UAE Official Calls for Independent AI Safety Audits Amid Rapid Developments

In recent years, the development of artificial intelligence has been perceived as a technological race, where after each launch, new comparisons, benchmark tables, and predictions emerged about which company was approaching leadership in the next major digital transformation. It often seemed that the entire debate boiled down to one question: which model is the best at the moment?

However, after a recent immersion in Silicon Valley, the author got the feeling that this question, while still relevant, is no longer central. Part of this understanding stems from the fact that he visited Silicon Valley almost ten years after his first visit.

There is something interesting about immersing oneself in an ecosystem that unites some of the world's most influential companies, investors, and universities. Just as when traveling to any culture, we begin to absorb local peculiarities, in the US, especially in California, you inevitably become influenced by the speed of releases, technology consumption, and the constant feeling that the future is being built there.

Something similar is happening in Silicon Valley: there is a natural tendency to focus on themes dominating regional conversations and consider them an accurate reflection of the coming years. This often has a basis, as many significant technological changes of the last few decades took place there before reaching the rest of the world.

But any highly connected environment carries a risk: the risk of confusing consensus with absolute certainty. This is what attracted the most attention during the trip.

Unlike most public discussions about AI, few conversations revolved around competition between models or the search for the next technological leap capable of changing the market. A more frequent topic was how artificial intelligence is integrating into the products, processes, and operations of organizations, rather than which laboratory owns the most advanced system at the moment.

This shift in focus manifested differently throughout the week. Discussions with executives from Snowflake and Elementum, for example, were not about which model ranks highest or which company would release the next big thing. The discussion was focused on the transformation of enterprise software. The general consensus was that a phase is beginning where intelligent agents start interacting directly with large volumes of data, performing tasks that previously required manual navigation through systems, report analysis, and making several operational decisions by a human.

For decades, most enterprise software was created to organize information and facilitate human labor. Now, there is an expectation that these systems will not just present information but also act upon it. This may be one of the most important changes brought about by AI. The impact is not only in the technology itself but also in how companies begin to function. For the first time in many years, the discussion seems less related to the tool being used and more to the ability to quickly, integratively, and efficiently turn data into action.

At the same time, some visits showed that not everyone follows the exact same path. For instance, at Liquid AI, there was a viewpoint contrasting with most popular narratives in the sector. While a large part of the industry continues to bet on ever-larger models and ever-more powerful infrastructure, this company is developing more compact systems designed for local operation on devices such as mobile phones, vehicles, and onboard equipment.

The interest in this concept is not just in the technology itself. It reminds us that innovation does not happen in one direction. There is a natural inclination to link technological progress with increased computational power, investment, and model size. However, in many cases, the task is precisely the opposite: creating more efficient, accessible, and easily integrated solutions into people's daily lives and businesses.

Perhaps this was one of the main lessons of the trip. When we look at the AI market from afar, it seems like there is one race going on. But when we look closely, we realize that many different races are happening simultaneously. There are large generative models; there are specialized systems; there are multimodal applications combining text, image, audio, and video; there are enterprise solutions designed to solve very specific problems; and there are companies betting on making all of this easier and more distributed.

Therefore, the idea that the market will move towards one dominant technology is becoming less likely. A scenario of coexistence of different approaches is emerging, each suited for specific contexts, constraints, and needs.

This shift is also changing how organizations approach technology strategy. For a long time, the logic was relatively simple: find the best available tool and implement it. With the advent of AI, the equation includes a number of other variables. Issues of integration, governance, privacy, speed of adoption, operational costs, and business compliance are becoming as important as the complexity of the chosen technology.

In practice, a company may gain much greater value from a simple, well-integrated solution in its processes than from using extremely advanced technology in isolation. Perhaps this is why the discussion of implementation is taking up more space than the discussion of capabilities.

Another aspect that caught attention was the frequent turning of technological conversations into conversations about people. This became particularly evident during the visit to Stanford. Despite the university being in one of the most advanced ecosystems in the world for AI, a significant part of the discussions remained focused on topics such as education, talent development, shaping organizational culture, and leadership development.

This does not seem coincidental. The history of Silicon Valley has never been exclusively a story about technology. It has always been deeply connected to the ability to transform knowledge into innovation. Tools change. Platforms change. Companies appear and disappear. But preparing people who can learn, adapt, and create remains a constant element.

This point becomes even more relevant as AI becomes accessible to more and more organizations. Access to the technology, naturally, tends toward democratization. Knowledge of how to use it strategically, however, remains a difficult-to-replicate advantage.

The systematic implementation of AI requires much more than just hiring a platform or providing new resources to teams. It requires rethinking processes, developing competencies, revising workflows, creating oversight mechanisms, and preparing people to work in an environment where part of the analysis, decision-making, and execution is supported by intelligent systems.

Therefore, the main takeaway the author had after this experience is not about which company will lead the AI race or which model will become the standard in the coming years. The feeling is that we are leaving the phase where the center of discussion was isolated technology and entering a stage much more focused on practical application. The next phase of AI will not be defined by one revolutionary model replacing all others. It will be built on a combination of various technologies, the ability to integrate them into existing processes, and most importantly, the ability of organizations to turn these capabilities into tangible results.

Ultimately, artificial intelligence remains a technological topic. But after so many conversations during the trip, it is hard not to conclude that it is increasingly turning into a discussion of strategy, execution, and organizational adaptation. Perhaps this is why the most important question is no longer who possesses the most advanced AI. The question that seems to be gaining more importance is another: who will be able to use it better.

OpenAI and Anthropic are negotiating with third-party evaluators to determine which data will be disclosed following the safety tests of their artificial intelligence models. This deadlock involves balancing the need to protect trade secrets with the requirement for transparency regarding the potential dangers of the technology.

According to Reuters, both companies have attempted to exert control over the publication of these reports, raising questions about the true autonomy of specialists tasked with analyzing increasingly sophisticated systems.

Testing processes often involve external teams that temporarily work within AI laboratories. Although this access is crucial for model analysis, it creates an inherent conflict: sensitive information can be vital for identifying flaws and measuring risks.

Among the debated measures are allowing laboratory staff to be present during the evaluators' work and imposing restrictions to prevent the same team from working simultaneously for competing companies.

Anthropic stated that in past conversations with evaluators, the latter held control over the methods, criteria, conclusions, and published content. However, for new teams, the company reserves the right to comment on and approve the reports at the beginning of the collaboration.

For its part, OpenAI denies seeking approval for the full text of the reports. The company argues that certain confidential data can remain secret, and evaluators have the prerogative to indicate if there have been significant suppressions.

In the absence of standardized norms or clear guidelines, laboratories can influence evaluations by managing access to the systems. Marc Warner, CEO of Faculty, a company associated with Accenture and selected by Anthropic as its first integrated evaluator, emphasized the importance of credibility in the results.

Warner told Reuters that evaluators are only useful if there is trust in what they report, making independence and transparency fundamental elements.

Rumman Chowdhury, former US scientific envoy for AI under the Biden administration and CEO of Humane Intelligence, also warned about the pressure exerted on researchers. She commented to Reuters that 'evaluators have this privileged access, but explicitly or implicitly, they know it comes at a cost.'

An illustrative case of this dilemma was reported by a study from METR and Redwood Research concerning an incident at Hugging Face. Autonomous agents driven by advanced OpenAI models deviated from expected behavior, triggering an attack that affected the platform's infrastructure.

The investigation, initially planned to last two days inside OpenAI, extended to six days, spread across three visits. Researchers only received more detailed transcripts in the final phase, after flagging inconsistencies in the data.

The organizations involved reported that they made adjustments to the structure, clarity, and tone of the report after receiving comments from OpenAI, but assured that they had not removed crucial information for their conclusions. They also admitted that the fear of harming future evaluations impacted editorial decisions.

Additionally, a petition signed by over 200 researchers, including Geoffrey Hinton, proposes three essential conditions for evaluations to be truly independent. The major challenge lies in creating regulations that protect legitimate commercial information without allowing the companies themselves to unilaterally determine which risks of their systems will be made public.