From corporate human capital to platforms’ digital workers
Lessons for HR leaders, from geopolitical labour friction in the Global South to the silent structural shifts happening inside Western executive suites.
Here is an announcement that slipped under the radar of human capital leaders—yet should have sparked concern: the Kenyan government’s ultimatum to Elon Musk’s X to open offices in Nairobi or risk losing its operating licence in the country.
While the public-facing rationale for the government’s ultimatum to X centres on accountability, disinformation, and tracking cybercrime, the underlying catalyst is a labour showdown involving the human capital that powers global AI.
My analysis was recently featured across several media outlets, including The Daily Influence, where Hannah Oladele interviewed me. I am sharing the article here:
We build on this case study to offer a provocative, macro-strategically minded piece of thought leadership here. Take a deep breath and follow me.
El Lissitzky’s Proun (1922-23, Study for Proun S.K.) is the architectural metaphor for corporate platformization. Just as the Soviet avant-garde sought to dissolve physical reality into ungrounded geometric vectors, modern tech platforms dissolve the human structures of enterprise into sterile data flows. It depicts the organization stripped of its social substance, reduced to a mere node in an algorithmic network.
Context
For years, companies like X have outsourced their data work to Kenya (data labelling, training LLMs, and content moderation) through BPOs such as Sama. When Kenyan workers were traumatized by labelling graphic content or faced mass layoffs, Big Tech’s defence was always: “We don’t have an office there. We don’t employ them; the local BPO does.” By forcing platforms like X to establish a physical presence in Nairobi, the Kenyan government is shutting down this legal shield. A local office means they are subject to Kenyan labour laws, Kenyan courts, and local tax regulations.
Kenya is no longer a passive source of cheap digital labour; it is positioning itself as a strategic regulator. This move coincides with the tabling of the Kenya AI Bill, which aims to regulate algorithmic bias and protect data workers. Kenya’s tech ecosystem understands its value to Silicon Valley. Workers in Nairobi processed millions of hours of AI model training. Because Big Tech depends on this educated, English-speaking, and cost-effective workforce, Kenya knows it has the leverage to demand physical offices without fear of these tech giants abandoning the country.
This pressure did not arise in a vacuum. Nairobi became the birthplace of the African Content Moderators Union, formed by tech workers who revolted against exploitative conditions. Lawsuits in Kenyan courts have already sought to rule that parent companies like Meta are the true employers of local contractors. By forcing X to build a physical office, the government is creating a direct address for regulators and unions to show up and enforce local labour rights.
Political frictions between AI production and human capital
The Kenyan government’s move cannot be fully understood without accounting for the rising political power of data-annotation unions, such as Kenya’s Data Labeller Association. The shadow workers known as click workers have forcefully entered the debate. They have rightly reminded us that the AI we comfortably consume in our Western corporate offices is first and foremost produced by human labour—through annotation, sorting, selection, moderation, and reinforcement learning. Far from the narratives that present AI as a magical black box, using buzzwords like ‘automation,’ ‘agentic,’ and ‘sentient AI,’ artificial intelligence is manufactured daily by hand by operators managed by platforms that are as powerful as they are invisible to the public.
The same mechanism used to abstract and exploit click workers in Nairobi is now being deployed by SaaS giants to abstract and automate the Global North and corporate workforce, sparking initiatives by various stakeholders to organize, resist, and claim their share of the pie. Thus, alongside African data workers, the following groups and initiatives are entering the fray:
Students. Freshly graduated students boo tech evangelists who parade onstage to deliver a reality check to the future workforce—a double bind commanding them to adapt to AI while warning them that they either won’t find a job or will soon lose it. [source]
White-collar workers. White-collar workers sabotage AI deployment initiatives within their companies, to say nothing of shadow AI or tokenmaxxing, which undermine any ambition to scale AI pilots across organizations. [source]
Blue-collar workers. South Korean blue-collar memory-chip workers secured a victory that guarantees a bonus-based share of the unprecedented profits generated by the production of core AI components, after threatening a general strike through their powerful union. [source]
Labour organizations. The ongoing International Labour Organization conference in Geneva presents this session’s ILO standard-setting committee as a major turning point in the regulation and accountability of digital platforms, starting with a once-and-for-all definition of gig workers’ legal status. [source]
Academic and legal activists. Lawyers (e.g., Petra Molnar) and scientists (e.g., Rafael Grohmann) provide global databases documenting acts of resistance to the AI industry, seeking to challenge the scale of AI development at all costs. (e.g., the AI Resist List or the tracker for Worker Mobilization around AI in Arts, Culture, and Media).
Government. California took action to protect workers by signing the first-in-the-nation executive order to address AI’s economic impacts. Policies under review include severance, stock compensation, and worker ownership models, which Newsom’s office calls “universal basic capital concepts.” The order also plans to review how unions are negotiating AI adoption, update workforce training efforts, and assess whether AI-related revenues could support broader public benefit. [source]
Law enforcement. Meanwhile, US law enforcement has coined a new concept to surveil and suppress political initiatives challenging the domestic tech agenda: ‘anti-tech extremism.’ Under this national security umbrella, any movement targeting AI deployments, data center construction, or legislative frameworks like the AI Act is effectively reclassified as a threat to be controlled. [source]
This list is far from exhaustive and will likely grow in the coming months. Yet these few examples offer critical lessons that human capital leaders would be well-advised to understand and integrate into their strategies—no matter how geographically, economically, or politically removed they may seem from their daily corporate landscape.
The blueprints of human capital platformization
As we noted in last week’s article [source], it is time for human capital leaders to abandon the neoliberal mindset that stifles strategic thinking. This framework falsely assumes that technology is value-neutral and promotes a technocratic, middle-ground governance that is allergic to friction and political turmoil. For the true reality check unveiled by AI—a mantra so beloved by tech vendors—is not a broken or inefficient workflow or dataflow. Rather, it is the clashing interests of the distinct human groups that make up the AI production and supply chain.
Accordingly, HR leaders must adopt a political perspective on human capital management in the era of corporate platformization, promoting sovereign human capital governance, workplace co-governance, and operational agency, in which the company actively protects its internal operational culture and human capital reserves from dependence on platforms. This is because the same mechanism used to abstract and exploit click workers in Nairobi is now being deployed by SaaS giants to abstract and automate the white-collar workforce.
The growing platformization of the workforce (12.5% of the global workforce today) is no longer limited to gig or Global South workers but is now permeating the corporate workforce through corporate platformization. Corporate platformization occurs both within and outside organizations through the outsourcing of operations to third-party platforms. Here are six major platforms to study to develop a blueprint for the future of human capital in platformized corporations:
Sama and Appen define the blueprint for labour arbitrage and shadow work. These platforms manage armies of click workers and data annotators across the Global South (notably in Kenya and the Philippines) on behalf of Big Tech and multinationals. They embody the dematerialization of the employment relationship. For the corporate client, complex human labour—such as content moderation and AI data labelling—is reduced to a line of code or an API stream. They provide the template for transforming an internal, unionized workforce into an external, disposable commodity, entirely stripped of legal friction.
Uber and DoorDash define the blueprint for the algorithmic orchestration of physical labour. Initially known as ride-hailing or delivery apps, they have evolved into the go-to last-mile logistics outsourcing partners for restaurant, retail, and distribution giants. They set the ethos of algorithmic management. They prove to traditional corporations that it is entirely possible to control a critical physical operation end-to-end—such as customer delivery—without owning a single vehicle, managing a single schedule, or employing a single worker, by replacing human management with dynamic pricing incentives and algorithmic feedback loops.
Salesforce sets the blueprint for business process standardization. As the global leader in CRM and cloud-based enterprise software, Salesforce embeds its operational logic deep within the corporation. Platforms like Salesforce reshape departmental architecture—sales, marketing, customer service—to mirror the platform’s layout. This is the ultimate blueprint for structural dependency (vendor lock-in): the corporation alienates its operational culture in favour of the platform’s ethos.
Teleperformance and TaskUs define the blueprint for customer experience (CX) platformization. These global giants manage outsourced contact centres, technical support, and customer experience for banks, airlines, and tech companies. They represent the convergence of software platforms (CRMs, AI-driven call routing) and human capital. They enable corporations to transform a core department—customer interaction—into an outsourced commodity, measured by platform-defined metrics (KPIs, resolution times).
Amazon Web Services (AWS) defines the blueprint for total infrastructural dependency. It is nothing less than the global backbone of cloud computing, hosting a hegemonic share of the web and corporate information systems. AWS serves as the physical foundation of platformization. It has trained corporations to abandon their own servers and physical data centres in favour of renting compute and storage on demand. This is the ethos of scalability: a company’s infrastructure is transformed into a variable cost indexed to its consumption, stripping the corporation of its technical sovereignty.
Scale AI provides the blueprint for industrializing the AI supply chain. It serves as the data platform that fuels, trains, and validates artificial intelligence models for the world’s largest corporations, fusing software infrastructure with a massive human workforce (via its subsidiary, Remotasks). Scale AI shows how the production of artificial intelligence is itself platformized. Corporations looking to deploy AI copilots build nothing in-house; instead, they outsource the raw material—high-quality labelled data—to a third-party digital factory. This is the blueprint for the Silicon Kolkhoz: an extreme concentration of technological control, hidden behind the promise of magical automation.
Consequences of human capital platformization for the HR leaders
These six platforms share a single trajectory that human capital and strategy leaders must decode: converting the fixed human structures of enterprise into variable, outsourced data flows. They operate according to their own internal logic and core ethos: algorithmic resource matching following its own governance and managerial rules (which is, in itself, a political choice over resource access and control that slips out of the clients’ organizations’ hands), value extraction (Shoshana Zuboff would name it behavioural surplus), and panoptic surveillance of workers. Eventually, platforms hollow out the corporation’s political and social substance, transforming it into a mere node within a network whose rules they entirely dictate.
Given the same causes leading to the same consequences, human capital leaders must consider how resistance and friction in current platforms operate to anticipate the frictions they will have to manage very soon. Here are the six consequences HR leaders must consider in priority:
Consequence #1: Digital workers are disconnected from the company they work for, jeopardizing the organizational culture that links human capital to the business strategy and vision, and fostering a mercenary-minded workforce.
Consequence #2: The balance of power is so unbearable that digital workers figure out a way to gather and resist together. The unionization of digital workers in Kenya and its influence on the Kenyan government make for a case study.
Consequence #3: When corporate operations are fragmented into modular API streams and token-based digital tasks, the business's underlying contextual knowledge is permanently transferred to the platform (e.g., Salesforce or AWS). The organization loses its corporate memory. Employees lose their understanding of the business ecosystem and become mere task executors, thereby destroying organic and cross-functional innovation.
Consequence #4: As white-collar workers realize that panoptic metrics are managing them, their resistance shifts from overt strikes to covert warfare. Human capital leaders will face a surge in tokenmaxxing, shadow AI usage, and malicious compliance. Workers will optimize their outputs to satisfy the algorithm’s flaws, creating a toxic layer of artificial productivity that masks deep structural decay.
Consequence #5: By outsourcing core operations to a centralized “Silicon Kolkhoz” (like Scale AI or Sama), a corporation’s human capital strategy becomes dependent on the geopolitical and regulatory stability of those specific platforms. If a data-annotator strike occurs in Nairobi, or if an ILO standard-setting committee freezes a platform’s operating model, the Global North corporation’s digital supply chain will experience an instant, catastrophic halt.
Consequence #6: As organizations adopt the state’s security rhetoric of “anti-tech extremism” to protect their platform deployments, HR's role will be dangerously warped. Human Capital departments risk being weaponized into surveillance arms of the corporate tech agenda—treating legitimate worker anxieties about AI deployment or algorithmic displacement not as valid cultural friction, but as insider security threats to be neutralized.
Conclusion
Human capital leaders can no longer be neutral administrative executors of a platform-first agenda. They must choose between managing automated human metrics for external platforms and actively rebuilding a sovereign, resilient, and human-centric corporate core. The future of organizational integrity depends entirely on their willingness to confront this friction.
About the Author & BomaliQ
This insight is authored by Mathieu Lajante, PhD, founder and principal scientist of BomaliQ Inc.
BomaliQ provides specialized strategic intelligence for the algorithmic frontline, helping corporate leaders navigate the behavioural and political frictions of corporate platformization.
Nature of Intelligence
The insights provided in this publication are based on the stress-testing of publicly available industry reports, market data, and proprietary analytical frameworks. This content is intended for informational and strategic signalling purposes only. While every effort is made to ensure the accuracy of the analysis, the algorithmic frontline is a volatile environment.
Limitation of Liability
The BomaliQ Silicon Kolkhoz does not constitute professional consulting advice, legal counsel, or a formal business diagnosis. Readers should not make critical strategic decisions based solely on this newsletter without a rigorous, organization-specific assessment. BomaliQ Inc. and Mathieu Lajante shall not be held liable for any business outcomes or losses resulting from the use of this general intelligence.