Three years have passed since ChatGPT ignited a global AI boom, and a subtle yet profound shift is underway in the industry’s collective sentiment. At the recent World Economic Forum in Davos, the tide has turned: the excitement and optimism surrounding AI a year ago have given way to demands for tangible results. Companies are seeking better return on investment (ROI); the market is questioning how much value the soaring capital expenditures and energy consumption from the computing power race actually deliver; and ordinary people are worried about AI’s impact on employment. Mark Benedetti, Executive Managing Director of private equity firm Ardian, stated that if AI drives more than 3% productivity growth for the US GDP over the next five to ten years, the current valuations of tech blue chips including NVIDIA are not just reasonable, but actually quite cheap. However, if the growth is only 1%, their valuations may indeed be overstretched.

 

On January 15, ahead of the Davos Forum, Donald Trump made a public statement that he would never allow American citizens to bear the burden of higher electricity bills due to data center expansion, and tech giants must foot the bill for their own energy consumption. Brad Smith, Vice Chairman and President of Microsoft, noted at a forum that data centers are in fact a political and economic issue. While they may create jobs, "what people really care about is: who will get these jobs? Will electricity bills go up? Will water pressure drop when you take a shower? These are all legitimate questions."

 

According to the Gartner Hype Cycle for AI Technologies released in July 2025, AI Agents are in the Peak of Inflated Expectations with conceptual positioning, while Generative AI has moved past this phase and entered the Trough of Disillusionment, as enterprises gain a deeper understanding of its potential and limitations. Despite the industry’s shift toward pragmatism, capital investment has not slowed down. For example, businesses are using AI image generation tools to mass-produce advertising materials, and even optimizing e-commerce product pages through the prompt engineering features of Nano Banana 2, in an attempt to strike a balance between cost and effectiveness. In 2025, total global venture capital investment in the AI sector reached an all-time high of $211 billion, with global AI spending approaching $1.5 trillion; this figure is projected to surpass $2 trillion in 2026.

 

Whether this massive investment can secure a sustainable future is the core question plaguing the AI industry. In the author’s view, the most indicative barometer remains the commercialization progress of leading AI enterprises. Since ChatGPT’s viral rise, OpenAI’s Annual Recurring Revenue (ARR) has seen exponential growth, surging from $2 billion to $6 billion and now exceeding $20 billion. According to previously disclosed internal financial documents from OpenAI, the company’s projected operating losses in 2025 will hit a staggering $9 billion, with concurrent revenue of only $13 billion and a cash burn rate of 70%. Over the next two years, although its loss scale will adjust slightly, the cash burn rate will remain at around 57% of revenue. It is forecast that OpenAI’s annual operating losses will skyrocket to approximately $74 billion in 2028, accounting for three-quarters of its annual revenue that year. This enormous deficit stems primarily from large-scale investments in computing resources, including chips, data centers and infrastructure development. By 2029, OpenAI’s cumulative cash burn is expected to reach $115 billion.

 

Nonetheless, the company remains optimistic about its profit prospects: the latest forecasts project its annual revenue to hit $200 billion by 2030, with positive cash flow to be achieved as early as 2029 and no later than 2030. In contrast, the financial trajectory of its rival Anthropic points to greater profit potential. Over the past three years, Anthropic’s revenue has grown tenfold annually, rising from zero in 2023 to $10 billion in 2025, and the company is expected to break even for the first time in 2028. In summary, even Anthropic, which appears to have stronger profitability, will not likely achieve break-even until at least 2028. The critical question is: do the capital raised by various AI startups suffice to sustain them until 2028 or even 2030? As it stands, neither OpenAI nor Anthropic has enough cash on hand, and barring unforeseen circumstances, both will need to seek further funding in the future.

 

Will GEO Be the Endgame for AI? With traditional monetization models hitting a wall, the industry has turned its attention to Generative Engine Optimization (GEO). Elon Musk recently posted on X that he will officially open-source the platform’s latest content recommendation algorithm within a week. He stated that the open-source release will cover "all code used to determine which organic and ad content is recommended to users", and emphasized that this is only the first step. Going forward, the code will be updated every four weeks, accompanied by developer documentation that details algorithmic and logical changes. The market has widely interpreted this move as a sign that "Musk is set to enter the GEO space", sparking a speculative frenzy around GEO as a result. Some even argue that "GEO is the endgame for AI".

 

At present, however, GEO is still in the conceptual stage. A nascent form of GEO involves leveraging a platform’s proprietary data to intelligently guide consumer behavior through AI interactive interfaces. In other words, AI is being used to deliver marketing content, rather than AI applications themselves achieving commercial monetization. In the era of traditional search engines, paid ranking was all about ad placement; today, AI marketing and GEO optimization hinge on information source credibility. When AI tools activate "web search" or "deep thinking" modes, some display the cited information sources. Marketers repeatedly test these citation paths to identify which websites and content formats AI is more likely to crawl, then conduct targeted optimization around these findings. The core is not to place direct ads on AI platforms, but to influence the data sources AI accesses and cites through a large volume of curated content.

 

Most users have grown accustomed to using large model applications for information retrieval and content curation, and many now feel that such content is already saturated with marketing material. Whether this stems from issues inherited from traditional search engines or the effects of GEO optimization services, training data contamination is an inherent flaw that the AI industry must confront on its path to commercialization. Thus, the fundamental challenge for the promotion of AI marketing lies in trust: if users realize that AI-generated search suggestions and content are rife with marketing information, will AI repeat the trust crisis that plagued traditional search engines due to their paid ranking systems?

 

After a frenzy of investment, the global AI industry is still grappling with finding a viable commercial path. From the massive losses of leading players to the exploration of new avenues like GEO, the industry is in search of a sustainable business model. Yet, whether it is the old playbook of burning cash for growth or the new GEO strategies that risk compromising user experience, balancing expansion, profitability and trust has become a pressing challenge that all AI enterprises must address. GEO may not be the endgame for AI, but the road to commercial success will undoubtedly require a monetization pathway that does not erode the core value of products. Only when the industry abandons the myth of growth to mask its profitability struggles will AI truly emerge from the bubble and create tangible value for the world.