Artificial Intelligence 2025: Between Bubble, Reality, and Prospects
L'artificial intelligence in 2025 it went from media triumph to a collective examination of conscience.’artificial intelligence Is it still sustainable, or are we in the midst of a speculative bubble that risks bursting? The question has become central to investors, companies, and institutions, sparking a fierce debate between technological promises and concrete returns.
Over the course of 2025, after years of almost unreserved enthusiasm, the tech community began to question whether the capital injected into the AI ecosystem was truly generating the expected value. The debate erupted particularly in August, when analysts and observers began openly speaking of a bubble, shifting attention from spectacular demos to the economic and operational sustainability of the projects.
Artificial Intelligence and the Speculative Bubble: The Investment Problem
The main issue concerns the unprecedented size of investments in artificial intelligence. Stargate has raised $500 billion with the involvement of SoftBank, Oracle has put $300 billion on the table, while Nvidia and OpenAI have signed deals worth $100 billion each. These figures, taken individually, would already be exceptional, but taken together they describe a financial ecosystem hyper-focused on generative AI.
These astronomical numbers, however, clash with a technological reality that is slower and more complex than expected. The much-heralded AGI, the artificial general intelligence that some executives believed should arrive "tomorrow," is now more realistically projected to arrive around 2035. The gap between storytelling and actual system capabilities creates a trust gap that impacts markets, companies, and public opinion.
The question of return on investment is becoming critical. Many companies have spent millions on solutions artificial intelligence Without being able to clearly measure the benefits. How, for example, do you quantify the qualitative advantage of an employee who works better thanks to a 24/7 AI assistant? And how do you justify million-dollar budgets if tangible productivity improvements are slow to arrive.
This doesn't just affect individual companies. A significant portion of US pension funds are tied to the big tech stocks that lead the S&P 500 index. If the AI ecosystem proves to be overinflated, the fallout could indirectly affect the savings of millions of people around the world, including investors in technology ETFs.
Artificial Intelligence Models in 2025: Expectations and Limitations
On the models front artificial intelligence, 2025 didn't live up to all its promises. GPT-5, released at the end of the summer, was expected to be a game-changer, but it turned out to be an incremental update. Many analysts have observed that the version numbers seem, in part, to be marketing tools rather than indicators of true technological paradigm shifts.
More interesting, in some respects, was the arrival of Google's Gemini 3 Pro. This model introduced a more dynamic graphical interface and advanced image generation capabilities, offering a richer, more multimodal experience. But the real game-changer was its training on Google's proprietary hardware, which eroded Nvidia's near-monopoly on specialized AI GPUs.
This move has opened a new chapter in the infrastructure competition of the’artificial intelligence, with the entry of players developing integrated vertical stacks, from chip to model. At the same time, lighter, more verticalized alternatives are emerging, such as Mistral in Europe and DeepSeek in China, designed for specific applications with much lower entry costs.
The technological landscape is further complicated by the lack of shared metrics. Each company defines its own benchmarks, choosing parameters that highlight the strengths of its model: some prioritize accuracy, others the certainty of responses, others speed or inference costs. For the end user, it becomes difficult to objectively compare different solutions. artificial intelligence present on the market.
For a neutral overview of the concept of AI, it is useful to refer to Wikipedia on artificial intelligence. For macroeconomic implications, it is also worth following reports from institutions such as the’OECD on AI, which analyze impacts on productivity, work and competitiveness.
Geopolitics, regulation, and artificial intelligence architectures
The trajectory of the’artificial intelligence It's driven not only by technical factors, but also by political and regulatory choices. In the United States, the new administration has pushed for near-total deregulation, with a very clear implicit message: "Do what you want, just do it." The goal is to maximize the speed of innovation and consolidate the leadership of national champions.
China adopts a different but equally growth-oriented approach: freedom to innovate, as long as it aligns with the Communist Party's directives. This creates an environment where startups and large players can experiment with business models. artificial intelligence very powerful, but always under precise political control.
Europe, however, is struggling with the AI Act, intended to be a global regulatory beacon, but is currently perceived as an uncertain instrument. The slowdown is due to the differing agendas of the 27 member states and the influence of their respective industry lobbies. The risk is that it finds itself in an intermediate position: regulations not clear enough to provide certainty, but still sufficient to slow adoption compared to the US and China.
From a technical standpoint, the Transformer architecture, the basis of all major chatbots, is showing its first structural limitations. Google has presented a paper on so-called "Numeric Learning," an attempt to transcend the current paradigm with new forms of training and generalization. At the same time, hardware is diversifying, reducing dependence on a single vendor and opening up space for specialized chips for specific use cases.
To better understand how regulation can impact the’artificial intelligence, it is also useful to monitor the initiatives of the European Commission on AI, which outline obligations, risks and standards for operators.
Artificial Intelligence: Expectations, Results, and Sustainability
A significant part of the disappointment surrounding the’artificial intelligence in 2025 was born from unrealistic expectations. Many expected revolutionary scientific discoveries, new laws of physics, or definitive cures for complex diseases. In reality, beyond notable advances in specific fields—such as chip design or protein structure prediction with AlphaFold—we have yet to see revolutions comparable to those promised in the most enthusiastic narratives.
This gap between narrative and results fuels doubts about the sustainability of the entire AI ecosystem. If companies fail to demonstrate concrete and measurable value, the market risks drastically revising the valuations of the companies most exposed to it.’artificial intelligence, with cascading effects on stock indices, technology ETFs and global investment portfolios.
2026 looks set to be a year of truth. It will be necessary to demonstrate that the’artificial intelligence It can generate real benefits for business and society, with clear impacts on revenues, costs, efficiency, and quality of life. This will require a new balance between innovation and responsibility, between freedom of development and protections for consumers and investors, between visionary narrative and technological realism.
The challenge is also cultural. It means accepting that AI is a powerful but imperfect tool, useful but not miraculous, promising but not yet as mature as many would like to believe. For companies, this means rethinking their adoption strategies.’artificial intelligence, shifting the focus from current trends to concrete and measurable use cases.
Artificial Intelligence: Impact on Marketing and Business
For digital marketing, the’artificial intelligence It remains an extraordinary accelerator, provided it is used with clear objectives and precise metrics. Companies can leverage AI to personalize content, optimize campaigns, segment audiences, and improve customer experiences at scale, but they must abandon the idea that simply "putting it in a model" will automatically boost sales.
In this scenario, the priority is no longer to demonstrate that the’artificial intelligence It works in theory, but generates value in the real funnel: more qualified leads, better conversion rates, lower acquisition costs, higher retention. Marketing teams must integrate AI into their workflows, measuring the impact of chatbots, recommendation systems, predictive scoring, and automation on clear business KPIs.
Customer communication is one of the most promising areas. Thanks to the’artificial intelligence, businesses can build virtual assistants on channels like WhatsApp Business that can handle simple inquiries, pre-qualify leads, and offer immediate 24/7 support. This frees up human team time to focus on higher-value conversations, increasing customer satisfaction and internal efficiency.
At the same time, AI enables more coherent omnichannel strategies. Data from websites, social media, email, and messaging can be analyzed to identify behavioral patterns and activate relevant automatic triggers. In a context where privacy is paramount, adopting AI-enabled solutions is crucial. artificial intelligence that comply with regulations and guidelines, especially for those operating in Europe.
How SendApp Can Help with Artificial Intelligence
In this complex context, the key is not to chase the latest technology artificial intelligence, but strategically integrate it into customer communication processes. SendApp was created specifically to help companies transform WhatsApp Business into a truly measurable marketing, sales, and support channel, leveraging AI where it delivers concrete value.
With SendApp Official, businesses can access the official WhatsApp APIs and connect their own templates artificial intelligence or conversational assistants to messaging flows. This allows you to manage notifications, campaigns, chatbots, and transactional conversations at scale, while maintaining reliability and compliance with Meta policies.
SendApp Agent It allows you to organize the work of teams that manage chats and tickets, combining AI automations and human intervention. For example, you can use the’artificial intelligence to pre-answer frequently asked questions, categorize requests, suggest responses to support staff, and route conversations to the right agent, reducing handling time and customer friction.
For those who want to take automation to the next level, SendApp Cloud It offers a scalable infrastructure for advanced integrations. Companies can connect CRM, e-commerce, payment systems, and search engines. artificial intelligence to create automated journeys on WhatsApp: from lead generation to nurturing, all the way to post-sales and proactive support.
Thanks to the APIs and automation features, it is possible to build workflows in which the’artificial intelligence It analyzes intent, history, and business data to deliver the right message at the right time, while SendApp ensures delivery, tracking, and operational channel management. This way, companies transform AI from a costly experiment into a concrete growth driver.
For companies that want to bring the’artificial intelligence For marketing, sales, and support on WhatsApp Business, the next step is clear: consider a platform that combines official APIs, team management, and cloud automation. Visit SendApp and request a consultation or a free trial to understand how to pragmatically apply AI to your customer conversations.
