Rapidly Evolving AI Technology Is Bringing Dramatic Change
Today, rapidly evolving AI technology is bringing dramatic change to our daily lives and business environments. The evolution of deepfake technology, algorithmic information manipulation, and the phenomenon known as "Work Slop" — AI-generated low-quality work — are all drawing attention as ripple effects of AI's commoditization. As deepfake precision improves and AI technology becomes freely and easily available, there is a deepening mix of concern and anticipation about the increasingly difficult task of distinguishing "genuine information" from fabricated content. For companies, investors, and general users alike, how we handle information — and what our working environments look like — are undergoing major transformation.
This article explores AI's commoditization and technological innovation, the nature of censorship on major platforms, and the impact of AI on work efficiency and quality, drawing on the latest debates and real-world examples across multiple angles. We examine how the frontier of the information revolution is affecting society and business, and what kind of future lies ahead.
- The Commoditization of AI and the Deepfake Revolution: The Benefits and Dangers of Technological Evolution
- The New Era of Social Media Censorship: Algorithmic Manipulation and the Crisis of Freedom
- The Trap of AI Adoption: The "Work Slop" Problem and How to Combat It
- Summary
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The Commoditization of AI and the Deepfake Revolution: The Benefits and Dangers of Technological Evolution
In recent years, AI technology's development has moved far beyond the realm of pure research — it has penetrated deeply into actual business settings and daily life. Particularly in the spotlight is AI's "commoditization" — the process by which AI becomes something people use without thinking about it, like cloud storage or compute resources. In one discussion, the view was raised that "if 50 to 70 percent of query results end up being essentially the same across major companies, consumers won't be able to tell which model they're using" — painting a vision of an AI-pervasive future similar to the evolution of deepfake technology and character animation.
Specifically, Alibaba's recently released "Wan 2.2 Animate 14B" model has attracted major attention. This model specializes in reproducing character animation and movement expression in videos of people. In demonstrations, a person replaced themselves with famous figures — Sidney Sweeney or Mark Zuckerberg, for example — showing how rapidly deepfake quality is improving. The technology involves video compositing and face replacement done in post-processing, so real-time performance still faces challenges at present. Within months to years, however, the gap is expected to narrow, with likely applications in live conversations and streaming.
Behind the advancement of deepfake technology lies the trend of AI commoditization — AI models increasingly delivering nearly identical results. What was once characterized by uniqueness, with each company developing differentiated models, has now reached a degree of maturity where multiple models flood the market and any of them will deliver a certain level of results. In domains like superintelligence tackling medical or scientific challenges, performance differences between models may still matter significantly. But for everyday information provision — how to cook delicious sushi rice, travel plan suggestions for a family trip — there may come a point where multiple AI models' outputs are essentially identical.
This technological evolution carries both the risks and the benefits of deepfakes simultaneously. As deepfake technology advances further, distinguishing whether video and audio is real or fabricated becomes extremely difficult, raising concerns about the ease of fabricating statements and footage of prominent people. There have already been reports of suspicious AI-generated videos involving politicians' and celebrities' statements from the past few months. In this environment, the importance of systems that guarantee authentic identity online — such as blockchain-based verification systems — is growing. Mechanisms that authenticate public figures' official accounts and guarantee that statements and footage are genuine will become increasingly necessary countermeasures.
Deepfake technology also brings revolutionary change not just to video production but to marketing and advertising. Companies will be able to rapidly produce more personalized promotional videos and custom messages using inexpensive deepfake technology. At the same time, traditional video production companies and advertising agencies will face new challenges in maintaining their quality and uniqueness. In this context, the emergence of free cutting-edge technology creates the real possibility of companies being drawn into price competition with rivals.
As a technical note on deepfakes, it has been reported that facing the camera directly improves model compositing accuracy. This is because consistent facial angle and movement makes it easier for AI to accurately capture the target person's features. In other words, shooting with the head still and looking directly at the camera is a basic condition for producing high-quality deepfake footage. The collaboration of filming technique and AI technology will undoubtedly give rise to even higher-precision content going forward.
This rapid evolution of the technology will be the catalyst for deepfakes to permeate not just commercial use but politics, journalism, entertainment, and numerous other fields. At the same time, however, technological progress carries the risk of blurring the boundary between "truth" and "misinformation." If a day comes when any footage can be easily created, everyone on social media will come to doubt the credibility of information, making the improvement of information literacy across society an urgent matter. Additionally, sufficient discussion is needed on the implications for privacy, copyright, and ethics. For example, if deepfake technology is misused to alter statements by public figures, there is a real risk of damage to individuals' reputations and credibility. Technology developers therefore need to implement measures to prevent misuse of the technology in parallel with establishing ethical norms and regulatory frameworks.
The New Era of Social Media Censorship: Algorithmic Manipulation and the Crisis of Freedom
As deepfake technology evolves and the degree of freedom in AI algorithms grows, debate about information control and censorship on various platforms is intensifying. Major video streaming services and social media platforms in the United States, for example, use algorithms to optimize users' feeds — but the extent to which these selection criteria are biased toward profit-seeking, creating inappropriate imbalances around political opinions and social issues, is a matter of serious concern. There have also been reports of one major company being pressured by government officials to delete certain user-generated content, continuing the debate over platforms' operational posture and algorithmic transparency.
In this context, California's proposed SB771 bill seeks to introduce a framework for fining social media platforms when algorithm-driven content recommendations have discriminatory or harmful effects on specific groups. The bill aims to be a strong deterrent against cases where algorithmic content bias harms specific groups — such as enabling the spread of antisemitism or other sensitive social issues. At the same time, critics have pointed out the risk that the bill's standards for what constitutes "harmful" are ambiguous, and that under political pressure or varying interpretations, it could end up suppressing opinions representing certain political viewpoints. Some Jewish organizations welcome the bill, viewing it as a step toward deterring online antisemitism, while pro-Arab and pro-Muslim organizations express concern that pro-Palestinian speech would be regulated instead.
YouTube has also had examples in the past of making content removal decisions for user-generated content under (unofficial) government pressure. This has made problems of information manipulation and censorship on platforms increasingly serious. For instance, documentation has surfaced suggesting that YouTube has sided with conservative voices publicly while actually removing inconvenient information under government pressure. Such cases reveal a significant gap between companies' public-facing policies and what actually happens in content management — creating a situation where users and citizens are confused about where "genuine free expression" ends and mechanical editing begins.
Furthermore, the way platform algorithms manipulate users' interests and operate to maximize advertising revenue is generating distrust among existing users. For example, there are cases where users are guided toward unhealthy content or controversial information in pursuit of advertising revenue, resulting in extreme polarization of users' information environments. In this context, companies are advocating for the importance of "giving users choices" — introducing mechanisms that allow users to select different algorithms themselves or turn attached filters on and off, which could contribute to building a healthier online environment.
Regarding the censorship problem, there are also growing expectations for a shift from traditional centralized platform operation toward distributed systems and online verification systems using blockchain technology. If verification systems become practical, information originating from public figures' official accounts would be reliably communicated to users as certified, legitimate information — making it possible to prevent the spread of fabrications and fake news.
Platform operators, governments, and users themselves need to seriously debate the boundaries of censorship and information management in this new era. Algorithms designed to maximize corporate revenue tend to recommend extreme content to capture user attention, and the result is the increased likelihood of unintended bias across society. The credibility and fairness of online information may consequently be undermined — and in the long run, this carries the risk of shaking the foundations of democracy itself.
To break through this situation, it is essential that platforms make the workings of their algorithms transparent and provide choices to users. Simultaneously, governments must move beyond mere formal pressure to establish specific standards and rules that support fair information distribution. These efforts, as they advance, will bring the problem of online censorship closer to resolution. The future of the information environment depends on technological innovation, transparency, and the wise judgment of users.
The Trap of AI Adoption: The "Work Slop" Problem and How to Combat It
According to recent surveys and research, while the use of AI tools is spreading rapidly, "Work Slop" — low-quality work product generated by AI — is beginning to be recognized as a problem in business processes. An MIT study found that 95% of AI pilot projects within companies actually failed to produce useful results. Harvard and Stanford research teams have also revealed that while AI-assisted work contributes to efficiency improvements, there are many cases where corrections and additional rework are required in later stages. For example, handwriting meeting notes was once considered effective for memory retention and deeper understanding of discussion content — but in recent years, AI-auto-generated notes are frequently submitted instead, raising questions about the accuracy and reliability of information.
This phenomenon is not merely a matter of efficiency — it also adversely affects trust relationships and cooperative attitudes within organizations. Colleagues and managers who receive low-quality work product frequently feel the output has not been sufficiently reviewed, and there are many reports of declining evaluations of each other's capabilities. In internal communications as well, over-reliance on AI can become a factor that diminishes individual thinking and creativity. Some companies have introduced the practice of having each meeting participant bring their own handwritten notes to management meetings, successfully improving information sharing and discussion quality among employees. But the concern is that as AI-generated output continues to proliferate, these human processes will increasingly be undervalued.
Meanwhile, particularly at startups, there is a growing trend of conducting pilot projects with customers before a product is fully complete. When an early-stage company implements a pilot project with a large enterprise or multi-location client, it is essential to precisely agree on the "definition of success" upfront and to negotiate clear rules with both parties about how to transition to full deployment after the trial period ends. Without clear criteria, customers may demand condition changes after the trial period, or pricing negotiations can become difficult. To prevent such situations, companies are advised to document specific outcome metrics and post-implementation schedules in the pilot project agreement and incorporate them into the contract.
Furthermore, as AI tools improve business processes, the risk of qualitative deterioration in the work itself is becoming apparent. A situation where many employees, by delegating portions of their work to AI, find their individual creativity and judgment undermined can inhibit innovation across the entire organization. For example, if meeting notes and reports are batch-generated by AI, and as a result each person loses the opportunity to think for themselves, this may become a major barrier to future operational improvement and the generation of new ideas. At one major enterprise, "Work Slop" — criticism of AI auto-generated content — has been reported, with enormous time and effort being spent on post-hoc corrections.
For companies seeking to improve efficiency through AI adoption, what matters is not just "using the technology" but "properly maintaining the quality of output." When this is not achieved, the result is a serious decline in overall organizational productivity and the breakdown of trust relationships between employees. More important than ever is the reconstruction of internal communication processes. For example, having all attendees use handwritten notes for discussion in management meetings — incorporating traditional analog methods — is cited as an effective countermeasure against the efficiency decline AI can cause.
In addition, employees' mutual evaluation and trust relationships are closely connected to these challenges. When low-quality work product continues, people lose the motivation to cooperate with each other, and the overall team morale declines. Therefore, when implementing AI tools, companies simultaneously need to put in place mechanisms that complement traditional work quality and human judgment. The experience of success from pilot projects, and employees themselves experiencing the improvement process, leads to both organizational trust recovery and operational efficiency.
Summary
This article has examined in detail the deepfake revolution accompanying the rapid evolution of AI technology, the problem of censorship on online platforms, and the phenomenon of declining work quality through AI adoption — the so-called "Work Slop." As discussed in each section, AI's commoditization represents the process by which once-groundbreaking technology transforms into a commonly available tool. Users gain easy access to high-precision footage and information, but lurking behind this is the risk of the boundary between truth and misinformation becoming blurred. Furthermore, the problems of algorithmic censorship on various platforms can be a major factor threatening the credibility and fairness of information, while simultaneously highlighting the need for users to have their own judgment capabilities and verification systems.
Enjoying the benefits of technological innovation while not neglecting risk management will contribute to the sustainable development of future corporate management and society as a whole. Each company, and indeed policymakers, must advance efforts toward algorithmic operations that ensure transparency and fairness, and toward building deeper trust relationships among employees — prerequisites for responsible technology utilization in the future. Going forward, we must continue to monitor the latest information and technology trends as AI's evolution advances on both the information environment and work efficiency fronts.
Reference: https://www.youtube.com/watch?v=KYopltO1UNU
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