1.1 The double paradox of data elements

In the process of AI technology evolving to general artificial intelligence (AGI), the triangle paradigm composed of "data-algorithm-computing power" is facing structural challenges. According to IDC's "2024 Global Artificial Intelligence Infrastructure Semi-annual Tracking Report", the global artificial intelligence infrastructure market is ushering in unprecedented growth, and it is expected that related expenditures will exceed the US$100 billion benchmark by 2028. Among them, the multimodal data generated by user terminal devices has become the core incremental source, but the traditional centralized data acquisition model exposes two core contradictions:

1.1.1 Economic Dilemma of Data Ownership Imbalance

The current "data colonialism" model dominated by Internet platforms has formed a deformed value distribution system. The "2025 Artificial Intelligence Index Report" released by Stanford University's Human-Oriented Artificial Intelligence Institute (HAI) pointed out that global users generate trillions of data interactions per day, but the economic returns obtained through advertising sharing, data authorization, etc. only account for a very small proportion of the total value of data assets. This one-way value extraction mechanism is triggering a double crisis:

• Compliance cost index rises

Since the implementation of the EU GDPR, data governance has entered the "deep waters of strong supervision". Forrester's "2024 Cybersecurity Threat Forecast Report" shows that global technology companies have paid tens of billions of dollars in cumulative fines for data violations, of which Amazon was fined 746 million euros by Luxembourg authorities for cookie tracking issues. What’s even more serious is that the proportion of compliance expenditure in corporate IT budgets soared from 9% in 2021 to 23% in 2024, forming a vicious cycle of "compliance investment swallowing innovation capital."

• User engagement continues to decline

Stanford HAI found through a million-level sample experiment that when users clearly know that data is used commercially, their active sharing behavior has decreased by 62%, the scene richness in the data quality dimension has decreased by 34%, and the labeling accuracy has decreased by 28%. Research at the University of Cambridge further revealed that the reduction in user's willingness to contribute data is causing structural deterioration of AI training data: when users learn that data is used for commercial training, the entropy value (information richness) of their data contribution decreased by 58%, the proportion of deliberately created data noise soared to 29%, and the temporal continuity fracture of multimodal data caused a 14 percentage point decrease in the accuracy of behavioral prediction models.

This negative cycle forces companies to move towards alternatives. Anthropic invested tens of millions of dollars to build a synthetic data factory to train the Claude 3.7 Sonnet model, but the model bias problem brought by synthetic data increased the cost of ethical review by 40%.

1.1.2 Technology locking effect of computing power concentration

When model complexity enters an exponential growth stage, computing power monopoly is killing innovation diversity. According to Synergy Research Group data, AWS, Azure, and Google Cloud account for 67% of the global AI computing power market share through full-stack control of "chip-cloud services-framework" (AWS 32%, Azure 25%, GCP 10%). This ecological imbalance is manifested as:

• Implicit technology bundling: Although the PyTorch framework does not force the integration of AWS S3, cloud service providers have formed factual dependencies by optimizing their own storage service performance; the compatibility differences between TensorFlow Lite for non-Google Cloud services have led to performance fluctuations in some scenarios.

• Innovation Eco-Plate Completion: GitHub’s annual report shows that in 2023, only 17% of open source AI projects can complete the entire process from prototype to deployment, a decrease of 22 percentage points from 2020. In-depth interviews found that 78% of developers were forced to give up cutting-edge directions such as reinforcement learning and multimodal fusion because they could not obtain stable computing power support, and instead developed "micro-innovation" applications with low technical thresholds. The cost of computing power oligopoly has been concrete: migrating a medium-sized NLP model from AWS to the private cloud requires refactoring 37% of the code and 42% of the performance losses. Synced's 2024 survey data shows that hardware expenditure accounts for as much as 76% of the single model training costs of small and medium-sized developers, and the training cost of typical image generation model exceeds the US$2.3 million threshold, blocking 90% of innovation teams from the track of technology evolution.