Competitive advantage increasingly belongs to organizations that can integrate across technologies, ecosystems and processes.

Section 2 examined how, when technology combinations scale, value is shifted from isolated assets and individual expertise towards coordinated systems that integrate technology, data and human judgement. As these systems expand, the need for effective coordination becomes more relevant than ever.

Competitive advantage in a convergence context is increasingly shaped by how effectively organizations orchestrate multiple technology stacks, partners and corresponding processes into coherent systems that can operate together at scale. Moving from technical mastery to integration across domains becomes an organizational imperative, whether for convergence-native technology providers or technology customers with legacy systems. These imperatives are not new, but as convergence accelerates, they are becoming much more relevant and urgent for organizations.

Working across boundaries

No single organization can build or own the full stack of combinatorial technologies. The goal, therefore, is to reduce friction within the organization and across partners, so collaboration becomes smooth, predictable and mutually reinforcing. This requires two forms of orchestration: internal orchestration that aligns an organization’s own technology, data and people into a single coordinated system; and external orchestration that connects the organization to capabilities across an ecosystem beyond traditional technologies and domains.

The most successful organizations build a culture where advantage comes from connectivity and the speed at which expertise and ideas flow across domains. They do so by establishing ways of working and rituals that make crossdomain exchange a structural habit rather than an occasional event, and by bringing together talent with depth in one area and breadth across multiple domains to enable cross-domain thinking. For instance, Starcloud brings together interdisciplinary expertise spanning data centres, satellite design and software engineering through intentional moments of cross-domain friction, working through problems together from the co-founder level. This interdisciplinary approach allows them to compress their engineering cycles and produce a unique product at the intersection of AI and robotics to build next-generation orbital data centres.

Strategically engaging the ecosystem reinforces learning loops, speeds communication and strengthens collaboration between organizations. As convergent systems increasingly integrate physical and digital technologies, choices about geographic location and ecosystem participation become more important because they shape how quickly coordination, learning and adoption can occur between providers and customers. Starcloud illustrates how these factors reinforce one another. By locating in Redmond, Washington, near major tech R&D centres like Amazon Web Services (AWS), Microsoft and Google, and aerospace companies such as SpaceX and Airbus, the company positioned itself within a dense ecosystem of cross‑domain talent, aerospace fabrication and space‑ready logistics.⁹ This ecosystem enabled tighter collaboration, faster feedback loops and shorter iteration cycles with AWS, integrating the AWS Outposts system into Starcloud satellites.¹⁰

Setting a common language

Technology convergence demands trust, transparency and adaptability across stakeholders. As systems become more interconnected, the absence of shared protocols and standards creates friction that slows integration and limits adoption. A common language, both technical and operational, is what makes orchestration possible at scale.

First movers can gain a strong advantage by introducing new orchestration processes; they can ensure their offering is adopted by the wider market and that others adopt their language. By setting standards that enable tools, data and partners to work together, an orchestrator becomes the default pathway through which innovation flows. This role increases influence, encourages others to build around the orchestrator’s environment and embeds the organization as a structural pillar of the ecosystem.

This dynamic is not new. Google open-sourced Kubernetes, which allows companies to move applications across cloud providers and made it easier for more customers to adopt Google’s cloud services. Anthropic released the Model Context Protocol (MCP) as an open standard, which made it easier for AI agents to connect to tools and data and encouraged more organizations to build around Anthropic’s ecosystem. What is new is that technology convergence makes this logic more relevant than ever.

Commonwealth Fusion Systems (CFS) demonstrates how first-mover orchestration can define an entirely new supply chain. When CFS began developing its breakthrough hightemperature superconducting (HTS) magnets, the specialized tape required had no reliable commercial source. CFS placed orders consuming roughly 10% of global HTS supply, working closely with major suppliers to scale production to the volumes required. By guaranteeing highvolume demand, CFS established the global specification standard for HTS tape, driving down costs, increasing reliability to CFS specifications and creating a stable market whose standards now extend to adjacent industries, including data centre infrastructure.¹¹

Transferring strength across industries

Combinatorial technology compounds complexity and extends development cycles. The strategic question is how to accelerate progress and reduce risk while capturing early economic value. For many organizations, the answer is to transfer what already works rather than develop everything from first principles. In a convergence context, capability development does not always require bespoke solutions. Many of the capabilities needed to advance frontier systems can be transferred, adapted or repurposed from adjacent industries. This provides an opportunity to reconsider what needs to be built from scratch; not every capability requires reinvention.

Again, CFS illustrates this approach precisely. Rather than inventing new manufacturing processes for its fusion magnets, CFS adapted proven technologies from adjacent industries. It repurposed EV chips to modulate radio frequency waves to heat plasma in tokamak systems. This deliberate transference of adjacent capabilities accelerated magnet development and contributed to a 30- fold improvement in production speed, reducing assembly and testing time from 30 days to one magnet every two days.¹²

Orchestration creates the conditions for monetization. Without it, technology combinations remain bespoke projects sold on a per-engagement basis, unable to generate the recurring revenue or fleet-level economics that sustain scaling. The monetization models that follow are not alternatives to orchestration; they are its economic expression.

Three monetization patterns of convergence

While no single model fits every convergent outcome, three patterns appear consistently across the domains examined in Section 2. Importantly, several companies operate across more than one pattern simultaneously; the boundaries are practical, not rigid.

1. Service-based delivery: This represents the most common pattern. Traditional ownership models force customers to absorb heavy financial and operational burdens. A surgical robot requires a million-dollar investment and specialist staff. Factory automation systems need constant tuning and integration. AI platforms demand ongoing model improvements and complex data engineering. Most organizations cannot reach the utilization levels or maintain the specialist teams needed to operate these systems effectively, which makes ownership economically unattractive. Service-based models correct this mismatch by shifting capital requirements, operational risk and system complexity to providers who can distribute these demands to many customers. Adoption becomes much easier for customers, who no longer need to make large upfront commitments or make assumptions about their long‑term use on day one. Providers benefit as well because they accumulate learning from every deployment across their fleet or platform. Performance improves faster than any single customer could manage alone, and the basis of competition moves away from initial product features towards sustained excellence in delivery and operation.

Formic is an automation provider that supplies robotic systems to help manufacturers take over repetitive production tasks and increase throughput. Instead of selling robots, Formic offers them through a RaaS model, combining the hardware, software, data systems and day‑to‑day operational support into one package.¹³ Its clients, often mid-sized manufacturers, pay a fixed monthly fee with no upfront capital,¹⁴ full maintenance coverage and guaranteed performance levels, effectively gaining access to turnkey automation without the cost and complexity of ownership.

This service-based model allows Formic to generate recurring revenue by keeping use high across its robotic fleet, with operations and performance managed centrally. By taking on the capital burden of deployment and maintenance itself, Formic makes automation accessible for mid‑sized manufacturers that previously could not justify buying equipment, expanding the addressable market beyond large enterprises. Each deployment contributes operational data that improves fleet‑wide performance over time, creating a compounding advantage as service quality and unit economics strengthen with scale.

The company reports more than 500,000 production hours¹⁵ across over 120 factories in the US, where it operates one of the largest independent robot fleets in the country.¹⁶ To date, its systems have moved over 1.2 billion items with an uptime of 99.3%. Deployment pace increased fivefold from 2024 to 2025, and the company maintains a 97% customer renewal rate, indicating consistent use and satisfaction.

2. Platform and marketplace models: Where service-based delivery monetizes the provider’s own operational capability, platform models monetize the connections between participants. Black Lake illustrates this clearly: the company does not own factories or produce goods but earns a coordination fee on every transaction that flows through its network. The more participants the platform connects, the more valuable it becomes to each of them. This pattern tends to emerge when convergence creates a fragmented ecosystem with many small providers and many varied demand signals, and the orchestrator’s role is to match, translate and coordinate between them.

Black Lake began as an enterprise software provider for large manufacturers such as Tesla, Foxconn, Contemporary Amperex Technology (CATL), Austron and Weben, building tools to improve operational visibility. While serving these clients, the company identified a much larger gap in the long tail of small factories across China, where sites with fewer than 100 workers still relied on manual planning and had almost no digital visibility. To address this, Black Lake developed a mobile product tailored to these smaller factories. It spread quickly, reaching tens of thousands of sites, placing Black Lake at the centre of a previously disconnected network of small manufacturers.

As this network expanded, creators began asking for help with very small production runs. Large factories did not accept this work, and small factories struggled to engage because creators’ ideas were too general while factory requirements were too technical. Attempts to collaborate broke down, so Black Lake stepped in to coordinate the exchange. The company used its cloud systems to identify where capacity was available and integrated generative AI to turn creative intent into manufacturable files, cutting costs, time and the expertise required to move from idea to manufacturable output. Black Lake monetizes this opportunity through a marketplace coordination model, earning a fee on each transaction by enabling the translation of intent into executable manufacturing solutions across its industrial network, matching demand to available supply – an approach made viable only because orchestration solves the fragmentation barrier. With the marketplace model in place, creators could produce small batches, reducing minimum order requirements, and small factories could earn more by using idle capacity. More than 230 small-batch orders have now been completed.

3. Ecosystem standards and licensing: Some organizations monetize orchestration indirectly by setting standards that make the broader ecosystem depend on their capabilities. Intel’s USB standard, Google’s Kubernetes and Anthropic’s MCP were all discussed in Section 3 as orchestration moves, but they were also monetization moves that increased demand for the sponsor’s core products by making the surrounding ecosystem easier to use. In convergence, where multiple technologies must interoperate, the organization that defines how they connect gains structural pricing power even without charging for the standard itself.

The common thread across all three patterns is that monetization follows orchestration capability. The organizations generating the most durable revenue from convergence are not those with the most advanced technology in isolation, but those that have built the coordination infrastructure to deliver integrated performance and structured it into a revenue model that scales with use.