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The challenges and dilemmas of technology giants and AI agent innovators

The challenges and dilemmas of technology giants and AI agent innovators

BlockBeatsBlockBeats2024/08/20 02:52
By:BlockBeats

In the future, we may see a convergence of Web2 and Web3 approaches, leveraging the strengths of each to create more powerful and versatile AI agents.

Original title: "IOSG Weekly Brief|Tech Giants and AI Agent Innovators' Challenges and Dilemmas #239"
Original source: IOSG Ventures


1. The Prospects of Centralized AI Agents


AI agents have the potential to revolutionize the way we interact with the Internet and perform tasks online. While there has been a lot of discussion around AI agents that leverage cryptocurrency payment rails, it is important to recognize that established Web 2.0 companies are also well positioned to offer comprehensive agent (AI agent) product suites.


Agents from Web2 companies mostly appear in the form of assistants or vertical tools with only weak execution capabilities. This is due to both the immaturity of the underlying model and regulatory uncertainty. Today's agents are still in the first stage. They can do well in specific areas, but have little generalization capabilities. For example, Alibaba International has an agent that specifically helps merchants reply to emails about credit card disputes. A very simple agent, which calls up data such as shipping records and sends them according to templates, has a high success rate that allows credit card companies not to deduct money.


Tech giants like Apple and Google, and AI expert companies like OpenAI or Anthropic, seem particularly well suited to explore the synergy of developing agent systems. Apple's advantage lies in its consumer device ecosystem, which can serve as a host for AI models and a portal for user interaction. The company's Apple Pay system can enable agents to facilitate secure online payments. Google, with its massive web data index and ability to provide real-time embedding, can provide agents with unprecedented access to information. At the same time, AI powerhouses like OpenAI and Anthropic can focus on developing specialized models that can handle complex tasks and manage financial transactions. In addition to Web2 giants, there are also a large number of startups in the United States that make such agents, such as helping dentists manage appointments or assisting in the generation of post-treatment reports, which are very detailed scenarios.


However, these Web 2.0 giants face the classic Innovator's dilemma. Despite their technological prowess and market dominance, they must navigate the treacherous waters of disruptive innovation. Developing truly autonomous agents represents a significant departure from their established business models. Furthermore, the unpredictable nature of AI, combined with the high stakes of financial transactions and user trust, presents significant challenges.


2. The Innovator’s Dilemma: The Challenges Faced by Centralized Providers


The Innovator’s Dilemma describes the paradox that successful companies often have difficulty adopting new technologies or business models, even when these innovations are critical to long-term growth. At the heart of the problem is the reluctance of existing companies to introduce new products or technologies whose initial user experience may not be as sophisticated as their existing products. These companies fear that adopting such innovations may alienate their current customer base, who have become accustomed to a certain level of sophistication and reliability. This hesitation stems from the risk of disrupting users’ long-cultivated expectations.


2.1 Agent Unpredictability and User Trust


Large tech companies like Google, Apple, and Microsoft have built their empires on proven technologies and business models. Introducing fully autonomous agents represents a significant departure from these established norms. These agents, especially in their early stages, will inevitably be imperfect and unpredictable. The non-deterministic nature of AI models means that there is always a risk of unexpected behavior, even after extensive testing.


For these companies, the stakes are very high. A misstep could not only damage their reputation, but also expose them to significant legal and financial risk. This creates a strong incentive for them to tread carefully, potentially missing out on first-mover advantage in the agent space.


For centralized providers considering deploying agents, the risk of customer protests is very high. Unlike startups that can pivot quickly and without much to lose, established tech giants have millions of users who expect consistent, reliable service. Any major misstep by an agent could result in a PR nightmare.


Consider a scenario where an agent makes a series of poor financial decisions on behalf of a user. The resulting outcry could erode years of carefully built trust. Users might question not only the agent, but all of the company’s AI-based services.


2.2 Ambiguous Evaluation Criteria and Regulatory Challenges


Further complicating the issue is how to evaluate what is a “correct” agent response. In many cases, it’s unclear whether an agent’s response was truly incorrect or simply an accident. This gray area can lead to disputes that further damage customer relationships.


Perhaps the most daunting hurdle facing centralized agent providers is the evolving and complex regulatory landscape. As these agents become more autonomous and handle increasingly sensitive tasks, they enter a regulatory gray area that can present significant challenges.


Financial regulation is particularly tricky. If an agent makes financial decisions or executes trades on behalf of a user, it may be regulated by financial regulators. Moreover, compliance requirements may be extensive and vary significantly across jurisdictions.


There is also the question of liability. If an agent makes a decision that results in financial loss or other harm to a user, who should be held responsible? The user? The company? The AI itself? These are questions that regulators and lawmakers are just beginning to grapple with.


2.3 Model bias can be a source of controversy


In addition, as agents become more sophisticated, they may run afoul of antitrust regulations. If a company’s agents consistently favor that company’s own products or services, this could be seen as anticompetitive behavior. This is particularly relevant for tech giants, which are already under scrutiny for their market dominance.


The unpredictable nature of AI models adds another layer of complexity to these regulatory challenges. It is difficult for Web2 to ensure compliance when it cannot fully predict or control the behavior of AI. This unpredictability may slow down innovation in Web2 agents, as companies need to deal with these complex issues, which may give advantages to more flexible Web3 solutions.


3. Opportunities of Web3


As the capabilities of the underlying LLM model improve, agents have the opportunity to enter the next form, with relatively high autonomy. At present, it seems unlikely that large companies will dare to touch this aspect, and helping users order a pizza may be the limit. Startups may be bold, but they will face many technical obstacles. For example, the agent itself does not have an identity, and any operation requires the identity and account of the agent user. Even if the identity is borrowed, it is not easy for the traditional system to support the agent to operate freely. Web3 technology provides unique opportunities for the development of artificial intelligence agents, which may solve some challenges faced by centralized providers. Under the Web3 system, agents can realize multiple DIDs by mastering wallets. Whether it is payment through encryption or the use of various permissionless protocols, it is very friendly to agents. When agents begin to engage in complex economic behaviors, there is a high probability that agents will interact with each other intensively. If there is no way to resolve the mutual suspicion between agents at this time, the agent economic system is not a complete economic system. This is also an aspect that can be solved using encryption technology.


In addition, Crypto-economic incentives can promote agent discovery and provide a penalty that can be reduced or confiscated if the agent behaves improperly. This creates a self-regulating system in which good behavior is rewarded and bad behavior is punished, thereby potentially reducing the need for centralized supervision and providing a degree of peace of mind for those who are early adopters of entrusting financial transactions to fully autonomous agents.


Crypto-economic staking serves a dual purpose, requiring slashing when misbehaving, while also serving as a key market signal in the agent discovery process. The intuition is simple, both for other agents and for people looking for a specific service, that the more stake there is, the more trust the market has in a particular agent’s performance, and the more peace of mind users will have. This could create a more dynamic and responsive agent ecosystem, where the most effective and trustworthy agents naturally rise to the top.


Web3 also enables the creation of open agent markets. These markets allow for a greater degree of experimentation and innovation than would be possible with trusting centralized providers. Startups and independent developers can contribute to the ecosystem, potentially leading to faster advancement and professionalization of agents.


In addition, distributed networks like Grass and OpenLayer can provide agents with access to both open internet data and closed information that requires authentication. This broad access to diverse data sources could enable Web3 agents to make more informed decisions and provide more comprehensive services.


Web 2.0 vs Web 3.0

The challenges and dilemmas of technology giants and AI agent innovators image 0


4. Limitations and Challenges of Web3 AI Agents


4.1 Limited Adoption of Crypto Payments


This post would not be complete without reflecting on some of the adoption challenges that Web 3.0 agents will face. The elephant in the room is that adoption of cryptocurrencies as off-chain economic payment solutions is still limited. Currently, only a handful of online platforms accept crypto payments, which limits the practical use cases of crypto-based agents in the real economy. Without deep integration of crypto payment solutions with the broader economy, the impact of Web 3.0 agents will continue to be limited.


4.2 Transaction Size


Another challenge is the size of typical online consumer transactions. Many of these transactions involve relatively small amounts of money, which may not be large enough to justify a trustless system for most users. The average consumer may not see the value of using a decentralized agent for small, everyday purchases if a centralized alternative exists.


5. Conclusion


The reluctance of tech companies to offer fully autonomous AI agents due to the unpredictability of non-deterministic models creates an opportunity for crypto startups that can leverage open markets and crypto-economic security to bridge the gap between agent potential and actual implementation.


By leveraging Blockchain technology and smart contracts, crypto AI agents may offer a level of transparency and security that is difficult for centralized systems to match. This may be particularly attractive for use cases that require a high degree of trust or involve sensitive information.


In summary, while both Web2 and Web3 technologies offer avenues for AI agent development, each approach has its own unique advantages and challenges. The future of AI agents may depend on how these technologies can be effectively combined and refined to create reliable, trustworthy, and helpful digital assistants. As the field develops, we may see a convergence of Web2 and Web3 approaches, leveraging the strengths of each to create more powerful and versatile AI agents.


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Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.

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