By Brian Wallace
CES reignited a familiar narrative this year, faster chips, bigger models, lower inference costs, all fueling speculation that we are accelerating toward the singularity, that hypothetical moment when AI improves itself faster than humans can guide or control it. With every hardware breakthrough, the storyline tightens: more compute equals more intelligence, more intelligence equals inevitable transformation.
Nvidia’s Vera Rubin GPU is a striking example, delivering 50 petaflops of performance, significantly reducing inference costs when models share answers, lowering latency while increasing throughput. The trajectory is undeniable, compute is scaling at extraordinary speed. But raw intelligence is no longer the hardest problem inside the enterprise.
If a form of singularity arrives in business, it will not be because machines suddenly crossed an intelligence threshold. It will be because intelligence became coordinated. Hardware accelerates capability, but coordination determines outcomes, and that distinction is where most organizations either compound value or create chaos.
We are entering an era where intelligence is abundant. Models can generate, predict, summarize, recommend, automate. The limiting factor is no longer whether AI can think, it is whether organizations can think with it. That is not a semiconductor constraint, it is an orchestration challenge that sits above the hardware layer.
Consider the orchestra metaphor. A Stradivarius violin does not create a symphony, a Steinway piano does not compose music. You can assemble the finest instruments ever crafted and still produce noise unless timing, context, and intent align. The enterprise is no different. Chips are instruments, models are instruments, agents are instruments. Without shared rules, shared definitions, and synchronized objectives, intelligence fragments into local optimizations and disconnected outputs.
Domo’s role in this environment is not to replace the instruments or compete with chip makers and model developers. It functions as the conductor, ensuring that data, models, agents, and human decision-makers operate in concert, aligned to shared business outcomes. The singularity inside an organization will not be defined by which GPU it purchased, but by how well its intelligence systems are synchronized across the enterprise.
This orchestration position also defines Domo’s long-term growth trajectory. History shows that companies sitting at coordination layers often compound value faster than those focused solely on components. Think of how ServiceNow became the workflow backbone across departments, or how Salesforce evolved from CRM to enterprise operating layer, or how AWS abstracted infrastructure into a scalable platform. In each case, the company that unified systems and standardized execution captured durable enterprise spend. As AI proliferates, Domo’s role as the coordination fabric across data, agents, and governance places it in a similar position, expanding not by replacing intelligence providers, but by becoming indispensable to how intelligence actually runs inside the enterprise.
This is where hardware readiness diverges from enterprise readiness. CES showcases what machines can do in isolation. In practice, businesses struggle with what machines should do, how they should connect, how their actions should be governed. The cracks are visible across industries, AI agents acting without context, models optimizing for narrow departmental metrics that inadvertently damage broader system performance, insights arriving faster than teams can trust or act on them.
The bottleneck is not speed, it is alignment across data sources, business rules, governance frameworks, and human judgment. Many AI strategies falter not because intelligence is weak, but because it is fragmented. Each team deploys its own tools, each system maintains its own definitions, each model operates on a partial view of reality. The result is acceleration without coherence.
Domo has long focused on unifying enterprise data across systems, teams, and geographies, creating a governed and shared foundation for decision-making. In the age of proliferating AI agents, that unified layer becomes more than analytics infrastructure. It becomes the coordination backbone that enables a true digital twin of the business, a living, synchronized representation of how revenue, supply chain, workforce, finance, and risk interact in real time.
A digital twin in this sense is not a static dashboard or retrospective report. It is a continuously updated model of enterprise reality, where data, rules, and intent intersect. When intelligence is coordinated through that structure, decisions are no longer isolated events. A pricing adjustment reflects margin impact, inventory constraints, and compliance requirements simultaneously. A marketing reallocation considers workforce capacity, supply chain readiness, and financial targets in parallel. That is orchestration at scale.
The airport metaphor offers another lens. Modern aircraft are engineering marvels, but air travel works because airspace is coordinated. Air traffic is managed, rules are enforced globally, intent is declared before action, humans intervene when judgment matters most. The system functions not because planes are intelligent in isolation, but because they operate within a governed and synchronized network.
As AI agents multiply across the enterprise, someone must manage the airspace. Which agent is allowed to act, on what data, with which permissions, toward which objective, under what oversight. That management layer is not a chip, not a standalone model, but an orchestration framework that aligns autonomy with accountability. Domo increasingly operates in that layer, embedding business rules into workflows, enabling intent-driven interaction rather than dashboard-driven exploration, and maintaining human-in-the-loop oversight at machine speed.
The conversation around “vibe coding” underscores this shift from raw speed to coordinated systems. “Vibe coding showed how fast ideas can turn into software, but speed alone does not create durable value,” said Josh James, CEO at Domo. “The next evolution is moving from raw code generation to composing applications that are governed by design and grounded in trusted data. Domo is the critical cog in that machine. Rather than vibe-coding, it’s more like integrated vibe-orchestrating, as we give teams a production-ready foundation they can extend confidently, instead of starting from code that was never meant to scale, and didn’t account for the technical debt it was incurring.” His framing captures the core issue facing enterprises today, speed without governance introduces risk, code without context accumulates debt, intelligence without orchestration erodes trust.
The shift underway is structural. Enterprises are moving from dashboards to decisions, from isolated agents to interconnected ecosystems, from intelligence as insight to intelligence as execution. As compute becomes cheaper and faster, trust becomes scarce, context becomes critical, coordination becomes the differentiator. Governance and transparency establish trust, unified data establishes context, orchestration aligns people and machines.
An organization may deploy dozens of AI models across finance, marketing, operations, and HR. Without shared context, each will optimize for its own metric, margin, growth, efficiency. Rational in isolation, destabilizing in combination. Coordinated intelligence embeds enterprise objectives, constraints, and accountability into the fabric of machine-driven decisions, ensuring that local optimization supports global performance rather than undermining it.
The popular singularity narrative centers on runaway computation, machines improving themselves beyond human comprehension. The enterprise version is less dramatic but more consequential. The inflection point will come when businesses can coordinate intelligence securely, transparently, and at scale across people, machines, and processes. A coordinated digital twin creates a form of enterprise self-awareness, not artificial general intelligence, but a synchronized system where data, rules, and intent move together continuously.
In that environment, AI agents operate within defined boundaries, humans remain responsible for ethical and strategic oversight, machines execute with speed and precision. The companies that lead will not simply be those with access to the fastest chips. They will be the ones that orchestrate intelligence coherently across the enterprise, transforming capability into coordinated action.
Hardware enables the future, but orchestration makes it operational. If singularity ever becomes more than a concept inside the enterprise, it will not be because compute crossed a threshold. It will be because coordination did, turning intelligence from scattered brilliance into synchronized execution at scale.
