The Future of the Professions

Why are medicine, law, education, and accounting changing so dramatically? In The Future of the Professions, Richard and Daniel Susskind argue that we are witnessing the unraveling of a centuries-old social contract between professionals and society. They call it the grand bargain—a deal that grants professionals exclusivity, autonomy, and status in exchange for maintaining standards, training members, and serving the public interest. Yet that deal, the authors contend, is no longer tenable in an era of digital technologies, data abundance, and increasingly capable machines.

The book presents a sweeping diagnosis of this tension and a roadmap for what comes next. You will explore how shifts in information technology, economics, and public expectations are transforming the production and distribution of knowledge—the professions’ core asset. You'll see how practical expertise, once inseparable from human experts, becomes codified, digitized, and increasingly delivered through systems and machines. And you'll learn how new skills, new roles, and new moral questions are emerging as humans and technologies start to collaborate and compete.

The Grand Bargain Under Strain

For centuries, the grand bargain gave society trusted guardians of specialized knowledge. In return, professionals policed themselves and pledged to act for the public good. But today six strains—economic, technological, psychological, moral, qualitative, and inscrutability—erode public confidence. Many people cannot afford key services; expertise is often cloaked in abstruse language; and technology promises access and affordability the old model cannot match. As professions cling to exclusivity, they risk becoming moral anachronisms in a connected world where information flows freely.

The Information Revolution

The book frames this shift as the latest transition in our information substructure—successor to the oral, script, and print eras. Print-based societies enabled professionalization by creating repositories of knowledge that required expert interpreters. Now, in a technology-based Internet society, expertise can be digitized, shared, and applied by non-specialists and machines. The authors call this a structural reorganization of how knowledge is produced and delivered. As Walter Ong argued about literacy, each new medium reshapes thought and organization; the Susskinds extend this to the architecture of expertise itself.

Increasingly Capable Machines

At the heart of the transformation is the rise of increasingly capable non-thinking machines. From IBM’s Watson to AlphaGo and GPT‑3, systems now perform tasks—diagnosis, legal prediction, text generation—once thought uniquely human. These systems do not reason like people, but they deliver results that often exceed human accuracy. The Susskinds call this rejecting the AI fallacy: machines do not have to think like you to outperform you. The practical implication is profound—performance matters more than mimicry. As systems process vast data sets, patterns and predictions emerge that no single human could compute, redefining what “expertise” means.

Knowledge as an Economic Commodity

To grasp why this matters, you need to treat knowledge as an economic good. Practical expertise is non-rival (my use doesn’t diminish yours), tends toward non-excludability (once shared, it leaks), is cumulative (each use improves it), and is increasingly digitizable. These four properties make expertise scalable and replicable at near-zero marginal cost, undermining business models built on scarcity and human mediation. Systems like Deloitte’s tax platforms, Wikipedia, or patients’ online communities reflect how knowledge escapes enclosure, becoming a commons as much as a profession.

An Evolutionary Path for Work

The Susskinds map this disruption as a four-step evolution from craft to standardization, systematization, and externalization. Each stage represents wider reach and lower cost: professionals move from bespoke one-to-one service toward knowledge embedded in systems and products. Online document drafting, MOOC-based education, or machine diagnostics illustrate this trajectory. The practical question becomes which parts of your work need human craft and which could be systematized for scale.

Patterns, Models, and Roles

Across different fields, the same patterns recur: routinization, demystification, and new models for delivery—from traditional face-to-face service to communities of experience, embedded or machine-generated expertise. This decomposition of work produces new roles: knowledge engineers, process analysts, empathizers, and data scientists who build, monitor, or complement systems. The Susskinds show that future professionals may either become “better than machine” at empathy, creativity, and moral reasoning, or “build the machine” that others will use.

Innovation and Adjustment

Professional firms face a choice between first‑generation innovation (tinkering with old workflows) and second‑generation innovation (building productized, scalable systems). The leaders are those who routinize complexity rather than defend handcraft. COVID‑19 accelerated this transition by making online courts, telemedicine, and remote teaching mainstream. The temporary crisis proved what was technologically and culturally possible, eroding long-held resistance to change.

Moral and Social Choices Ahead

The transformation raises deep moral questions: What happens to trust without personal relationships? How do we train professionals when machines do routine work? What limits should govern automation in domains of compassion and justice? The authors argue for pragmatism with moral awareness: preserve human responsibility where it genuinely matters, but expand digital access where it alleviates inequality. The grand bargain must be renegotiated—less about monopoly, more about stewardship of systems that democratize expertise.

Key Idea

The future of the professions is not human or machine—it is human and machine. The challenge is to distribute expertise more fairly, design trustworthy systems, and retrain ourselves for roles that creativity, empathy, and moral judgment make uniquely human.

The professions exist because society needs trustworthy expertise in complex domains—health, justice, education, finance. The Susskinds call the formal understanding between professionals and society the grand bargain: in exchange for autonomy and exclusivity, professionals commit to competence, ethics, and public service. Historically rooted in medieval guilds and Victorian professionalization, this bargain formed the stable scaffold of expert life for centuries.

Why the Bargain Is Breaking

The authors detail six fault lines. Economic—fees are prohibitive, excluding those most in need. Technological—knowledge is no longer locked in books or heads. Psychological—individuals can now reclaim agency as self-servicing users of expert systems. Moral—if accessible technology can enhance justice or health, withholding it becomes a moral failing. Qualitative—one-on-one work limits reach and performance consistency. Inscrutability—the opacity of professional judgment reduces transparency and accountability.

Concrete examples bring these cracks to life: LegalZoom offers affordable legal forms; NHS digital platforms broaden access to doctors; online tax systems outmatch accountants for routine filings. These are early renegotiations of the bargain at scale.

What Comes After

The question, the Susskinds say, is not whether the professions will survive but what will replace their role in granting access to reliable expertise. The emerging answer is “systems, not just professionals.” Society still needs trusted frameworks—but those frameworks may increasingly reside in transparent algorithms and networks rather than exclusive human guilds.

Key takeaways

The grand bargain organized expertise for a print-based world; the digital world demands its re‑negotiation. Tomorrow’s social contract will balance technological scalability with moral accountability and equitable access.

To understand why professions rise and fall, the Susskinds ask you to examine the information substructure of society—the underlying media environment that shapes how knowledge is stored, transmitted, and valued. From orality to writing, print, and now the Internet, each stage has reorganized authority and expertise. Professionals flourished in the print era because knowledge was scarce, distributed physically, and required intermediaries to interpret.

Print and the Birth of Professions

Printing enabled standardized texts, professional training, and certification. Universities became centers of textual knowledge and exam-based validation. Professions built their authority on mastery of codified, printed information. This explained why licensing and libraries became central symbols of expertise.

Technology and the End of Exclusivity

The Internet, by contrast, collapses barriers to access. Massive storage, search engines, and analytic tools dissolve technical scarcity. The authors describe a technology lag—a temporary phase when early digital tools overwhelmed users with data but provided little intelligence. That lag is closing as machine learning, natural-language processing, and big data analytics extract actionable insight. Access and capability now converge, challenging the assumption that human experts must mediate all knowledge.

How Availability Changes Expertise

Once expertise lives in systems, it can serve millions simultaneously rather than one client at a time. Tools like TurboTax, WebMD, and LegalZoom illustrate this democratization. While the Susskinds caution that not all expertise is digitizable, they insist the dominant direction is toward broader, cheaper access. In a networked society, being a gatekeeper to knowledge is increasingly indefensible unless justified by moral or safety reasons.

Insight

Every great shift in the “information substructure” has redefined who counts as an expert. The Internet does not merely amplify print—it transforms the logic of exclusivity into an architecture of shared capacity.

The Susskinds’ central argument about technology is pragmatic: machines don’t need to think like humans to outperform them. Rejecting the AI fallacy, they show that pattern recognition, probabilistic inference, and computational scale now enable systems to achieve expert-level outcomes in domains from law and medicine to engineering.

Four Forces Driving Capability

First, exponential information technology growth—compounded by Moore’s and Metcalfe’s laws—continuously increases processing power, storage, and network connectivity. Second, Big Data provides the raw material: billions of data points in health, finance, or justice. Third, second-wave AI systems (like IBM’s Watson or DeepMind’s AlphaFold) use non-rule-based learning to deliver insights impossible by human reasoning paths. Fourth, robotics and affective computing translate digital intelligence into physical and emotional domains—from surgical robots to emotion-aware assistants.

Capabilities Without Consciousness

Machines are, as the authors put it, “increasingly capable non-thinking entities.” Their advantage lies in performance, not sentience. Deep Blue did not become a better philosopher of chess than Kasparov; it became a better calculator. Likewise, a diagnostic system can outperform a doctor by processing correlations across millions of cases, not by being wiser or empathetic. The lesson: when machines achieve superior results, preserving human exclusivity becomes an ethical question, not a technical one.

Human Edge and Moral Boundaries

The authors divide human capacities into cognitive, manual, affective, and moral domains. Machines are proficient in the first two, advancing in the third, and remain limited in the fourth. Thus, the future of human work lies where accountability, creativity, and empathy matter most. Professionals must learn to design, oversee, and interpret systems rather than replicate their output.

Evidence of the Shift

Examples abound: auditors now test 100% of transactions using PwC’s HALO; online dispute resolution replaces some court cases; educational bots adapt lessons in real time. COVID‑19’s surge in telemedicine, online courts, and virtual teaching normalized the presence of digital intermediaries. Machines no longer assist professionals—they increasingly are the professionals.

Key reflection

Focus on what humans should do, not what they currently do. Machines expand the realm of the possible; moral and creative stewardship defines the human response.

Professional work evolves through identifiable phases—craft, standardization, systematization, and externalization—each expanding reach while compressing cost. Understanding these stages helps you analyze where your work lies and how it might transition next.

Craft to Systematization

Craft represents bespoke expertise delivered face-to-face—essential for moral or complex contexts but limited in volume. Standardization identifies repeatable elements and codifies them as checklists, templates, or protocols. Systematization digitizes these rules—embedding them into tools that generate outputs automatically. Examples include automated tax engines, diagnostic platforms, or individualized online curricula.

Externalization and Access

Externalization brings these systems directly to end users, who can access expertise online. The authors distinguish between paid, free, and commons-based models, each raising questions of control and equity. Wikipedia, premium databases, and open government portals represent the spectrum between enclosure and openness. In all cases, knowledge once mediated by professionals becomes directly accessible, often at negligible cost.

Economic Logic

Because knowledge is non-rival and cumulative, sharing expands value rather than depleting it. However, setup costs for engineering systems remain high. The authors propose temporary exclusivity or crowdsourced commons as funding solutions. The risk is that commercial enclosures rebuild monopolies in new digital guises.

Summary insight

Professional work migrates rightward: from rare human craft toward scalable knowledge systems. Each step reduces marginal cost but demands moral vigilance about access and accountability.

The same developments—automation, data, client choice—manifest across all professions. The Susskinds identify eight macro-patterns linking law, medicine, education, consulting, and design. These include routinization of bespoke work, technological transformation, emergence of hybrid skills, reconfigured workflows, new labour models, and rising transparency.

Practically, this means dentistry sees AI diagnostics just as architecture sees generative design and law sees online document assembly. The behaviors converge even when vocabulary differs. This cross-professional lens prevents myopia: your challenges are systemic, not sectoral.

Innovating Beyond Automation

Inside firms, the authors differentiate first-generation innovation—incremental improvements within an old model—from second-generation innovation, which creates products that scale without human supervision. Allen & Overy’s MarginMatrix and Deloitte’s tax engines exemplify the latter. First-generation firms automate; second-generation firms reimagine business around products that work even when people sleep.

Implication for Leadership

Leaders must map their services on a grid of competitiveness versus task routinization, moving from bespoke projects toward scalable systems. Those who cling to handcraft risk a slide into low-margin competition; those who productize capture global scale. COVID‑19’s hybrid experiment revealed that the most resilient organizations were those already working along these second-generation lines.

Guiding principle

Innovation that merely digitizes old practice is camouflage. Innovation that redefines delivery creates enduring advantage.

As professional work decomposes into tasks, new roles and skills emerge. The Susskinds map these roles—data scientists, knowledge engineers, process analysts, empathizers, moderators, and designers—onto the network of future expertise delivery. Each fulfills a distinct function in preparing, encoding, curating, or humanizing professional outputs.

The Core Skill Clusters

You will need mastery in three clusters. First, communication across new media—video, telepresence, and digital communities. Second, data literacy—understanding what algorithms deliver and what biases they hide. Third, technology partnership—working with and designing systems, not just using them. These skills supplement rather than replace ethics and empathy.

Changing Labour Models

Four models dominate: para-professionalization (delegating routine work to trained assistants), labour arbitrage (offshoring), flexible self-employment (freelance experts on platforms), and automation. Professional identity fragments into ecosystems of human and digital contributors, contracting around projects rather than institutions.

Career Guidance

For students deciding their path: become better than the machine in creativity and empathy, or help build the machine through engineering and analysis. For incumbents: collaborate across disciplines, audit tasks, and retrain iteratively. The professions of the future are hybrid spaces demanding constant flexibility.

Takeaway

The professional of tomorrow is not defined by title but by contribution—how effectively you translate complex expertise into accessible, reliable systems and compassionate human service.

Redesigning the professions raises enduring moral debates: Can you trust a system as you trust a doctor? Should some tasks never be commoditized? How do you preserve empathy and human purpose? The Susskinds address these concerns through eight objections, concluding that most rest on nostalgia rather than logic, though moral caution is essential.

Trust and Quasi-trust

You no longer rely purely on interpersonal trust but on quasi-trust—confidence grounded in performance metrics, transparency, and reliability. People already trust algorithmic platforms—Khan Academy, TurboTax, online banks—without assuming moral proximity. The future of trust is data-verified dependability within regulated frameworks.

Empathy and the Division of Labour

Empathy remains vital but need not reside in the same actor who performs technical functions. A medical empathizer can deliver caring counsel while a machine advises diagnostics. The emotional and cognitive components of service diverge, yet both matter for holistic human welfare.

Good Work and Education

Critics fear hollow work and weak training. The authors counter that many routine tasks being automated were never intellectually fulfilling. Well-designed simulation, apprenticeship, and task rotation can train novices more effectively than rote repetition. The ethical responsibility is to design learning experiences, not to preserve drudgery.

Ethical guide

Protect moral boundaries but don’t fetishize tradition. True professionalism lies in expanding reliable access to life‑enhancing expertise, not guarding jurisdictional walls.

Can everyone keep working meaningfully as machines advance? The Susskinds’ answer is cautious optimism. They foresee long-term displacement of many traditional roles but also creation of new ones—if society designs transition mechanisms wisely.

The Hotdog Parable

Their “hotdog story” illustrates how automation destroys and creates simultaneously. When one stage of production is mechanized, others expand as costs fall and demand grows. Yet in the professions, the substitution effect may dominate: machines perform not just sub-tasks but entire decision flows, leaving fewer high-paying human roles.

Feasibility of Liberation

Liberating knowledge into commons-based systems (like Wikipedia or PatientsLikeMe) is socially desirable but financially complex: high fixed costs, near-zero marginal costs. The authors advocate temporary exclusivity to recover investments, then release to open access—balancing incentive and justice. They position this as the digital analogue of the old grand bargain: temporary enclosure for long-term liberation.

Designing for a Sustainable Future

Society must retrain workers, revalue moral and creative labour, and craft funding models that align innovation with accessibility. The future professions will blend entrepreneurial, technical, and ethical capacities around system stewardship rather than service ownership.

Final reflection

Technological unemployment is not inevitable injustice. Managed rightly, it is the gateway to liberated, inclusive expertise and higher-value human contribution.