Monday, August 3, 2026

Among today's hot news, there are actually only two things that deserve most attention: one is the "intelligence-to-price" war on large models, and the other is the accelerated implementation of the AI content production chain. Let's start with DeepSeek V4‑Flash. The model is only a few hundred megabytes in size, but it can run a throughput of 30‑40 tokens/s on a single card. Five tasks can be completed with the official price tag of 3 yuan. Compared with OpenAI and Anthropic's computing power of US$10 - 20 per hour, the difference is directly of the order of magnitude. For ordinary developers, this low-threshold model immediately moves "playing AI" from scientific research laboratories to in-house companies and even personal projects. The biggest pitfalls in implementation are that the security aspects of the model are still catching up, issues such as prompt injection and privacy leaks have not been packaged by the security team of the big factory. Engineers have to write their own protective code, and the debugging cost is not low. At the commercial level, the essence of price wars is to push costs to a few cents per thousand tokens. Only companies that can maintain profits on large-scale calls will continue to invest computing power. DeepSeek's "low price + good enough" solution will force American giants to cut prices, and may even prompt them to position higher-priced models in higher-end vertical fields (such as medicine and finance), otherwise they will only become "high-priced toys." The risk of this wave of competition is that if the price is too low, it will lead to a decline in model quality, increase errors and illusions, and ultimately cause users to lose trust.

Let's look at the full-link acceleration of AI content production: from AI short dramas to seize the path of real-life dramas, to Codex's automatic generation of PPTs, to the implementation of the internal Agent evaluation system of startups, the entire ecosystem is moving from "AI can write" to "AI can deliver" transformation. Technically, the Agent evaluation framework and the combination of SDD+TDD have become standard in some companies, which can catch errors in advance and reduce rework costs when requirements are ambiguous. The trap is that this system often relies on a large amount of annotated data and self-developed evaluation scripts. If there is no universal template, the team will have to build it themselves, and the investment cost in a short period of time cannot be underestimated. For ordinary developers, if they only want to use Codex to do PPT, the ready-made five-step method is enough, but to truly let AI be responsible for business processes in the project, they still have to learn to write Tips, do evaluations, and build monitoring-this is not a "just click" thing.

Overall, the AI price war is downgrading models from "high-end scientific research tools" to "consumer-grade computing power", which is good for startups, but it will also raise the hidden costs of safety and quality. The acceleration of the content production chain has further lowered the threshold for creators, but the ensuing engineering burden and quality control needs are also rising. The risk is that too much price pressure will lead to an increase in model error rates, or companies ignore safety compliance when pursuing speed, which may eventually hit regulatory red lines. Smart teams will add a self-developed security layer to low-cost models and use SDD/TDD to lock in quality; companies that rely on low prices to seize the market are likely to be eliminated in the next round of competition.

The price war for big models has finally reached Silicon Valley, and the landmark significance of this matter goes far beyond the parameters themselves. When DeepSeek v4 flash pushed the cost of calling to the floor, OpenAI and Anthropic had to follow suit and prove the cost performance, indicating that the industry's competition axis has completely shifted from "whose model is the smartest" to "whose unit is the cheapest intelligence." Cursor quietly removed cost information from the usage page, which just exposed the anxiety of the AI tool layer-the model is devaluing, but if the middle layer of packaging the model cannot prove that it is worth the difference, it will be bypassed. Some people in the developer community are looking around for cheaper DeepSeek access methods. Some people are asking why open source authors don't use AI to accelerate development. These fragments put together point to the same conclusion: when intelligence itself is almost free, what's really scarce is the ability to embed intelligence into real business flows, not the right to call APIs.

The shock wave brought by this commercialization is spreading to all walks of life. Tongji University's abolition of long-term appointments and the promotion of all employees is a reform of the university's personnel system. However, in the context of AI, the essence is that the organization is using "quantifiable output" to redefine the existence value of each position. Academia is no longer an ivory tower. It is facing the same force as the film and television industry-AI short dramas can already generate content without saying hello to actors, and the living space of graduate students and waist actors is squeezed from both ends. When "enough" content can be produced by machines at one-tenth of the cost, human practitioners are no longer required to "know how to do it" but to "do it better than AI enough to be worth paying a premium." This threshold is rising every year.

But commercialization also has its own backlash. A Flutter developer made a running App using AI, and there were only a few downloads online. AI has lowered the development threshold to the floor, but the distribution threshold and application value have not been reduced. On the contrary, because everyone can do it, homogeneous products are flooded faster. This means that the popularity of AI tools does not automatically lead to business success, it just converts the question of "can you do it" into the question of "value is not worth doing." Half a year from now, we'll see a lot of AI-generated apps, content, and code flooding the market, but the ones that really survive are players who have domain understanding, channel advantage, or relationship networks superimposed on top of AI. Intelligence is free, judgment begins to charge.

The release of DeepSeek V4 and its "Pro" version, coupled with the price war between OpenAI and Anthropic, is undoubtedly the most popular focus in the AI field. From a data point of view, the "power" of DeepSeek V4 flash has been pushed to fourth place on the Zhihu hot list, while the "big model price war hits Silicon Valley" ranks first on WeChat hot articles. The information revealed behind this is far more than just an iteration of technical performance, but also a profound change in business logic.

In the past, the competition for AI models mainly focused on "who has the highest running score", which is the upper limit of technical capabilities. But now, as Agents begin to truly integrate into the workflow, the criteria for selecting models for companies are rapidly shifting to "who can complete the same task at a lower cost." The emergence of DeepSeek V4 flash and its announced upcoming "Pro" version capture this pain point. The statement that it was mentioned that it might "cause all three American companies to close down" is exaggerated, but it reflects the market's expectations and concerns about this "cost-effective"-driven competitive landscape. The essence of the price war is to lower the threshold for AI applications and make it affordable for more small and medium-sized enterprises and individual developers, thereby accelerating the popularization and commercialization of AI. But the question is, when price becomes the main driving force, will the long-term R & D investment, safety, and ethical issues of models be sacrificed? As industry pioneers, will the "American Royal Three" miss the opportunity because they stick to their original models like some technology giants did during the Internet bubble, or will they find new profit points to deal with this price war?

Another phenomenon worthy of attention is that the penetration of AI in the field of content creation is accelerating. The discussion of AI short dramas replacing real-life dramas, as well as the "five-step approach" of using AI (such as Codex) to make PPTs, indicate that AI is gradually evolving from an auxiliary tool to a main body of content production. The second WeChat hot article,"AI short dramas replace real-life dramas and never say hello to actors" and the Chinese developer community,"It seems that the author of open source projects does not know/does not want to use AI to accelerate his own development?" The discussions all point to the same trend: AI is reshaping the cost structure and efficiency of content production and may have disruptive effects on traditional practitioners. The dilemma of AI writing code faster, more rework has also been mentioned in the Chinese developer community. This shows that while AI is accelerating, it also poses new challenges to requirements understanding and quality control. The practical sharing of "Five-Step PPT Using Codx" represents that another group of people are actively embracing AI and looking for ways to improve efficiency.

These phenomena are connected together to paint a picture of accelerating AI commercialization and constantly expanding application scenarios. However, we cannot just see the bright side. Behind the "power" of DeepSeek V4 flash is the massive investment in computing power and data training. How can the cost be absorbed? Will the price war really benefit everyone, or will it create a new monopoly? Will the rise of AI short dramas further squeeze the space for real-life creation and lead to content homogenization? The discussion of "How to let the CEO remember your output when joining an AI startup" also exposes individuals 'anxiety about how to increase value through AI in a rapidly changing market. Historical technology bubbles are often accompanied by excessive optimism and neglect of potential risks. At a time when the wave of AI is sweeping, keeping a clear mind and paying attention to the sustainability of its technology, the health of its business model and its impact on all aspects of society are issues that every participant needs to think about.