a smartphone, analyse their wardrobe data and mood from wearables, then orchestrate custom production directly from sustainable fabric sources, bypassing wholesalers entirely.

This horizontal force extends beyond retail. In healthcare, the vertical model funnels patients through diagnostics, treatments, and pharmacies, with paperwork and intermediaries inflating costs. AI could enable predictive diagnostics from personal data, while crypto tokenises health records for secure, peer-to-peer sharing—collapsing silos into networks where patients connect directly to specialists. Education follows suit: linear curricula delivered via institutions give way to AI-curated learning paths, with crypto verifying credentials in decentralised ledgers. These aren’t patches; they’re reconstructions, where the old model’s hierarchies dissolve into fluid connections.

At the heart of this lies a behavioural insight: human instincts—rooted in what we might call the limbic core, those primal drives for survival, connection, and meaning—have always shaped markets. Fear prompts hoarding, greed fuels speculation, and the quest for belonging influences trends. Industrial models tempered these through central control, but AI amplifies them exponentially. It anticipates desires in real time, turning psychological needs into precise fulfilment. Crypto complements this by enabling trustless incentives—tokenised rewards for participation, or smart contracts that automate royalties in creative industries like design. Together, they forge ecosystems where value accrues through networks, not layers.

Yet, the illusion persists because early implementations feel familiar. Companies pour billions into AI pilots—global investments topped 200 billion dollars in 2025 alone—expecting linear improvements. Speculatively, though, as agentic systems mature by 2030, the rewrite accelerates: abundance emerges as production costs plummet, and scarcity-based pricing crumbles. What if clothing becomes a service, with AI agents evolving outfits based on daily contexts, funded via micro-tokens? This isn’t efficiency; it’s a paradigm where human behaviour, untethered from industrial constraints, drives innovation.

Of course, not all verticals will vanish overnight—hybrids may linger as regulations catch up. But the trajectory is clear: additive thinking blinds us to the horizontal revolution. As we turn next to the exponential logic of networks over chains, we’ll see how this shift creates self-reinforcing loops that legacy models simply can’t match.

Section 2: Networks Over Chains – The Exponential Shift in Business Logic

Having exposed the illusion of treating AI and crypto as mere add-ons to industrial models, we now turn to the heart of the transformation: the move from linear chains to exponential networks. This isn’t just a tweak in operations; it’s a fundamental change in how businesses scale and create value, where connections replace hierarchies, and growth compounds in ways that leave old structures behind.

Linear models, as we’ve seen, build value additively—double the inputs, and you might double the outputs, but diminishing returns set in quickly. Factories produce more widgets, but costs rise with inventory, logistics, and market misreads. Networks, by contrast, thrive on exponential logic. Here, value multiplies through interconnections, following principles like Metcalfe’s Law: the utility of a system grows with the square of its participants. A phone network with two users is basic; with millions, it becomes indispensable. In business terms, this means ecosystems where users, producers, and data points link directly, amplifying efficiency and innovation without proportional cost increases.

The synergy between AI and crypto supercharges this shift. AI provides the intelligence—analysing vast datasets for patterns, personalising experiences, and automating decisions. Crypto, through blockchain, offers composability: modular building blocks like smart contracts that allow seamless integration without central gatekeepers. On platforms like Solana or Sui, these contracts execute automatically, handling everything from payments to royalties with minimal fees. Empirical evidence underscores the momentum—decentralised finance protocols, a prime example of this fusion, locked over 160 billion dollars in value by mid-2025, up sharply from prior quarters as users flocked to yield-generating networks. Meanwhile, AI-crypto projects have seen market caps swell into the hundreds of billions, driven by tokens that incentivise data sharing or computational contributions.

Take clothing as our ongoing example. In a network model, the agentic AI we discussed evolves the user-to-production loop into a web of interactions. A consumer’s preferences—drawn from social feeds, biometric data, and past behaviours—feed into a decentralised marketplace. AI designs the garment, crypto tokens secure sustainable fabric sourcing via transparent ledgers, and peer producers bid to manufacture it locally. No central wholesaler dictates terms; instead, the network self-organises, with participants earning fractions of value through micro-transactions. This interconnects utility (fit and function) with psychological fulfilment (custom styles that resonate with identity), turning consumption into a collaborative act.

The pattern repeats across verticals. In energy, linear grids—centralised plants distributing power through utilities—give way to peer-to-peer networks. AI optimises solar panel outputs based on weather and usage patterns, while crypto enables households to trade excess energy directly, tokenising kilowatts for instant settlements. Early implementations in regions like Europe have shown 20-30 percent efficiency gains, with blockchain reducing fraud in micro-grids. Finance undergoes a similar overhaul: traditional banks, with their layered approvals and fees, face DeFi platforms where AI assesses credit risks in seconds, and crypto lends assets globally without intermediaries. By 2025, these networks have facilitated trillions in transactions, outstripping some legacy systems in speed and accessibility.

At a deeper level, this exponential shift fosters abundance flywheels—self-reinforcing cycles where lowered barriers spark more participation, driving down costs and unlocking new value. Debt-based growth, a hallmark of industrial economics, fades as tokenised assets circulate freely. Identity becomes central: proof-of-personhood mechanisms, powered by blockchain, could underpin economies where human engagement—creativity, curation, or connection—earns rewards. Behavioural drives amplify here; the limbic pull toward belonging draws users into communities, while AI nudges toward optimal choices, creating loops that grow organically.

Speculatively, by 2030, these networks could dominate, with vertical incumbents either integrating or eroding. Imagine a world where education networks tokenise skills, allowing learners to build personalised paths funded by community stakes, or healthcare ecosystems where patient data fuels AI-driven cures, shared equitably via crypto royalties. The inference is bold: scarcity yields to plenty, but only if networks prioritise inclusion over extraction.

Yet, this logic alone doesn’t capture the full acceleration. As we explore next, humanoid robots and compute advancements act as catalysts, physicalising and scaling these networks toward the singularity’s edge.

Section 3: Accelerants to the Singularity – Robots, Compute, and the Tipping Point

With networks reshaping the exponential logic of business, as detailed in the prior section, the transformation gains even more velocity through key accelerants: autonomous humanoid robots and the rapid evolution of compute power. These elements don’t merely support the shift; they propel it, physicalising digital intelligence and compressing the timelines for abundance, pushing us closer to the economic singularity where human roles in value creation fundamentally change.

Autonomous humanoid robots represent the embodiment of agentic AI, extending its reach from software into the physical realm. Unlike specialised machines locked into single tasks, these robots mimic human form and adaptability—bipedal designs with dexterous hands, vision systems, and learning algorithms that allow them to navigate dynamic environments. They handle complex actions like grasping irregular objects or collaborating in teams, drawing from vast training data to improve over time. In practical terms, this means robots can execute the fluid loops of networked ecosystems we explored earlier. For instance, in clothing production, a humanoid could assemble custom garments on demand, adjusting fabrics in real time based on AI designs, then package and deliver them without human intervention. Empirical developments highlight the pace: the global humanoid robot market is estimated at around 7.8 billion dollars in 2025, with prototypes from companies like Boston Dynamics and Figure AI entering early deployments in warehouses and homes. Tesla’s Optimus, for example, aims to place thousands of units in factories by year’s end, despite recent redesign adjustments to enhance reliability.

This physical agency interconnects with behavioural themes: robots free humans from repetitive churn—the grind of assembly or logistics—allowing focus on higher-order drives like creativity and connection. In healthcare networks, humanoids could assist in personalised care, fetching supplies or monitoring patients, while in education, they might facilitate interactive simulations. The result is an amplification of network effects; as robots integrate into decentralised systems, they reduce costs further, fostering abundance flywheels where production scales without proportional labour inputs.

Equally critical is the evolution of compute power, the underlying engine that fuels AI’s intelligence. Compute refers to the processing capacity—measured in operations per second or training flops—that enables models to handle increasingly complex tasks. Trends show exponential growth: training compute for leading AI systems doubles roughly every five months, driven by advancements in chip architectures and data centre expansions. By 2025, industry controls about 80 percent of global AI compute resources, up from 40 percent six years prior, as hyperscalers like Google and Amazon pour trillions into infrastructure. Gartner forecasts that half of cloud compute will dedicate to AI workloads by 2029, reflecting surges in demand from generative tools and agentic systems. This isn’t abstract; it manifests in hybrid setups blending classical processors with neuromorphic designs that mimic brain efficiency, slashing energy use while boosting speed.

In our clothing example, enhanced compute allows AI agents to process multimodal data—images, biometrics, and social trends—in seconds, enabling hyper-personalised designs that align with psychological needs for identity expression. Across verticals, it compresses innovation cycles: energy networks optimise grids in real time, finance models predict market shifts with unprecedented accuracy. Behavioural instincts tie in here too—the limbic drive for efficiency and reward finds expression in systems that anticipate human wants, turning speculation into seamless fulfilment.

Together, these accelerants converge with crypto’s composability, creating tokenised ecosystems where robots operate as networked assets. A humanoid might earn tokens for tasks, redistributed via blockchain, reinforcing exponential loops. Speculatively, by 2030, this could tip us into the singularity: labour scarcity vanishes, debt models collapse, and abundance becomes baseline. Yet, challenges loom—energy demands from compute could strain grids, and robot integration might displace roles unevenly, testing social cohesion.

As we move to the final section, navigating this singularity demands strategies that preserve human agency amid the upheaval, ensuring the rewrite serves our core drives rather than overshadowing them.

Section 4: Navigating the Singularity – Identity, Agency, and Human Thriving

As the accelerants of robots and compute push us toward the economic singularity outlined in the previous section, the question shifts from what happens to how we steer it. This tipping point promises abundance, but it also surfaces risks that could undermine human essence. By focusing on identity and agency—the core drivers of our behaviour—we can chart paths that turn disruption into thriving, ensuring the rewrite enhances connection rather than eroding it.

The singularity’s risks stem from its speed and scale. Inequality could widen as networks concentrate wealth among early adopters; those with access to AI tools or crypto liquidity thrive, while others lag. Empirical patterns show this already: in 2025, the top 1 percent of crypto holders control over 40 percent of Bitcoin’s supply, mirroring broader wealth gaps amplified by tech. Cohesion crises loom too—displacement in labour markets hits hardest in developing regions, where automation replaces manual jobs without retraining infrastructure. Psychologically, abundance might hollow out identity; when needs are met effortlessly, the drive for meaning—rooted in striving and scarcity—could falter, leading to voids where consumerism once filled gaps. Behavioural science points to this: studies on universal basic income pilots reveal mixed outcomes, with some participants gaining freedom but others struggling with purpose.

Yet, these challenges interconnect with opportunities if we leverage human fundamentals. Identity, as a psychological anchor, becomes the compass. In networked ecosystems, AI can nudge toward self-actualisation—personalised experiences that align with limbic drives like belonging or achievement. For instance, in clothing, an agentic system might evolve outfits not just for fit but for emotional resonance, drawing from user data to reinforce personal narratives. Blockchain ensures equitable access: tokenised identities, via proof-of-personhood protocols, could distribute resources fairly, preventing extraction by central players. Early examples exist—decentralised autonomous organisations on platforms like Ethereum have governed billions in assets through community votes, fostering inclusion.

Agency emerges as the safeguard, empowering individuals to direct the flow. Education transforms into “machinery” for this: AI-driven platforms teach generalism—adaptable skills like systems thinking or creative synthesis—preparing people to orchestrate networks rather than labour within them. By 2025, online learning tools have reached hundreds of millions, with adaptive curricula boosting retention by 30-50 percent in trials. Speculatively, by 2035, humanoid robots could serve as mentors, physicalising learning in real-world scenarios, while compute enables lifelong digital twins that simulate career paths. This interconnects with behavioural insights: agency satisfies the limbic quest for control, turning potential alienation into empowerment.

Practically, investment plays ground this vision. Positions in convergence tech—like Tesla for robotic integration or Solana for high-throughput liquidity—capitalise on the shift, where tokenisation arbitrages value across networks. For individuals, steps include building in ecosystems: tokenise skills on blockchain marketplaces, contribute to open-source AI projects, or form peer groups for shared compute resources. These actions democratise abundance, ensuring psychological fulfilment through active participation.

Visionary strategies extend further. Imagine blockchain aiding displaced communities with micro-economies—tokenised aid for third-world regions, where robots handle infrastructure while humans focus on cultural preservation. Identity markets could flourish: users trade digital selves for custom experiences, with AI safeguarding privacy. The inference here is optimistic: if governed thoughtfully, the singularity fosters a world where human thriving stems from connection, not competition—abundance flywheels that prioritise meaning over material.

This navigation isn’t guaranteed; it demands proactive choices. As we conclude, the economic singularity stands as a dawn, not a dusk, rewriting systems to serve our deepest drives in ways the industrial era never could.

Conclusion: The Dawn of a New Economic Order

Having charted paths to navigate the singularity in the prior section, we now stand at the threshold of what this all means—a profound rewrite of the economic order, where industrial relics give way to a landscape shaped by human potential.

At its core, the thesis we’ve unpacked holds firm: AI and crypto, as horizontal forces, dismantle vertical business models not through addition but reinvention, collapsing chains into networks that scale exponentially. We’ve seen this in clothing’s evolution from linear supply to agentic fulfilment, where utility meets psychological depth without intermediaries. Networks amplify this logic, fostering abundance flywheels that interconnect behavioural drives—fear, greed, belonging—with decentralised value flows via smart contracts and tokenisation. Accelerants like humanoid robots and surging compute power compress the timeline, physicalising intelligence to end labour churn and enable universal access.

Empirical realities ground this: AI investments exceed 200 billion dollars annually in 2025, crypto markets handle trillions in transactions, and robot deployments in factories signal a shift already underway. These trends interconnect with identity: as scarcity fades, clothes or services become extensions of self, curated by AI to reinforce agency. Yet, the singularity’s dawn isn’t uniform—cohesion risks persist, demanding inclusive networks to bridge divides.

Speculatively, by 2040, this order could realise a world of thriving: education as adaptive generalism, healthcare through peer data shares, energy via communal grids—all tokenised for equitable participation. Human behaviour reigns here, with limbic cores directing innovation in ecosystems that prioritise connection over control. Debt cycles dissolve, replaced by flywheels where meaning derives from creation and collaboration, not consumption alone.

The call is clear: embrace first principles now—build in networks, tokenise contributions, invest in convergence plays like robotic platforms or high-throughput blockchains. This isn’t passive evolution; it’s an active forge, where agency turns upheaval into opportunity.

In the end, the economic singularity marks a new dawn: one where systems serve identity, unlocking potentials the industrial era confined. Amid networks and machines, humanity’s drive to define, connect, and thrive endures as the ultimate order.

Disclaimer

This essay explores speculative ideas on technology, economics, and human behaviour for informational and educational purposes only. It does not constitute financial, investment, or professional advice. Any references to specific assets, such as Tesla, Solana, or Sui, are illustrative examples drawn from broader trends and should not be interpreted as recommendations to buy, sell, or hold. Market conditions are volatile and unpredictable; always consult qualified financial advisors and conduct your own research before making decisions. The author holds no liability for actions taken based on this content.

Facts vs. Inference: Facts—investment examples are based on publicly observed trends in 2025 markets. Inference—outcomes of such positions remain speculative and subject to risks like regulatory changes or technological shifts.

References

This reference section lists sources for the empirical facts mentioned throughout the essay, organized by section for clarity. All links were verified as active and relevant as of September 09, 2025. Where facts are approximations based on trends (e.g., mid-year projections), I’ve noted them as such. Speculative visions in the essay are not referenced here, as they fall under inference.

Section 1: The Illusion of Additive Tech – Why AI Doesn’t “Fit” Industrial Models

Section 2: Networks Over Chains – The Exponential Shift in Business Logic

Section 3: Accelerants to the Singularity – Robots, Compute, and the Tipping Point

Section 4: Navigating the Singularity – Identity, Agency, and Human Thriving

Conclusion: The Dawn of a New Economic Order