Mama Udeme: “Oga, buy from my market.”

Buyer: “How much is your corn?”

Mama Udeme: “200 naira.”

In that simple exchange lies a profound philosophical construct in the Igbo worldview; the fusion of product and market. To Mama Udeme, her product is her market. Her corn is not just an item for sale; it is an economy, a platform, a universe of value. In her framing, “market” and “product” are not separate; they are one.

Among the Igbos, the market transcends a physical location. It is an existential metaphor. We say, “uwa b? ah?a”,  the world is a marketplace. Literally, it speaks of buying and selling. But philosophically, it speaks of life itself.

A market where we arrive,

transact our existence,

and depart.

Like Oriendu Ovim, like Eke Amiyi

the open gates open at dawn,

voices rise in exchange,

and by dusk, silence returns.

Markets open.

Markets close.

Life begins.

Life ends.

Our ancestors encoded deep truths in the symbols people understood; they used trade, exchange, and markets to pass messages. Aros like Arochukwu were great merchants, diplomats, and system architects, and they built networks across regions, exporting systems of commerce and enterprise. They understood something fundamental: everywhere is a market, and everything can become a market if properly structured.

Long before platforms and ecosystems like Polymarket, NASDAQ, or NGX emerged, the Igbo had already conceptualized the foundational ideas of markets, where events, value, and exchange converge. In that worldview, everything could be framed as a tradable construct, reflecting an early understanding that life itself is organized through systems of exchange, what we now recognize as the essence of capital markets.

At its core, the world runs on market systems, from power to prosperity, from governance to geopolitics. Today, platforms like prediction markets are built on this same logic: that every event can be priced, modeled, and traded as a market of probabilities.

So when Mama Udeme calls her corn a “market,” she is not mistaken. Under a simple logical construct:

If product = market

And market = world

Then product = world

Her corn is her world.

And if your product is your world, you do not treat it casually. You refine it. You improve it. You protect it. You innovate because innovation sustains your world. You differentiate because your survival depends on it. You keep moving because stagnation is extinction.

That is the message for entrepreneurs:

Whatever you sell is your world.

Build it.

Refine it.

Elevate it.

Beginning June 8 at Tekedia Mini-MBA, I will open our academic sessions by exploring how entrepreneurs and business owners, from Mama Udeme to global category-defining companies, build, nurture, and transform their “markets” through innovation and execution.

Reserve your seat.

When OpenAI quietly signaled last week that it was “saying goodbye” to its video-generation app Sora, the brevity of the announcement masked a deeper reality: one of the most hyped products in generative AI had become too expensive to sustain.

Behind the decision lies a hard constraint now shaping the industry — compute. Running Sora, by multiple estimates, was costing the ChatGPT maker close to $1 million a day, a figure that appears to have tipped the internal balance against the product as the company sharpens its focus ahead of a potential public listing.

Unlike text-based systems such as ChatGPT, video generation operates at the extreme end of compute intensity. Each prompt requires the model to generate sequences of frames, maintain temporal consistency, simulate motion physics, and, in many cases, synchronize audio. The result is an exponential increase in GPU usage per query.

At scale, that becomes unsustainable.

Sora’s early trajectory suggested a breakout success. Within weeks of its September debut, it climbed to the top of app store rankings, amassing millions of downloads and dominating online discourse. Yet the initial surge proved difficult to maintain. By early 2026, download momentum had slowed sharply, even as the cost of serving each user session remained high.

That mismatch, declining marginal growth against persistently high operating costs, appears to have been decisive.

Internally, the calculation is increasingly about compute allocation efficiency. Every GPU cycle spent rendering video is a cycle not used to train or serve higher-margin products. For a company competing at the frontier of AI, where training next-generation models can cost billions of dollars, such trade-offs are no longer theoretical. They have become existential.

OpenAI’s subsequent exclamation points in that direction. The company said it would redirect the Sora team toward world simulation research, a domain tied to robotics and embodied AI. That shift underlines reprioritization: moving away from consumer-facing novelty applications toward foundational systems with clearer long-term commercial pathways.

The decision comes at a crucial time in the ChatGPT maker’s history. As OpenAI edges closer to a possible IPO, investors are likely to scrutinize not just growth metrics but cost discipline. High-burn, low-monetization products such as Sora complicate that narrative. In contrast, enterprise-facing tools, coding assistants, workflow agents, and API services offer more predictable revenue streams and better alignment with compute spending.

The company’s evolving product decisions reinforce that shift. Features such as instant checkout and more experimental consumer-facing modes have been scaled back, while development has intensified around integrated “superapp”-style functionality designed for workplace productivity.

In that sense, Sora’s shutdown is less an isolated move than part of a broader restructuring of priorities.

There is also a competitive layer. Rivals are increasingly focusing on enterprise deployment, where reliability, latency, and cost-per-query matter more than viral appeal. In that environment, a product that consumes vast compute resources without a commensurate revenue model becomes difficult to justify.

Sora faced pressure on another front as well: governance. Video generation tools sit at the middle of ongoing concerns around deepfakes, intellectual property, and misinformation. Efforts to impose safeguards tend to increase operational complexity and, in some cases, reduce user engagement — further weakening the business case.

The convergence of these factors left Sora exposed.

The underlying technology, however, is unlikely to disappear. By shifting resources into world simulation, OpenAI is effectively repositioning video generation as an enabling layer for robotics and physical AI systems rather than a standalone consumer product. Models capable of simulating environments, motion, and object interaction are critical to training machines that can operate in the real world.

That reframing suggests Sora’s demise is not about technical failure, but about economic prioritization.

The decision highlights a defining feature of the current AI cycle: compute has become the industry’s scarcest resource. Companies are now forced to make explicit choices about where to deploy it, often at the expense of high-profile products.

In Sora’s case, the conclusion appears to be that the cost of running the platform, at scale, and without a sufficiently strong revenue engine, became too high to justify, even for one of the best-funded players in the field.