As AI overhauls professional services pay models, tokenised synthetic equity emerges
In the AI era, professional services firms are under increasing pressure to rethink their compensation structures in line with evolving commercial and operating models. Paul Lalovich, Partner at Agile Dynamics, argues that tokenised synthetic equity represents a compensation architecture that firms can no longer afford to defer.
In May 2026, Samsung’s Korean semiconductor workers ended an 18-day strike by accepting a profit-sharing arrangement that could deliver bonuses of up to €350,000 per employee. More than 60,000 union members voted for the deal. Under its terms, chip division staff will receive a performance bonus equivalent to 10.5% of earnings, paid in stock, with payouts rising steeply if operating profit reaches the ₩300 trillion threshold analysts now expect.
Samsung’s rival SK Hynix had already moved in the same direction the previous year. Workers at LG, Kakao, and other Korean technology companies are now demanding equivalent arrangements and have threatened strikes if they do not receive them.
The story ran briefly on the technology wires. It should have run on the front pages of every management journal in the world. Because what happened at Samsung was not a Korean labour dispute. It was a preview of the global conversation about who captures the economic value that artificial intelligence creates – and who does not.
At almost the same moment, the Financial Times reported that McKinsey & Company was overhauling its partner compensation model. The firm had told partners to expect a greater proportion of their pay in equity rather than cash – a move designed, in the words of those familiar with the changes, to simplify pay structure, boost capital, and protect the firm from turbulence in the consulting industry. For some partners, the shift would affect tens of thousands of dollars. The mechanics were internal. The signal was systemic.
McKinsey is not a distressed firm making concessions. It is the most prestigious consulting firm in the world taking a considered strategic bet: that retaining capital and converting cash into long-horizon alignment is the right posture for the AI era. When the market leader restructures partner pay in the same month that Samsung’s workforce strikes for AI profit-sharing, something structural is in motion.
The only question left for every other professional services firm is whether they design their response or have it forced upon them.
The ‘old’ model in professional services
For decades, professional services partnerships operated on a stable equation: hours billed, multiplied by rate, distributed annually as cash. Artificial intelligence has broken every variable in that equation simultaneously.
Analytical work is compressing. Tasks that once anchored leverage models – research synthesis, document review, financial modelling, due-diligence triage – are now performed by AI systems in a fraction of the time previously required. The billable hour is not disappearing, but its share of genuinely high-value work is shrinking, and clients are explicit about it in fee negotiations. They are unwilling to pay human-scale rates for work delivered at machine speed.
Outcome-based pricing is accelerating in parallel. Clients want fees tied to measurable results – cost savings realized, transformation milestones achieved, enterprise value created. These outcomes materialize over multi-year horizons, not within a single billing cycle. Firm revenues are becoming lumpier, more contingent, and more back-loaded, while the cost base remains stubbornly fixed and front-loaded. The mismatch between revenue realization and compensation distribution is no longer theoretical; it is showing up in cash flow statements.
The third pressure is capital. Building defensible AI capability is not a marginal IT line item. It requires sustained investment in models, data infrastructure, engineering talent, and workflow integration. As Strat-Bridge observed in its analysis of the McKinsey announcement, firms that were historically asset-light – where the core assets were talent, relationships, and reputation – are now being forced to behave more like operating platforms that combine advisory capability with proprietary AI infrastructure. That requires capital. And capital distributed as same-year cash bonuses is capital that cannot fund the platform.
The implication is direct. Compensation architecture has crossed the line from an HR mechanism to a strategic capability lever. A firm whose reward system pushes cash out the door every year cannot simultaneously fund AI, weather revenue volatility, and align partners to multi-year value creation. Historically, when these pressures collide, investment in the future is the first casualty. In the AI era, that trade-off is no longer survivable.
The mechanism: ownership without dilution
The conceptual breakthrough behind synthetic equity is the unbundling of rights that conventional equity packages together. A traditional share confers economic participation, voting power, governance rights, information access, and a liquidation claim in a fixed bundle.
Synthetic equity allows a firm to decompose that package precisely – granting economic participation in capital growth or profitability while retaining voting, governance, and control entirely within the existing partnership structure.
For professional services partnerships, this distinction matters more than in almost any other organizational form. Partnerships are governance-sensitive by design and culture. Diluting actual equity, or extending voting rights to a broader contributor base, threatens the decision-making fabric that holds the firm together.
Synthetic equity sidesteps that constraint entirely. Senior contributors receive genuine upside alignment; the partnership retains undisturbed control over admission, strategy, and capital decisions. The model expands alignment without expanding ownership in the legal sense — and that asymmetry is what makes it strategically valuable.
Tokenization is the operational layer that makes this practical at scale. Smart contracts automate vesting schedules, performance gates, and distribution logic. On-chain records replace spreadsheets and side letters with a transparent, tamper-evident audit trail. Administrative friction collapses. Disputes over what was promised, when, and on what conditions become structurally less likely. The instruments are not speculative tokens; they are instruments of disciplined long-term alignment, designed to behave like deferred ownership stakes rather than tradable assets in search of a market price. Tokenized real-world asset infrastructure has matured to institutional grade, and the custody, compliance, and integration infrastructure required to operate tokenized compensation at scale exists today in a form that was not credible three years ago.
“Tokenization is the operational layer that makes synthetic equity practical at scale.”
Where most implementations fail
This is where most tokenization conversations stop – and where most implementations quietly collapse.
Tokenized synthetic equity treated as a technology project produces a predictable pattern. Tokens are issued. Vesting schedules are encoded. Dashboards are demonstrated to the executive committee. And then partners quietly resist, the remuneration committee hesitates to approve grants at meaningful scale, valuations are challenged in corridor conversations, and within two cycles the programme is wound down or reduced to a symbolic overlay on the cash bonus system it was supposed to replace. The failure is rarely technical. It is almost always architectural and behavioural.
The questions that determine adoption are questions about partnership economics, and they must be answered before any code is deployed. What share of total compensation should sit in tokens versus cash, and how should that split vary by cohort, tenure, and contribution profile? What does contribution actually mean in an AI-augmented firm, where origination, delivery, capability-building, mentoring, and enterprise-value creation now sit in different proportions than they did a decade ago?
Who decides grant size, vesting, and adjustment – and what is the escalation path when senior partners disagree? How is the underlying enterprise value calculated, by whom, with what cadence, and with what independent oversight? Do partners believe the system is fair, or do they suspect it is a capital-conservation device dressed in technology language?
Until these questions are answered with conviction, no smart contract is worth deploying. The reward logic must precede the tokenization layer; the technology then encodes a system the partnership already understands, has debated, and is willing to support.
Advisory work – partnership reward diagnostics, contribution architecture, remuneration committee governance, valuation discipline, and partner adoption planning – must do its job before infrastructure is procured. The sequencing is the strategic point. Reversing it is the single most reliable way to ensure the programme never reaches full adoption.
The risks that deserve explicit attention
Four risks warrant honest attention from any firm considering this transition. Regulatory and tax classification of synthetic equity tokens varies materially by jurisdiction and is still evolving in most major markets. Mitigation requires engaging specialized legal and tax counsel at the design stage – before issuance – and structuring jurisdiction-specifically rather than assuming a uniform classification.
Partner resistance is the deeper risk. Partners who perceive the shift from cash to tokens as capital-conservation rather than strategic alignment will resist quietly and persistently. The narrative must be anchored in strategy – in how preserved capital funds AI investment, in how the contribution logic was designed, in how value accrues over time – because partners care about the future of the firm, not about smart contracts.
“The compensation advantage of the AI era will belong to firms that redesign the relationship between contribution, capital, and enterprise value.”
A tokenised programme is also only as credible as the valuation that underpins it. If partners distrust the inputs, the immutability of on-chain outputs is irrelevant. Establishing the valuation methodology in advance, documenting it, and making inputs transparent to participants is not optional governance hygiene – it is the foundation of adoption.
Finally, governance drift: token rules encoded once can become disconnected from firm strategy as conditions change. Remuneration committees lose ownership of mechanisms they did not design. Mitigation requires building review and adjustment mechanisms into the programme design from the outset, not as afterthoughts.
The window is narrowing
Samsung was forced into structural alignment by confrontation after 18 days of industrial action. McKinsey moved of its own accord, reading the economics before the pressure became acute. These are not isolated events. They are two data points on the same trend line: the global recalibration of how AI-era value is shared between the organizations that generate it and the people whose expertise makes it possible.
Professional services firms have assets that Samsung does not: the governance flexibility to design alignment proactively, the partnership structures that synthetic equity was built for, and – for now – the time to make the transition on their own terms rather than under duress. Challengers can build contribution logic from inception around the variables that define the AI era: client outcomes delivered, AI capability built, collaboration generated, enterprise value created.
Incumbents must do the harder work of redesigning existing structures – but the credibility gap from moving prematurely on the technology without resolving the partnership economics first is significantly more costly than doing the diagnostic work properly.
The compensation advantage of the AI era will not belong to the firms that simply tokenize their bonus pool. It will belong to the firms that redesign the relationship between contribution, capital, ownership alignment, and enterprise value – and then encode that redesign into infrastructure that runs reliably and transparently at scale.
Tokenized synthetic equity is the mechanism. Partnership reward governance is what gives it legitimacy. Neither works in isolation, and the firms that recognize this earliest will set the terms on which talent, capital, and clients are won for the rest of the decade.
