News: A New Wave of Low-Cost AI, Just as Markets Remember 2025
As of mid-February 2026, speculation is rising across AI communities and investment circles about DeepSeek’s next release. The central expectation is that DeepSeek may unveil its next-generation model, often referred to as “DeepSeek-V4,” around the Lunar New Year period.
At this stage, the model’s technical details remain unconfirmed. What is clearer, however, is the broader industry trend: Chinese AI companies are accelerating the release of low-cost, increasingly capable models, and the timing suggests a cluster of announcements rather than a single isolated event.
This matters because markets still remember early 2025. The first “DeepSeek shock” was not primarily about benchmark performance. It was about cost structure. It challenged the assumption that frontier-level AI inevitably requires ever-growing capital expenditure in GPUs, data centers, and power.
The concern in 2026 is similar in nature. It is not “How smart is V4?” but “How far can costs fall, and what happens to the profit models built on expensive intelligence?”
Analysis: The Real Story Is Not Specs, but the Collapse of Key Premises
If V4 delivers meaningful cost improvements, the impact will not be limited to a single stock or a single quarter. It could accelerate a structural shift already underway.
Below are three long-term changes that investors and business leaders should watch beyond short-term volatility.
1. Sovereign AI Faces a Classic Policy Trap
Sovereign AI is increasingly vulnerable to rapid commoditization.
Over the past few years, many governments have invested heavily in domestic LLMs and compute infrastructure under the banner of national security and economic resilience. These are, in practice, national infrastructure projects.
The problem is timing. If global models become dramatically cheaper and “good enough” for most commercial use cases, domestic projects risk becoming high-cost solutions to yesterday’s problem. By the time they are operational, the global baseline may have moved further ahead.
The most dangerous part is political inertia. Private companies can pivot or cut losses. Governments often cannot. They may keep funding underperforming projects to avoid admitting failure, creating a long-term gap where:
- the private sector adopts cheaper global models, while
- public institutions remain locked into expensive domestic systems.
This is not only a technology issue. It is a budget and legitimacy issue.
2. AI Could Accelerate Global Fragmentation, Not Globalization
Low-cost AI is likely to spread fastest where budgets are tight.
Top-tier US models remain extremely powerful, but they also come with higher subscription costs, stricter compliance policies, and in many cases USD-based payments. For many startups and engineers in emerging markets, those constraints matter.
If Chinese providers offer capable models at significantly lower cost, adoption in emerging economies becomes economically rational, even if the models are not always “best in class.”
Over time, this may accelerate a bifurcation of the AI ecosystem:
- A Western sphere: higher cost, higher compliance, more regulation
- A cost-driven sphere: lower cost, faster implementation, looser constraints
For investors, the implication is simple but uncomfortable: the total addressable market for US AI giants may not be “the world.” It may increasingly be “the regulated world.”
3. The Profit Pool Shifts from “Building AI” to “Using AI”
The biggest long-term winners may not be model builders.
From 2023 to 2025, markets rewarded the “pick-and-shovel” layer: chips, cloud infrastructure, and data center buildouts. Companies like NVIDIA became symbols of the AI boom.
But commoditization changes the equation. When intelligence becomes cheaper, the margin shifts away from supplying intelligence and toward applying it.
The next winners are likely to be companies that can:
- embed AI into workflows and cut operating costs
- automate high-friction processes in legacy industries
- capture distribution and the “last mile” of user interaction
- demonstrate measurable ROI rather than theoretical potential
In other words, the next phase is less about who owns the smartest model, and more about who can turn cheap intelligence into durable cash flow.
Conclusion: The AI Boom Enters Its Post-Magic Phase
The current DeepSeek discussion is not a panic. It is a re-pricing of assumptions.
We are watching AI move from a “magical luxury product” to a utility. When that happens, hype-driven bubbles often deflate. But the real economic impact begins after the transition, not before it.
Electricity changed the world not when it was a novelty, but when it became boring infrastructure. AI may follow the same pattern.
For 2026 and beyond, the key question is no longer:
“Who has the smartest model?”
It is:
“Who has a business model that still works when intelligence becomes cheap?”
References
- A year on from DeepSeek shock, get set for flurry of low-cost Chinese AI models (Reuters)
- China’s DeepSeek sparks AI market rout (Reuters)
- Market Rumors on X regarding DeepSeek V4 Specs
