At GTC Conference 2026, Jensen Huang did more than outline product roadmaps. He sketched a shift in how computing itself is valued, arguing that the future of artificial intelligence will revolve around a single unit of measurement: tokens.

The Nvidia chief executive described a world where computers function less like personal tools and more like industrial systems that continuously generate intelligence, with tokens serving as the output. In that framing, AI is no longer a feature embedded in software—it becomes a consumable resource, metered, priced, and optimized much like electricity.

Tokens, often described as fragments of text processed by AI systems, have largely existed in the background of tools such as ChatGPT and Claude. They determine how much input a model processes and how much output it generates.

What Huang is proposing goes further. He is effectively elevating tokens from a technical accounting unit into a financial and strategic metric that could reshape corporate decision-making.

In practical terms, this introduces a new cost paradigm. Traditional enterprise software spreads costs across fixed subscriptions or licenses. Token-based pricing ties cost directly to usage, meaning that every query, automated workflow, or AI-generated output carries a marginal cost. The more deeply AI is embedded into operations, the more variable—and potentially volatile—those costs become.

A New Layer In Corporate Budgets

Huang’s suggestion that tokens could sit alongside salaries and infrastructure in corporate budgets signals a structural change in how companies allocate resources.

In high-skill environments, particularly engineering, the balance between human labor and machine-generated output is shifting. Huang argued that giving engineers access to substantial token budgets could unlock productivity gains that far outweigh the additional compute costs. In effect, companies would be buying output, not just labor.

That logic reframes compensation itself. Instead of simply paying for time or expertise, firms may increasingly invest in augmented productivity, where an employee’s effectiveness is amplified by the volume of AI compute they can deploy.

The idea is already gaining traction across the industry; compute access is beginning to feature in hiring conversations, an early sign that AI capacity is becoming a competitive workplace resource, much like high-end hardware or proprietary software once were.

Underlying Huang’s argument is the rapid emergence of agentic AI systems that can operate independently, execute tasks, and iterate without constant human prompts.

That transition has profound implications for demand. Today, most computing remains intermittent; devices sit idle for large portions of the day. In an agent-driven environment, that idle time disappears. Systems run continuously, generating tokens in the background as they analyze data, write code, manage workflows, or simulate decisions.

The result is a shift from burst-based computing to persistent workloads, where demand for processing power becomes constant rather than cyclical. This, in turn, drives a surge in token consumption and places new pressure on infrastructure, energy supply, and cost efficiency.

Hardware Competition Becomes Cost-Per-Token Competition

For Nvidia, this vision aligns closely with its commercial strategy. If tokens become the currency of AI, then the key metric for hardware is no longer raw performance alone, but how cheaply and efficiently it can generate those tokens.

This is where Nvidia sees its advantage. By designing chips that deliver higher throughput at lower energy cost, the company positions itself as a supplier of token production capacity at scale.

The implications extend across the industry because cloud providers, chipmakers, and enterprise users will increasingly compete on cost-per-token economics, a metric that blends hardware performance, software optimization, and energy efficiency into a single benchmark.

The token model also introduces a new layer of financial complexity. As AI adoption accelerates, companies could face rising operational expenses tied directly to usage. In periods of intense activity—product launches, financial modelling cycles, or research bursts—token consumption could spike sharply.

Huang’s willingness to absorb high token costs during “crunch time” underpins a belief that productivity gains will outweigh inflationary pressures. But that balance is not guaranteed across all sectors. For lower-margin industries, sustained increases in token usage could compress profitability unless offset by efficiency gains or pricing power.

This dynamic mirrors earlier transitions in cloud computing, where initial cost savings were followed by runaway usage bills as companies scaled their operations.

Implications for the global AI race

At a macro level, the rise of token economics adds a new dimension to technological competition. Nations and corporations are no longer just competing on talent or algorithms, but on their ability to generate and afford large volumes of compute.

This has geopolitical implications. Access to advanced chips, energy resources, and data center infrastructure will directly influence a country’s capacity to produce tokens at scale, shaping its position in the AI hierarchy.

It also reinforces Nvidia’s central role in the ecosystem. As demand for token generation grows, so does reliance on the hardware and platforms that make it possible.

Perhaps the most consequential aspect of Huang’s argument is how it reframes productivity itself. In a token-driven economy, output is no longer limited by human bandwidth alone. It is constrained by how much compute can be deployed and how effectively it is used. Employees equipped with large token budgets and powerful AI agents could achieve levels of output previously unattainable, creating asymmetric productivity gains within organizations.

That raises new questions for management, from how to allocate compute resources to how to measure performance in environments where machines contribute significantly to output.

Huang’s vision is still emerging, but its contours are becoming clearer. AI is evolving into a utility-like layer of the economy, where tokens function as both the unit of production and the measure of value. If that model takes hold, the implications will ripple across industries—from how companies budget and hire to how nations compete in the global technology race.

Here are my thoughts on the decision by the Confederation of African Football (CAF) to take the tournament trophy from Senegal and award it to Morocco. Good People, there is no ambiguity here: CAF is wrong, completely and unequivocally.

The Confederation of African Football (CAF) has stripped Senegal of its Africa Cup of Nations title and awarded the championship to Morocco, following a dramatic and disputed final that has now escalated into a legal battle.

In a statement released Tuesday, CAF said Senegal had forfeited the match after players temporarily left the pitch in protest during the closing stages of the game. The governing body ruled that the result would be recorded as a 3-0 victory for Morocco, overturning Senegal’s 1-0 win secured after extra time.

The decision has triggered a sharp backlash from Senegal’s football authorities, who have vowed to challenge the ruling at the Court of Arbitration for Sport (CAS) in Lausanne.

I may not have been a great footballer, but back at Secondary Technical School, Ovim, I was the fan-in-chief among my classmates. Nicknamed “Sausa,” I analyzed games and offered commentary, even though I never made the team. One lesson our coach, Mr. Udeagu Snr, always emphasized was this: the referee holds the ultimate authority and must be respected if you do not want to lose the match.

In this case, the match was restarted and eventually completed, despite the earlier disruption. Yes, the Senegalese team may have left the pitch at some point, but they returned and finished the game. That is what matters.

The referee is, in effect, the supreme authority on the field, the final arbiter. Once the match was concluded under his authority, that result should stand. CAF’s appeal committee should recognize this and refrain from overturning what was decided on the pitch. All the rules should consider that the match was completed on the pitch.

Simply put, this trophy belongs to Senegal because the game was played to completion. We must avoid technicalities in our beautiful game.

I am Sausa,

ex-football strategist, Secondary Technical School Ovim Abia State

Google is pushing a new concept into the fast-evolving AI lexicon—“vibe designing”—as it deepens its challenge to traditional software and design tools with updates to its experimental Stitch platform.

Unveiled by Google Labs, the feature signals a shift from structured design workflows toward a more intent-driven approach, where users describe outcomes, emotions or business goals rather than manually building interfaces step by step.

The announcement immediately rattled incumbents. Shares of Figma, a dominant player in UI and UX design software, dropped sharply following the news, reflecting investor anxiety over how quickly AI could erode established software categories.

From “vibe coding” to “vibe designing”

The terminology builds on “vibe coding,” a trend that gained traction in 2025, where developers rely on AI to generate code based on high-level prompts. Google is extending that logic to design, effectively collapsing the gap between concept, interface design and front-end development.

With Stitch, users can generate high-fidelity UI layouts and production-ready front-end code using text, images, voice, and even conversational prompts. Instead of starting with wireframes or component libraries, the process begins with abstract inputs such as what a product should feel like or what outcome it should achieve.

In practice, that redefines design as a dialogue with an AI agent. The system can critique layouts in real time, suggest alternatives, and iterate instantly, allowing users to move from idea to working interface in minutes rather than days.

A direct challenge to design incumbents

The implications for companies like Figma are threatening. Their platforms are built around structured workflows—frames, layers, components, and collaborative editing. “Vibe designing” bypasses much of that structure, replacing it with prompt-driven generation. That does not necessarily eliminate the need for design tools, but it changes their role. Instead of being the primary environment for creation, they risk becoming refinement layers on top of AI-generated outputs.

Market reaction suggests investors are already pricing in that risk. The selloff in Figma shares confirms concerns that AI-native tools could compress margins and reduce switching costs across the industry.

What makes Stitch particularly disruptive is its ability to bridge design and engineering. By generating both UI layouts and front-end code, it erodes the traditional handoff between designers and developers.

This convergence has long been a friction point in software production. Misalignment between design intent and implementation often leads to delays and rework. AI-driven tools promise to eliminate that gap by producing designs that are immediately executable. Over time, this could lead to smaller, more agile teams where a single individual—or even a non-technical user—can handle tasks that previously required multiple specialists.

Google’s addition of voice interaction pushes the concept further. Users can speak directly to the system, request variations, and refine outputs in real time. The AI agent effectively becomes a creative collaborator, capable of interviewing users, interpreting intent, and generating alternatives on demand.

This interaction model reflects a broader shift toward agentic interfaces, where software is no longer static but actively participates in the creative process.

It also aligns with industry trends highlighted by leaders such as Jensen Huang and Sam Altman, who have both argued that AI will fundamentally change how software is built and used, even if it does not eliminate the need for software altogether.

Disruption fears—and pushback

The rapid advancement of tools like Stitch has intensified concerns about a potential “SaaSpocalypse”—a scenario in which AI displaces large segments of the software industry.

Huang has dismissed that view, arguing that AI will expand the market rather than destroy it. Altman has taken a more measured stance, suggesting that while software is not going away, the way it is created and consumed will change significantly.

From the perspective of incumbents, volatility may be part of the adjustment. Dylan Field, CEO of Figma, has argued that market turbulence can ultimately strengthen companies, forcing them to adapt and innovate.

At its core, “vibe designing” reflects a deeper transformation: the move from tool-centric workflows to outcome-centric creation. Instead of mastering complex interfaces, users define goals and let AI handle execution. That lowers the barrier to entry, enabling a broader range of people to build digital products.

However, it also raises new challenges. Ensuring consistency, maintaining brand identity, and managing complex systems may become harder when outputs are generated dynamically rather than constructed manually.

The bigger picture

Google’s push into AI-driven design is part of a broader effort to embed generative AI across the entire software lifecycle—from ideation to deployment. The idea is expected to accelerate a shift where software creation becomes faster, more accessible, and increasingly automated, while redefining the roles of designers and developers.

For now, “vibe designing” remains an emerging concept. But the reaction it has triggered—both in markets and across the industry—suggests that the battle over the future of software is moving beyond code and into the very process of creation itself.