An AI vs AI Combat

A new world is taking shape, the era of AI which is happen to be a huge business:

• The global AI market is worth almost $300 billion.
• The AI market is set to hit $1.77 trillion by 2032.

https://explodingtopics.com/blog/ai-market-size-stats#projections

The new hype is the agentic AI, that has made its way everywhere.

What is it all about, briefly?

Here is an AI summary about agentic AI:

“Agentic AI refers to autonomous AI systems that can independently plan and execute tasks to achieve a goal with minimal human oversight. Unlike traditional AI that requires step-by-step guidance, agentic AI uses advanced reasoning, such as that from large language models (LLMs), to make decisions and adapt in real-time to complex problems. Examples include AI systems that can manage an employee’s vacation request or handle IT support tickets. 

Key characteristics

• Autonomy: Agentic AI operates independently, making decisions and taking actions without constant human direction.
• Goal-oriented: These systems are designed to achieve specific, pre-determined goals.
• Adaptability: Agentic AI can adapt in real-time and handle complex, multi-step problems that may not have been explicitly progr

Important considerations

• Maturity: The field is still developing, and it is important to distinguish genuinely agentic systems from those marketed as such (sometimes called “agent washing”).
• Implementation: Successful deployment requires more than just AI, including robust engineering discipline, data management, and monitoring.
• Governance: Robust governance and analytics are needed to ensure the AI operates in a controlled and safe manner.” 

Everyone understands the simple case in the example (to manage an employee’s vacation): you have to organize a trip—with flights, accommodation, car rental, sightseeing, etc. – while considering factors like available budget, time frame, maximizing different target values, like value/cost efficiency, and so on. This case is relatively simple because a human formulates a task for the AI assistant, which then examines various scenarios, taking into account what is necessary, presents the feasible scenarios, offers options for decisions, iterates—sometimes multiple times—and then produces the version to be implemented and – finally – organizes everything.

The case, however, that you can read about below in the MIT article, is very different and much more complex and exciting, and this will be the real challenge for all of us.

In the following scenario, AI agents face each other. One has a purchasing task, the other a selling task. Narrowly speaking, the research is (only )about price negotiation, but the players have competed in different sales areas (selling-buying of electronics, motor vehicles, real estate), both as sellers and buyers.

The details can be read in the referenced MIT article, which I have read several times.

Here are the most important points highlighted:

“… access to more advanced AI models —those with greater reasoning ability, better training data, and more parameters—could lead to consistently better financial deals, potentially widening the gap between people with greater resources and technical access and those without. If agent-to-agent interactions become the norm, disparities in AI capabilities could quietly deepen existing inequalities.“

“Over time, this could create a digital divide where your financial outcomes are shaped less by your negotiating skill and more by the strength of your AI proxy,” says Jiaxin Pei, a postdoc researcher at Stanford University and one of the authors of the study.”

“One notable pattern was that some agents often failed to close deals but effectively maximize profit in the sales they did make, while others completed more negotiations but settled for lower margins. GPT-4.1 and DeepSeek R1 struck the best balance, achieving both solid profits and high completion rates.

Beyond financial losses, the researchers found that AI agents could get stuck in prolonged negotiation loops without reaching an agreement—or end talks prematurely, even when instructed to push for the best possible deal. Even the most capable models were prone to these failures. The result was very surprising to us,” says Pei. “We all believe LLMs are pretty good these days, but they can be untrustworthy in high-stakes scenarios.”

“This study is part of a growing body of research warning about the risks of deploying AI agents in real-world financial decision-making. Earlier this month, a group of researchers from multiple universities argued that LLM agents should be evaluated primarily on the basis of their risk profiles, not just their peak performance.”

For now, Pei advises consumers to treat AI shopping assistants as helpful tools—not stand-ins for humans in decision-making.

All the details:

https://www.technologyreview.com/2025/06/17/1118910/ai-price-negotiation/?utm_source=the_download&utm_medium=email&utm_campaign=the_download.unpaid.engagement&utm_term=&utm_content=11-06-2025&mc_cid=c073fdedea&mc_eid=d55319adcd

For now, this is where we stand.

AI has enormous business potential.

The development is ongoing, and it’s certain that even now, AI agents are already negotiating simpler procurement tasks (like purchasing A4 paper), but it’s clear that AI solutions will quickly move up the complexity scale.

What comes next?

For example, managing smaller operational human teams.

Then comes the AI mid-management.

And so on.

I am not a pessimist, I am a realist because we all know what insatiable an appetite for profit Capital has.