Today’s advances in AI and decentralized data are opening the door to new kinds of markets that actively reward people for socially beneficial behaviour. The disappearance of traditional jobs in the face of automation has sparked proposals like Universal Basic Income (UBI), but simply paying everyone a flat income may be an inadequate solution in a post-work world. UBI addresses material needs yet ignores the human need for purpose. Instead, “Markets with a Social Purpose” could offer a more humane alternative: leveraging personal data and AI to create market incentives for virtuous activities — learning, caregiving, community service — that enrich society. In this vision, individuals are rewarded not for economic labour (which machines increasingly handle) but for “Virtue Labor” that contributes to personal and social well-being. Before exploring how such markets might work, we first consider why the era of conventional economic labour is drawing to a close and why a UBI-only approach could lead to a bleak, Hobbesian social landscape.

The End of Economic Labour

AI driven automation will rapidly outperform humans in sector after sector, heralding the end of most traditional “economic labour” in the coming decades. Advances in robotics and AI mean machines can work longer, faster, and cheaper than people for an ever-growing array of tasks. Factories already use robotic arms to palletize goods and run assembly lines around the clock, without coffee breaks or sick days. Self-driving vehicles, AI customer service agents, and algorithmic stock traders are not science fiction but emerging reality. Research backs this inexorable trend: a landmark Oxford study estimated that roughly 30% of jobs could be technically automated by 2030 , and AI expert Kai-Fu Lee has predicted 40% of the world’s jobs may be displaced within 15 years . Crucially, these aren’t just manual or low-skill jobs — AI is encroaching on white-collar work as well. What remains for human workers? Perhaps only roles that demand irreducibly human qualities, like genuine creativity, emotional empathy, or spiritual guidance might endure. A machine might write passable news articles or diagnose illnesses, but being an inspiring religious leader or an avant-garde artist taps into depths of human experience that automation can’t easily replicate (at least for now). Apart from those niches, however, no occupation is truly safe.

The economic logic driving automation is simple: once AI and robots can do a job better and at lower cost, businesses will adopt them. The past promise that technology creates new jobs to replace the old is wearing thin — AI itself can spawn new industries and then run them with minimal human input. Automation, unlike humans, can be copied and distributed globally with little infrastructure cost, accelerating its spread. Just as mechanization once outpaced horse-drawn ploughs, AI-driven machines are poised to outperform human labour across the board, from driving trucks to analysing legal contracts, with superhuman efficiency. This economic endgame — machines doing virtually all production and services — might fully materialize by mid-century or sooner, according to many technologists. It forces us to ask: if income and wealth are generated mostly by machines, how will humans sustain themselves not just materially but psychologically? UBI is one popular answer, but a world that simply pays people to be idle could be fraught with new problems.

The Hobbesian World of UBI

UBI, the idea of providing every citizen a no-strings-attached basic income, has gained traction as a remedy for mass joblessness. On the surface, it’s elegant: as AI and robots produce abundant wealth, that wealth can be taxed and shared to enable everyone’s needs met, even if they have no formal job. Several countries have trialled UBI on a small scale, and proponents argue it could eliminate poverty and give people freedom to pursue their interests. Yet a future where paying people to do nothing is the norm raises profound psychological and social concerns. Human beings are not wired to be content in idle comfort; purposeless affluence can breed discontent. Thomas Hobbes famously described life without social order as “nasty, brutish, and short.” By analogy, a society where millions sit idle on a stipend could become a Hobbesian dystopia of alienation and unrest despite material plenty.

Psychological research shows that work is about far more than a paycheck. Employment provides structure, social connection, and a sense of purpose that are hard to replace. Even when money isn’t an issue, the loss of meaningful activity can erode mental health. Studies find that unemployed individuals suffer not only from lack of income but from the absence of routine, community, and status that work had given them. Being indefinitely on “vacation” sounds utopian until one considers the toll of aimlessness: depression, anxiety, and a loss of identity. In one striking meta-analysis, social psychologists Marie Jahoda and others confirmed that people without employment report significantly lower well-being on measures like self-worth and social engagement. In short, being needed and being useful are psychological needs. A society that meets material needs while neglecting those deeper needs risks fraying the social fabric. We derive self-esteem and social status from contributing to something beyond ourselves; UBI, if not paired with opportunities for contribution, could leave people feeling both dependent and pointless. Additionally, simply handing out income could engender resentment or complacency — some might game the system, while others seethe that they’re working while neighbours seemingly loaf. The danger is a polarized world of listless beneficiaries and disaffected holdouts, hardly the cohesive community one would hope for. This suggests that post-labour policy must address human purpose and dignity, not just cash transfers.

“Economic Labour” vs “Virtue Labour”

If we accept that in the future economic labour (the work done for producing goods and services in exchange for wages) will be largely automated, what remains is the realm of “virtue labour.” This encompasses all those valuable activities that aren’t done for a paycheck, but for personal growth or community benefit: caring for an aging parent, mentoring youth, volunteering at a shelter, studying new skills, creating art, participating in civic life. Historically, markets and GDP measurements have mostly ignored such work. If you quit a salaried job to care for a sick relative, the economy registers a loss of productivity, even though you may be doing something profoundly important. A mother reading to her children or a neighbour tending a community garden doesn’t count in financial ledgers. Market forces reward activities that generate profit, not necessarily those that generate value or virtue.

The fundamental difference between economic and virtue labour lies in their incentives and visibility. Economic labour is guided by prices, contracts, and competition; it has clear outputs that can be bought and sold. Virtue labour, by contrast, is intrinsically motivated or socially motivated, and its “output” is often intangible or long-term (a well-raised child, a cohesive neighbourhood, a wiser populace). Because traditional markets struggle to value what they can’t price, virtue labour has been systematically undervalued. Consider caregiving: the International Labour Organization estimated that unpaid care and domestic work would equal roughly 9% of global GDP (about $11 trillion) if it were paid. That staggering contribution is largely invisible to markets. Similarly, educating oneself or maintaining one’s health produces benefits (higher knowledge, lower healthcare costs) that accrue to society over time, but there’s no immediate market driven paycheck for hitting the gym or reading a book. The result is a kind of market failure: we incentivize people to flip burgers for pay rather than cook healthy meals for their family, or to work overtime at a company while neglecting community ties, because only the former activities yield monetary reward. Our economies thus routinely under-provision things like caregiving, community building, and lifelong learning — the virtues that make a good society. To redesign a post-work economy, we must find ways to measure and reward virtue labour without stripping it of its intrinsic meaning. That is where digital technology and new market mechanisms promise something revolutionary.

Evolution of Our Digital Technology

Transformative change in how we value virtue labour is becoming possible thanks to the explosion of personal data and AI capabilities in recent years. We live in an age where nearly everyone carries a smartphone or wearable device, continuously generating data about their activities, health, learning, and social interactions. This ubiquity of personal digital devices means that much of what was once invisible (like how far you walk each day, or how you spend your free time) can now be recorded. Initially, much of this data flowed into centralized tech platforms — big companies amassed profiles on our behaviour to sell ads or improve services. But the tide is turning. New privacy and digital competition laws such as Europe’s GDPR, DMA, DSA, and Data Governance Act give individuals the ability to access and control their personal data generated by tech companies and their connected devices, such as phones, wearables, and home smart devices. This allows individuals to aggregate their data in private data spaces, known as “personal data stores,” which only they can access and through which they can manage the sharing of their data with third parties. In this model, a person might have a personal data vault containing health records, education history, volunteering hours, etc., choosing which data to share or have analysed. Concurrently, AI is evolving to become decentralized and privacy-focused, driven by the need to gain users’ trust and obtain permission for AI models to access personal data necessary to understand user context and deliver the best services. For example, Apple emphasizes “on-device” AI for its personal intelligence strategy, enabling iPhones to offer smart features without uploading all data to the cloud. This technological trend points toward the future of personal AIs acting as digital assistants that work with your data, understand your context, and provide assistance while maintaining data privacy and security.

In this not-so-distant scenario, individuals will have a personal AI that knows their goals (say, to learn French, or to stay fit, or to care for your grandmother better) and that monitors relevant data streams in your personal vault (your practice time, your biometrics, your calendar of caregiving tasks). This AI would act like a 24/7 personal mentor, nudging you toward beneficial habits and verifying your efforts and achievements. Because data can be kept locally even very sensitive activities (mental health journaling, for instance) could be leveraged by AI without that data ever being exposed to a tech giant or advertiser. As personal data becomes decentralized and under the user’s control, and as AI becomes more context-aware and privacy-preserving, the stage is set for entirely new kinds of services — and notably, new markets. Where traditional markets trade in goods and services, these new markets could trade in verified outcomes and social good, all enabled by trustworthy personal data and AI verification.

Markets with a Social Purpose

Imagine a marketplace where the “product” being bought and sold is a positive social outcome — education, health, environmental stewardship — verified by data and AI. Traditional consumer markets pass only minimal information between participants: when you buy a loaf of bread, you mostly just exchange money for the bread, with price and brand as the main signal of value. Little else is communicated about how that bread was made or the broader impact of the transaction. In contrast, Markets With A Social Purpose would leverage rich data to ensure that each transaction rewards a virtuous achievement by someone. We already see glimmers of such markets. Take carbon credits: here, the goal is to reduce greenhouse emissions, and the “product” is one tonne of CO₂ not emitted or removed. To trade a carbon credit, you need reliable data proving that reduction happened (through sensors, audits, satellite imagery of forests, etc.). This market has sprung into existence precisely because society decided emissions reduction is valuable and measurable — we put a price on a ton of saved carbon and created a tradeable unit. The global carbon offset market, driven by data and verification, grew to an estimated $1.9 billion in value in 2022. Each credit encapsulates information about projects (planting trees, installing clean energy) that go well beyond a simple buy/sell of fuel; it is a data-rich transaction in service of a social good (a safer climate).

Now extend that idea to individual actions. With personal AI and data tracking, it becomes feasible to measure a person’s contributions or achievements in various virtuous domains, without sharing the underlying data upon which the measurement is based. For example, consider an education market: students could earn certified tokens or credits for mastering new skills or completing courses, which could then be redeemed for income or opportunities. These tokens would be backed by verified data — perhaps your personal AI proctor confirms you truly learned the material and passed the exams. Similarly, a health market might reward people for healthy behaviour (steps walked, medical adherence), converting those verified habits into something of economic value. There are already apps hinting at this future: fitness apps that give you points or crypto tokens for daily steps, or platforms that reward users for sharing health data for research. The difference in a fully realized virtue market is the scale and reliability of the data gives orders of magnitude greater flexibility and reach. Almost any behaviour can be measured and rewarded with almost no administration cost to operate the new market. If everyone has a personal data vault and AI, cheating becomes difficult — your AI can cryptographically prove you did spend 100 hours caring for an elderly neighbour (via location data, check-ins, and the positive impact seen in the elderly persons data store etc.), so you earn a caregiving credit that might translate into monetary stipend or other benefits. Crypto economics could play a key role here: blockchain technology allows the creation of tokens and smart contracts that automatically distribute rewards when conditions are met. One could imagine decentralized autonomous organizations (DAOs) that distribute machine-generated wealth (from robotic factories or AI-driven financial markets) and then channel those profits to reward people who engage in socially valuable activities. In essence, the profits of the AI-run economy would fund a new social contract: instead of wages for economic labor, dividends for virtue labor. These markets with a social purpose would still be markets — driven by voluntary action, competition, and innovation — but the “currency” they trade is social impact, and the payout is tied to personal achievement of democratically chosen goals.

What’s the Good Life?

All these possibilities beg the question: who decides what counts as virtuous behaviour worthy of reward? The “good life” is a concept philosophers have debated for millennia, and there is no one-size-fits-all answer. In a diverse society, we must democratically define the virtues and outcomes that such markets reward. This could be achieved through new forms of hyper-democracy and expert input, within frameworks that protect our pluralism — much like how the United Nations agreed on Sustainable Development Goals as a broad set of societal aims. Different communities might prioritize different virtue markets; one region could reward environmental cleanup efforts; another might focus on arts and cultural creation. The key is that it cannot be a top-down imposition of values. To maintain the liberal pillars of choice and freedom, people must have a say in setting the targets and must retain the freedom to pursue their own vision of the good life (within the broad rules that society sets). Free markets, for all their flaws, have been engines of innovation and personal freedom in our open democracies. In replacing or augmenting them with social-purpose markets, we should be careful not to lose those benefits. These new markets should harness market dynamics to support individuals to choose virtuous pursuits that suit them, by society incentivising a wide range of activities aligned to the common good.

We are contemplating a future where traditional economic jobs, largely, fade into history. This also has profound implications for the idea of capital ownership of the enterprises which form the backbone of economic production. In the future AI will also dominate innovation and investment choices — as it will simply become better at those tasks than humans. Gone will be the days when investors rightly earned the reward of their risk taking — as they won’t have made the decision and won’t understand why the investment decision was made. Their dividend returns will become unearned. No society is likely to permit a tiny fraction of its members receiving vast incomes by accident of their ancestors’ actions when the individuals now perform no economically useful role. If AI and machines generate most wealth, the old idea that shareholders (individual people) privately own all production may give way to new models of shared ownership. This isn’t a political perspective, but rather just the logical consequence of the evaporation of jobs and economic labour driven by the new technologies we are currently birthing.

What might life look like in this post-economic-labour future?

Markets with a social purpose would make it normal to earn one’s livelihood by improving oneself and one’s community. The teacher and the volunteer could be as valued as the banker once was. Of course, this future raises as many questions as it answers. It challenges us to rethink education, welfare, and even the meaning of profit. It also carries risks: if not implemented carefully, such systems could be gamed or could slip into dystopian social engineering. But done right, they promise a synthesis of freedom and fraternity — using free-market style incentives to nurture, rather than erode, our social bonds and individual purpose.

The end of economic labour need not mean a collapse into aimlessness relieved only by a stipend. Instead, it can herald the beginning of a new incentive structure for humanity. As we stand on the brink of this transformation, we briefly have the opportunity to shape our future. We have a chance to design new types of market that leverage democracy, psychology, culture and our new digital technologies as a means to achieve whatever we collectively believe to be “the good life” and drive our societies toward that goal. The challenge now is to seize that opportunity, before the old systems fade, and ensure that the next stage of capitalism becomes not a Hobbesian nightmare but a renaissance of human thriving. For we are at the integrum. The old order has died but is not yet dead. The new order is born, but not yet alive. We stand between two eras, with the future in our hands.

Navigating the New Frontier of Agentic AI

In the not-so-distant future, the world of business is set to experience a seismic shift that will redefine competition, customer engagement, and ultimately, corporate survival. At the heart of this transformation is agentic AI — a breed of artificial intelligence that promises to revolutionize everyday life by acting as our personal digital assistants, our “gofers” in the truest sense. This is not the stuff of science fiction; it is an emerging reality that is already making headlines and promising to simplify life in ways that were once the realm of our wildest dreams.

Picture the scenario: You are preparing for an important business trip overseas. Your agentic AI, which has been learning your preferences for years, springs into action. It begins by analysing your calendar, identifying potential gaps, and cross-referencing your frequent flyer mileage, dietary restrictions, and hotel preferences. Within minutes, the AI has not only booked a direct flight to Paris but has also secured a suite in a boutique hotel renowned for its impeccable service. The system then coordinates with local partners to arrange dinner reservations at a hidden gem of a restaurant that serves authentic French cuisine, and it even schedules a taxi to pick you up the moment your flight touches down. This picture of a near-future reality underscores the transformative power of agentic AI, one that goes far beyond digital assistants proffering advice. It becomes an integral extension of the self — managing mundane tasks while curating personalised experiences.

Yet, as agentic AI integrates more deeply into our daily lives, it forces a dramatic rethinking of business strategy. Traditional models of competition, built around reaching individual consumers through targeted marketing and emotional branding, are about to be disrupted. In a world where consumer decisions are mediated not by human whims but by sophisticated algorithms, companies will increasingly be interacting not with the consumers themselves, but with the consumers’ AI. This shift calls for a re-evaluation of corporate strategies in the wake of a new form of market negotiation — one that occurs between intelligent digital entities.

Businesses have three potential strategies to adapt to this brave new world of AI-mediated commerce. The first strategy banks on the belief that the end-user will continue to make the final purchasing decision. In this approach, companies maintain their current product presentations and marketing messages, assuming that the consumer will see the emotional appeal of their offerings. However, as the role of agentic AI expands, this assumption becomes increasingly precarious. There is a growing risk that an individual’s AI might filter out a corporate offer entirely before it even reaches the consumer.

The second strategy posits that the consumer’s AI will be armed with a detailed profile of the user’s preferences and requirements. With this in mind, companies might choose to tailor their messaging to make it easy to map their claims to the customer’s predefined preferences. In essence, they would be offering a clear signal to the customer’s AI about what type of use cases their products and services are designed for. For example, a car manufacturer could prominently display the car’s performance and financial and environmental characteristics in a standardised way and demonstrate that independent auditors verify the claims are true. While this approach makes it easier for the AI to categorise and match products with consumer desires, it ultimately leaves the heavy lifting of matching these preferences entirely in the hands of the consumer’s AI. The corporate message, though clear, remains passive — a static piece of information waiting to be picked up by an algorithm rather than actively engaging in a dialogue that could lead to a sale.

The third strategy, which may hold the greatest promise, involves a dynamic negotiation between the corporate’s AI and the consumer’s AI. Here, companies would deploy their own AI systems to engage directly with the consumer’s digital agent, initiating an interactive process of discovery and negotiation. Instead of simply broadcasting a one-way message, the corporate AI would probe the consumer’s AI for more granular details about the customer’s needs, priorities, and even latent desires. Imagine you owned a high-end jewellery store and were engaged by a customer’s agentic AI, which signalled an interest in purchasing a diamond ring. Instead of simply displaying the product and hoping for the best, your store’s AI could initiate a conversation, inquiring about the context behind the search. Through this digital discussion, your AI might discover that the ring in question is not just a piece of jewellery but is intended to be a wedding ring. With this additional insight, the jewellery store’s AI is now positioned not merely to sell a ring but to offer additional products and services through partnerships with other providers, such as wedding planning, venue suggestions, catering and bespoke honeymoon packages. By engaging directly with the consumer’s AI, the corporate AI transforms a simple product inquiry into an opportunity to build a broader ecosystem of services — a move that opens new cross-selling opportunities and the potential to grow the Jewellery store’s profit.

Each of these strategies presents its own strengths and vulnerabilities. The first strategy is grounded in traditional marketing wisdom but risks obsolescence if the consumer’s AI begins to act as a gatekeeper, filtering out offers before the human consumer even has a chance to weigh in. The second strategy is a step forward — it ensures that a company’s offerings are clearly defined in a language that the consumer’s AI can understand and trust. Yet, it is passive and relinquishes a certain degree of corporate agency by allowing the consumer’s AI to be the primary driver in matching preferences. In contrast, the third strategy actively engages in a negotiation process, enabling the corporate AI access to valuable insights about customer needs and priorities. In the cutthroat era of agentic AI, it is not enough to simply be seen by the customer’s AI; one must actively win its trust and secure its confidence.

Trust becomes the linchpin in this new ecosystem. For a corporate AI to successfully engage in negotiations and obtain meaningful information from a consumer’s AI, it must first earn the consumer’s trust. In practical terms, this means that the consumer’s AI must be confident that any shared data will be handled responsibly and ethically. The sharing of personal data raises consumer concerns over privacy and security and in many jurisdictions will require specific and informed consent. To navigate these waters, a growing array of “consent engines” will likely emerge. These technological tools may evolve from today’s simple consent dashboards to rule-based systems and then to more sophisticated risk-based “consent engines” which take into account a corporate’s reputation, data breach history and rigour of the regulatory regime where the corporate is located. The outcome of such an assessment would directly influence how much and what type of data the consumer’s AI is prepared to share. In this emerging environment, trust will no longer solely be the concern of compliance departments and corporate tree huggers; it will also be critical to the sales and marketing departments.

This dynamic sets the stage for a profound bifurcation in the corporate world. Companies that manage to earn and maintain the trust of consumers — and by extension, the consumer’s AI — will gain a competitive advantage. These trusted brands will be able to access richer customer data, providing them with a deeper understanding of the customer’s needs and preferences. The data exchange during an AI-to-AI negotiation could reveal latent needs that open up entirely new revenue streams. Conversely, companies that fail to build the requisite trust will find themselves relegated to the market’s periphery. These distrusted brands will see an ever-higher percentage of their sales being made via more trusted partner brands, ultimately diminishing their customer relationships and ruthlessly driving their commoditisation. Over time, the market will likely witness a clear bifurcation: on one side, there will be a cadre of trusted corporates that benefit from rich data flows and can actively negotiate and shape customer outcomes; on the other side, there will be a host of distrusted brands who are relegated to a supplier role, and shorn of their customer insight and relationships.

This bifurcation will inevitably lead to a radical restructuring of consumer markets. Over the coming decade, we can expect to see entirely new winners and losers emerge. The victors will be those companies that successfully build trust with consumers, effectively harnessing the data shared by agentic AI to create ever-expanding ecosystems of products and services. In many ways, this scenario is reminiscent of the early days of the Internet. Back then, the rise of big-tech companies was fuelled by their ability to leverage new data flows and expand their business models beyond their original core services. These companies moved from being simple providers of information to becoming integral parts of multiple interconnected verticals — from advertising to e-commerce, from cloud computing to entertainment. Agentic AI is poised to trigger a similar wave of transformation across all consumer sectors, from retail and hospitality to healthcare and finance. The companies that manage to use the new data flows to weave intricate networks of services will be the ones that thrive in the new AI-driven landscape, much like the tech giants that rose to prominence by capitalising on the data revolution in the early 21st century.

For CEOs and corporate strategy departments, the advent of agentic AI represents both an unprecedented opportunity and a formidable challenge. The traditional levers of competition — brand, quality, and customer service — will need to be augmented by a new focus on data trustworthiness and AI negotiation capabilities. Corporate leaders must now reconsider investments in their traditional martech stacks and refocus on corporate AI systems that can interact with consumer agents, while building robust data governance frameworks that reassure customers about the corporate’s ethics and data handling competence. The rise of agentic AI heralds what may well be an extinction event for those corporations that fail to adapt to this new paradigm. The companies that continue to rely on outdated models of direct human engagement risk being left behind as consumer decisions become increasingly the purview of intelligent digital agents. By proactively embracing strategies that involve dynamic AI-to-AI negotiations, forward-thinking companies can not only survive but thrive. The corporate extinction event is not a distant threat — it is a clarion call to innovate, adapt, and embrace the future of business in an era defined by the rise of agentic AI.

Artificial intelligence is reshaping how enterprises operate and, in the not-too-distant future, may challenge long-held beliefs about corporations’ customer relationships. Large organisations are pouring resources into AI-based solutions, reflecting the high-value executives place on data-driven insights, automation, and personalised experiences. From algorithms that optimise ad delivery to generative models that create deeply targeted content, AI-driven marketing technologies have become essential for businesses intent on growing their customer base and improving loyalty. Modern marketers deploy these tools at scale, weaving together chatbots, predictive analytics, and personalisation engines into a tightly integrated Martech stack that helps them fine-tune campaigns and boost conversion rates. Yet, a crucial question looms: are corporate leaders truly unlocking the disruptive power of AI, or merely grafting new systems onto old ways of doing business to chase short-term gains?

This question recalls a moment in history when the internal combustion engine was first invented. Some water-powered factories still relied on rivers that occasionally ran dry, so they used these new engines only to pump water back into reservoirs and keep water wheels turning. They never fully recognised that these engines could liberate their factories from depending on rivers altogether. This analogy highlights the danger of viewing AI as just another tool to preserve existing infrastructure. Many enterprises use machine learning to refine their marketing funnels or improve customer segmentation without questioning whether it simply prolongs outdated practices. Meanwhile, outside the walls of large corporations, entirely new AI paradigms are emerging that may ultimately eclipse today’s incremental strategies.

A powerful new wave of consumer-oriented AI now promises to upend traditional enterprise-customer relationships. Personal devices that people use every day are evolving into powerful localised engines of intelligence that empower individuals to take greater control over their data, interactions, and transactions. Apple Intelligence, for instance, delivers on-device AI capabilities that enable personal devices to make sophisticated predictions about user behaviour, automate daily tasks, and seamlessly integrate across multiple Apple offerings. Meanwhile, Google’s Project Astra is pathfinding a future where personal AI transcends the boundaries of any one device and becomes a service that follows users wherever they go, learning from their interactions across apps, sensors, and platforms. This personalised intelligence may soon become as ubiquitous as cloud-based infrastructure has become in enterprise computing. Meanwhile, in regions like Flanders in Belgium, innovative projects like Tim Berners-lee’s Inrupt personal data pods demonstrate how the web could evolve into a decentralised data-sharing ecosystem where people, rather than corporations, control access to their personal information. By rethinking who holds the keys to data, these pods create possibilities for decentralised privacy-preserving AI applications that perform sophisticated tasks on behalf of individuals, independent of or in coordination with the bigger corporate data pipelines.

This emerging wave of decentralised AI can be described as agentic AI, a concept that envisions a future in which personal AI agents act as trusted representatives for individuals, going beyond mere recommendation engines and actually engaging in actions traditionally performed by humans. Instead of just suggesting that users buy a particular product or subscribe to a specific service, agentic AI armed with digital IDs and verified credentials will be authorised to make these decisions on consumers’ behalf. The shift from AI merely providing advice to AI executing tasks on behalf of its users is significant. It introduces a new model of interaction where humans transfer not just decision-making support to machines but also the authority to complete transactions, albeit often with the users “in-the-loop” providing oversite. This may herald a very different future — one in which agentic AI initiates negotiations and collaborates with other AIs to achieve goals set by its users. This level of delegation cuts to the core of what marketing, sales, and service design will look like in a future where businesses might not interface with humans at all.

How might ubiquitous agentic AI transform everyday life? Consider booking meetings, which has already started to become partially automated with tools like Calendly or AI-driven scheduling assistants. An agentic AI might not only schedule an appropriate slot on a calendar but also negotiate with other stakeholders’ AI assistants to find the ideal time and place, confirm the best available rates for travel, and coordinate last-minute changes. In the realm of utilities, an agentic AI could automatically monitor the market for better electricity or broadband deals, then seamlessly switch providers whenever it finds a plan that offers a balance of cost savings and service quality aligned with the consumer’s preferences. When planning a holiday, this personal AI might book flights, accommodations, and excursions, taking into account real-time data about weather patterns, crowd levels, and personal interests, and then finalise the transactions without requiring the user to fill out yet another form or call a travel agent. Even large logistical tasks like moving house could be simplified. Instead of an individual spending hours contacting moving companies, insurance providers, and local government offices, the agentic AI would gather quotes, compare rates, check regulatory requirements, and handle documentation, negotiating on the user’s behalf and finalising the best combination of services for the move. Each scenario reimagines the user not just as a customer, but as someone whose AI actively shapes the market by building new offerings from the fragmented services corporates offer. If corporates do not adapt, they might find themselves locked into outdated interactions with real people only at the final step — or cut out of that final step entirely if a competitor’s APIs align better with the personal agentic AI protocols.

For corporate marketing, these scenarios challenge every layer of the Martech stack. Today’s systems are designed to engage human consumers, even though much of the marketing process is automated on the enterprise side. Yet, corporate strategies must shift if personal AI agents become the gatekeepers of the individual’s data. Rather than crafting elaborate ads for human eyes, businesses will need to persuade AI agents whose primary goal is maximising the user’s defined preferences. Marketing teams accustomed to applying emotional appeals or brand narratives may find those tactics less effective when negotiating with algorithms that weigh quantifiable trade-offs. Further complicating things is the possibility that agentic AIs could outmanoeuvre even sophisticated corporate tech stacks. They might learn how to switch user behaviour quickly, exploit timing issues in dynamic pricing, or band together to negotiate bulk discounts. If millions of personal AIs join forces, they could dramatically flip market power away from incumbents.

Enterprises that assume humans will remain the main point of contact may discover their Martech funnels sidelined. Predictive analytics might still have value, but the data signals and feedback loops on which they rely will change fundamentally once AI agents control access. Without direct human touchpoints, brand loyalty could evaporate into purely algorithmic loyalty — if an AI perceives that rival services offer a better package, it can pivot instantly. Marketing materials might never reach human eyes and instead be filtered out by AIs that deem them irrelevant. On a larger scale, personal AIs might cooperate to exploit inefficiencies in corporate logistics, or en-masse time product purchases to off-peak demand or collaborate to gain crowd-based discounts. Such collective behaviour turns traditional digital marketing on its head and demands a rethinking of how companies architect their entire Martech stack.

This suggests that today’s corporate investments may mirror factories using combustion engines to pump water instead of fully leveraging a revolutionary invention. Although automating an existing marketing funnel can yield efficiency gains, it may miss the broader shift toward decentralised, agentic AI that grants individuals unprecedented leverage. Just as the internal combustion engine’s real genius was its potential to unshackle factories from rivers, AI’s true impact lies in its ability to empower individuals to manage their data, transactions, and relationships on their own terms.

The ramifications for Martech run deep. Much of marketing is currently geared toward capturing people’s attention, triggering emotional responses, or seeding brand recognition. In a future dominated by personal AIs, the emphasis shifts to forging AI-friendly interfaces, competitive pricing logic, and transparent value propositions that an algorithm can parse quickly. Gathering zero-party data becomes more complicated when individuals can easily shield it via decentralised data pods. Businesses that thrive in this environment will be those that rethink everything from product design to loyalty programs, ensuring their offerings meet the strict, data-driven criteria of agentic AI.

None of this is hypothetical; the pieces are already in play. Apple and Google are steadily pushing on-device AI, and decentralisation initiatives like Inrupt, Dataswyft, Dataspaien and Meeco are proving that individuals can hold and control their own data. Meanwhile, corporate AI strategies often centre on short-term wins — micro-targeting improvements, chatbots for customer support, automation or improved analytics pipelines. Though these initiatives can enhance efficiency, they fail to address how quickly consumer-facing AI could flip the script. If executives delay, they risk facing a radically altered marketplace where algorithmic intermediaries place or withhold orders, negotiate real-time pricing, and filter brand messages before humans ever see them. Strategies that cling to funnel-based marketing might eventually be as obsolete as water-powered factories in the age of abundant electricity.

The challenge, then, is to view AI not as a tool to make legacy models more efficient, but as a catalyst for ground-breaking change that will redefine how corporations and individuals interact. This moment calls for bold thinking about the role of data ownership, AI interoperability, and value exchange. Just as the internal combustion engine enabled entirely new industries — cars, airplanes, modern logistics — AI stands to transform business more deeply than most organisations are prepared for. Leaders who embrace this shift can create new forms of value by aligning with consumer-side agents, building trust at the algorithmic level, and finding ways to collaborate.

These transformative implications of the emergence of AI still lay largely untapped. Its onset invites a fresh wave of thinking: not how to embed AI into existing corporate infrastructures, but to understand how the nature of competition will change, how new corporate winners and losers will emerge and how new business models can sustainably create value. Make no mistake: the coming decade will see many established brands go to the wall while new market leaders emerge. Few enterprises have taken this notion seriously, partly because it challenges entrenched organisational practices and threatens near-term profits derived from an antiquated funnel-based approach. Yet the longer they wait, the more likely it becomes that their future markets will be defined by competitors who have embraced personal AI systems, leaving those who relied on incremental improvements locked out of a marketplace that has moved on. The challenge for corporate leaders is to start seeing personal agentic AI as a lever for radical transformation — one that will reshape not just the efficiency of operations but also the fundamental dynamics of how businesses engage with the people they aim to serve.

The loss of a child is every parent’s worst nightmare. The loss of three innocent children in the Southport knife attack, senselessly murdered by a 17-year-old boy, must leave an unimaginable gulf in the lives of their parents, family, and friends. Yet that wasn’t the reaction of all hearing the news of their murder; some callously saw it as an opportunity. Almost immediately after the attack, a user of X (formerly Twitter) posted a screenshot of a LinkedIn post falsely purporting to be from one of the parents, claiming the attacker was an immigrant. This X user asserted that the attacker was called Ali al-Shakati, who arrived in the UK illegally by boat last year and was on an MI6 watch list. According to the Police, none of this was true.

Yet, this disinformation spread rapidly on X, with posts on small accounts being picked up by larger accounts with more followers. These were then amplified by news channels such as Channel3Now and Russia Today — one of whose posts were reposted by Elon Musk. X was far from alone, with TikTok and a host of other platforms being used to spread the disinformation. In part, this rapid viral spread was driven by X’s and TikTok’s recommendation engines, which featured the disinformation in their “Topics trending in the UK” and “What other users searched for” features. Messaging platforms such as Telegram, Signal and WhatsApp were then used to orchestrate the flow of anger from the digital world to the physical streets, leading to the worst rioting in the UK for a decade.

In the aftermath of the rioting, the media and politicians focused on the role of social media platforms in sparking the violence. The UK’s ‘Online Harms Act’ makes platform owners responsible for illegal content, such as incitement to violence and racism. More problematically, it tries to address content that may be considered harmful but not illegal. This is not easily codified, as the same content can be disinformation, fiction or a joke, depending on its context. Further, when should a sincerely held view, no matter how irrational, become unutterable? To navigate this line, the ‘Online Harms Act’ mandates the platform providers to offer tools to their users that can block certain types of content — such as that relating to misogyny, eating disorders or self-harm. This requires the platform providers to decide whether the content falls into these categories. How they will do this is opaque, with no clear rules or appeals process for those who feel their voice has been unreasonably silenced. The Act, in effect, outsources censorship of the Internet to private companies without proper public governance. Yet, what alternatives exist?

In a deep sense, this dilemma between letting harmful disinformation flow and censoring it is a result of the architecture of today’s internet. This is an architecture where the Internet platforms control our data and use it to target us with content. As such, they are the only point of control who can restrict what content we are shown. When we use X, Facebook or TikTok, we enter an information bubble of their design, even if they give us tools to control the bubble’s size. Yet there are alternatives. It’s perfectly feasible to give control over filtering the content back to individuals in the form of data and tools that live on their devices. These AI-powered filtering tools can be connected to fact-checking services of the individual’s choice, the ones they trust, to empower them to filter their own content. Public education campaigns could help individuals comprehend the consequences of setting their tools to different risk levels. This provides individuals with the means to minimise the likelihood of being exposed to disinformation. After all, people don’t want to be fed lies and misled, and it’s short-sighted not to trust and empower citizens to be an active part of the solution. This approach prevents power from becoming concentrated in corporations or the state and preserves the individual’s agency.

By Matt Stroud, author of ‘Digital Liberty’ — published by Buckingham University Press

As election fever grows in the United States, there’s a growing realisation that the 2024 Presidential election is unlike any before. It will take place against a backdrop of the increasing influence of social media and growing political polarisation. This polarisation has manifested in the US as an intemperate dialogue between Republicans and Democrats, where divides are attributed not to differences of opinion, but to the opponent’s lack of intellectual or moral fibre.

According to YouGov, more than half of all American adults get their news from social media. Yet, as widely reported, the AI that decides what content to send to whom is not designed to deliver unbiased information but rather to engage users, keep them on the platform and drive the social media companies’ advertising revenue. To make matters worse, human nature responds more strongly to negative information than positive. As Steve Rathje from the University of Cambridge University, who studied the phenomena, puts it, “This business model has ended up rewarding politicians and media for producing divisive content in which they dunk on perceived enemies.” This creates a mismatch of incentives between society, which needs reliable sources of news, and social media companies, whose directors are under a fiduciary obligation to deliver shareholder returns.

This is the tip of an iceberg below which malevolent actors lurk. In 2018, Cambridge Analytica hit the headlines after it was revealed that they had misused data relating to 87 million people that they had acquired from Facebook. Cambridge Analytica’s business model centred on influencing elections by using people’s digital footprints to construct their psychological profiles. These profiles were then used to target the voters with political adverts, which played to their psychological motivations to sway their vote. Over time, Cambridge Analytica constructed a database which their CEO Alexander Nix claimed; “… in the United States we have somewhere close to four or five thousand data points on every individual … So we model the personality of every adult across the United States, some 230 million people.” The company played a role in Ted Cruz’s campaign in 2015, Donald Trump’s campaign in 2016 and the Brexit referendum. Following the scandal, Cambridge Analytica went into administration, although some of the staff went on to found similar businesses. Cambridge Analytica wasn’t an isolated rouge business but rather just one competitor in a vibrant market. In 2023, an international team of investigative journalists broke the story of “Team Jorge”, an Israeli outfit specialising in manipulating elections using an army of 30,000 fake digital identities used to post disinformation across social media. This misinformation would then get picked up by the mainstream media and amplified. Team Jorge was led by the Israeli ex-special forces operative Tal Hanan. Hanan is alleged to have boasted to undercover journalists that he’d been involved in 33 presidential campaigns globally, 27 of which he’d secured ‘successful’ outcomes. According to the undercover journalist, Hanan quoted Team Jorge’s fee of ‘between €6m and €15m for interference in elections.

The dividing line between such private companies and state actors is blurred. Russian interference in Western elections hit the headlines during the 2016 US presidential election and has been detailed in the FBI’s ensuing “Crossfire Hurricane” investigation. More recently, in June 2024, as the European Union went to the polls, Peter Stano, the European Commission’s lead spokesperson for foreign affairs and security policy, accused Russia of using social media to spread disinformation during the EU election. The Commission estimated that Russia had spent about $1 billion dollars on their campaign, which primarily targeted Germany and sought to boost the popularity of far-right populist parties. With the war in Ukraine war raging and the risk of conflict between Russia and the West growing, Russia may view incapacitating the West by sowing division as a way of weakening its adversary, in case the new cold war turns hot.

All this raises the question of how these trends will develop and what the future has in store for open, liberal democracies. To understand this, we need to look at how technology is developing and consider what new threats and countermeasures will emerge. Today, our digital landscape is being redefined by Artificial Intelligence. With rival models from Open.ai, Google and Meta being released on an almost monthly basis and Apple readying its debut, the pace shows no sign of slackening. In April 2024, Apple published a research paper describing an approach to AI called ReALM, which demonstrated the power of feeding AI models details of the user’s broader context. This helps the model better understand what answers are relevant to the user, significantly boosting the model’s performance. ReALM used data about what was on the user’s phone screen and was detectable from its mic and camera. However, more data will offer more context and further boost the model’s power. In this endeavour, Apple is well placed as, with the user’s permission, it can access other data stored on the phone, in their iCloud account and data from other organisations through initiatives like ‘Open Banking’. The result will be a new powerful form of personal AI, not in the cloud, but embedded in each person’s phone. Indeed, the likes of Apple, Google and Samsung have been preparing this ground for years, introducing ever more powerful neural engines into their phone’s microchips to power AI.

Initially, the impact of personal AI will be modest: smarter Siri and better suggestions. In time, new forms of Apps will emerge containing ‘Robotic Process Automation’, which won’t just suggest options but also carry out their user’s wishes. For example, they won’t just provide suggestions for better utility provider tariffs, but they will also be able to switch providers, too — and do this weekly if the user wishes. These new ‘personal AIs’ will act as decision support tools, helping their users become happier, healthier and wealthier. There’s no element of your life in which they won’t be able to make insightful recommendations; “Are you sure you should be dating that person?” The problem is not that these recommendations are intrusive, although they may be — the problem is that they will be right. No one will indeed force you to follow these suggestions, but what choice will you have when you see those who don’t are poorer, less healthy and less happy? In truth, you’ve no choice at all. Yet, how do you know that your personal AI’s recommendations are entirely in your best interests and not skewed by a few percent to benefit the commercial or political interests of big tech? Who guards the guards?

Personal AI will become the lenses through which we see the world. They will help us find information that is relevant and engaging; they will essentially become personal filters shaping what we see from all information channels, including social media and mainstream media. Today, we spend 40% of our waking hours online, and in the future, much of this will be spent speaking and interacting with our personal AIs. They will develop a unique understanding of our interests, personalities, hopes and fears. This will give them a unique ability to influence us, and so it’s vital that they seek to further their users’ interests, not those of their ‘big tech’ creators, political parties, or malign hostile states. Unless ways are found to ensure that this is the case, our democratic processes will become hollowed out, and all that will remain is the ritual and theatre of casting our vote.

Yet, we must be careful not to mistake the medicine for the disease. In the future, those who control the data and AI will wield evermore power — they will control society’s nervous system. If we centralise that data and AI in today’s internet platforms, of Facebook, Google, Apple and Amazon, their unaccountable power will lead to growing tension with the Western open, liberal, democratic tradition. Decentralising data and AI is a vital step in preserving individuals' agency. The real challenge we face is how to ensure personal AI is aligned with its user’s best interests, not those of Elon Musk, Team Jorge or Russia.

Today’s revolution in data and AI could herald a new enlightenment, but dystopian alternatives are also at the door. At this juncture, it’s vital that we grasp the relationship between our technology’s architecture and the character of the societies in which we want to live. As the American academic John Culkin said, ‘We shape our tools, and then our tools shape us.’ Only then can we hope that in the 2028 ballots, our algorithms will cast our votes.

By Matt Stroud, author of ‘Digital Liberty’ — published by Buckingham University Press

Would you like a small, medium, or large brain, Sir? What an absurd question! Brains are not like sizes of clothing; they’re more complex and multifaceted. The cognitive diversity in any population is vast. Each has its strengths and weaknesses. These differences are important and valuable, as employers increasingly recognise.

“Neuro-diversity is key to keeping Britain safe. At GCHQ, some of our most talented and creative people have a neuro-diverse profile — including dyslexia, autism, dyscalculia and dyspraxia conditions that affect the ability to process different forms of information. Having a diverse team and a mix of minds better equips us to carry out our mission and tackle new and emerging threats posed by terrorists, criminals and hostile states.”

- Jo Cavan: GCHQ’s director of strategy, policy & engagement

This richness of cognitive diversity is not reflected in today’s service journeys, resulting in waste and poorer outcomes. Take, for example, High School maths classes. A given year may be split into three streams, top, middle and bottom, and each stream teaches different material tailored to the pupil’s learning style. Tailored — really?! So the children in the bottom set who have ADHD, poor diet, low IQ, or whose parents are divorcing and can’t sleep, all have the same needs and benefit from the same material and style of teaching?

Let’s be honest; this is grossly unjust but forced on us by resource constraints in the form of a limited number of teachers. Schools are doing their best, but Personal AI, Data and Digital ID mean that the old resource constraints need no longer be limiting. Each student can be taken on a truly personalised learning journey. One in which AI monitors what each student struggles with and responds by presenting the child with new material, which the AI has learned is most effective to support a child with those characteristics.

Through techniques like federated learning, no child’s data needs to be shared, yet the AI can draw on the experiences of millions of children to find just the right content to help that specific child learn their maths task. In addition to tailoring the content, the AI may recommend more sleep or a change of diet for the child. With Personal AI, Data and Digital ID, our lives will no longer be siloed but rather seen holistically as a set of intertwined threads, each shaping the others.

Today, our university system rigidly selects students based on exam performance without ensuring they have a deep understanding of the subject’s principles. These students are then fast-tracked into top corporate jobs. At the same time, we waste the abilities of large parts of society because their brains aren’t small, medium, or large.

As if this weren’t bad enough, the carnage caused by the lack of genuinely personalised services is not confined to the classroom. What harm is caused by people struggling to understand their finances, or health advice, or voting for politicians with fantastical economic policies? Or on the more frivolous side: How much more exciting could video games be if tuned to our personal fear and serotonin centres?

Today’s personalised services are not good enough, and the harm this causes is plain enough to see all around us. We now have the opportunity to do much better and create better lives for individuals.

By Matt Stroud, author of ‘Digital Liberty’ — published by Buckingham University Press