The AI evolves towards hybrid and open source architectures, combining specialized agents, finops and security for a more efficient and controlled future.
In a world in perpetual digital transformation, artificial intelligence (AI) is an essential strategic asset. However, behind its innovation potential hide considerable challenges: costs explosion, security vulnerabilities and integration complexity into existing systems.
LLM-RL hybrid architectures: to smarter and economical AI
The large models of language (LLM) are constantly growing in size and power, but this frantic race shows its limits in terms of costs and energy efficiency. An alternative emerges: hybrid architectures combining LLM and strengthening learning (RL), which allow models to self-improve without depending on excessive gross power.
Concrete examples:
- QWEN QWQ-32B (Alibaba): This hybrid model is based on a multi-stage RL, surpassing much more massive models while reducing operating costs by 30 %. A logistics company integrated it to optimize its routes, reducing its operational expenses by 15 % in six months.
- DIGIGL (https://digirl-agent.github.io/): This experimental agent has demonstrated a success rate of 62.7 % on complex Android tasks, surpassing traditional models. A software publisher uses it to automate its mobile application tests, would accelerate its 40 %development cycles.
These advances show that the performance of AI is no longer based solely on the size of the models, but on their ability to learn efficiently.
The rise of specialized agents: democratization and security risks
Specialized AI agents gain popularity thanks to marketplaces and simplified interfaces, facilitating their adoption by companies. However, this democratization raises important security challenges.
Adoption examples:
- Salesforce Agentxchange offers a market place of more than 200 specialized agents optimized for sales, marketing and finance.
- Opera Operator, integrated into the Opera browser, reduces the processing time for customer requests by 30 % for an e-merchant.
- The MCP (Model Context Protocol) protocol standardizes interactions between agents and models, simplifying their deployment.
However, this presents a growing threat according to Gartner, by 2028, 25 % of security vulnerabilities could come from malicious IA agents. Solutions like Trism (Trust, Risk, and Security Management) become essential to anticipate these threats.
Hardware optimization: need faster and less expensive IA inference
The evolution of the material is crucial to accompany the rise of AI. Recent innovations improve the effectiveness of infrastructure without weighing down costs.
New actors:
- Fractile, a promising specialized chip, would make an inference 100 times faster than conventional GPUs and reduces energy consumption by 20 times.
- Grok.com offers a faster and less costly IA inference than traditional solutions, allowing companies to optimize their calculation charges.
Software optimization also plays a key role, with techniques such as Pruning and Quantification, which reduce the size of the models without compromising their precision.
Open Source: a challenge for LLM owners
While the LLM owners dominate the market, the open source represents an increasingly competitive alternative. Deepseek, Qwen and Llama compete today with leaders like GPT-4, offering more transparent and accessible solutions.
- Deepseek has established itself as a credible alternative to closed models, with advanced performance in natural language treatment.
- Qwen (Alibaba) relies on a modular architecture, facilitating its adaptation to the specific needs of companies.
- LLAMA (META) offers a good compromise between power and accessibility, allowing companies to deploy AI without depending on a single player.
This rise of open source pushes companies to reconsider their dependence on proprietary solutions and to integrate more flexible solutions.
Finops: a necessity to control the costs of the AI
With exploding AI costs, Finops stands out as a strategic response. According to Gartner, without strict control, budget overruns can reach 1000 %.
Example: a logistics company has reduced its IA costs by 25 % in one year by adopting Finops, optimizing its resources in real time and reducing energy waste.
Conclusion: a future between opportunities and vigilance
Companies that will succeed in AI will be those that have been able to optimize their infrastructure, secure their agents and master their costs. According to a Gartner study, by 2027, 60 % of large organizations will adopt solutions such as Finops to better control these financial issues.
In parallel, the open source shakes up the proprietary models like those of Openai, with increasingly efficient alternatives such as Deepseek, Qwen and Llama.
The question remains: are our information systems today ready for this revolution?
