AI Industry | Hidden Cost-Cutting Measures and Pressure to Save in Generative AI – Including Shrinking Word Count: Less is Cheaper
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Published on: September 28, 2025 / Updated on: September 28, 2025 – Author: Konrad Wolfenstein
AI Industry | Hidden Cost-Cutting Measures and Pressure to Save in Generative AI – Including Shrinking Word Count: Less is Cheaper – Image: Xpert.Digital
The hidden cost crisis among providers of generative artificial intelligence
The great AI illusion: Billions in losses and cost-cutting measures – what the tech giants are hiding from you
Artificial intelligence is experiencing an unprecedented boom, but behind the glittering facade of this technological revolution lie massive financial challenges. What users perceive as minor technical adjustments, upon closer inspection, turn out to be desperate attempts by major AI providers to control their exploding costs. In September 2024, Google and OpenAI made changes almost simultaneously that, at first glance, appear unrelated, but reveal a larger problem facing the entire industry: the monetization of AI services is lagging dramatically behind the massive investments and operating costs.
Generative AI (Generative Artificial Intelligence) is a subfield of artificial intelligence that specializes in generating new content – rather than just analyzing or classifying existing data.
This development is particularly noteworthy because it demonstrates how even the multi-billion-dollar tech giants are suffering under the financial pressures of their own innovation. While OpenAI, despite a valuation of $157 billion, is projecting losses of $5 billion for 2024, Google is quietly implementing measures that drastically increase the costs of data collection and processing. These seemingly minor changes have far-reaching consequences for the entire digital landscape and point to a larger structural crisis in the AI industry.
Google's silent revolution
Disabling the num=100 parameter
On September 14, 2024, Google implemented one of the most significant changes to its search infrastructure in years, without much fanfare. The URL parameter num=100, which for decades allowed the simultaneous display of 100 search results, was completely disabled. This technical change had far-reaching consequences for the entire SEO industry and AI applications.
While the parameter was a convenience feature for regular users, it was of vital importance to the SEO industry. Virtually all major SEO providers, such as Ahrefs, Sistrix, SEMrush, and specialized ranking tools, used this parameter for efficient data collection. With a single request, they could capture the complete top 100 rankings, which was both more cost-effective and faster than retrieving the results page by page.
Disabling this parameter led to a cost explosion in the SEO industry. Ranking tools now have to perform ten separate queries to obtain the same amount of data that was previously possible with a single request. This represents a tenfold increase in query costs and has already presented several tool providers with serious technical and financial challenges.
Impact on website operators
The effects were immediately visible in Google Search Console: 87.7 percent of all websites examined recorded a drastic decrease in measured impressions. Paradoxically, at the same time, the average position of the websites improved, as fewer "low-ranking impressions" from position 11 to 100 were recorded.
Many publishers are reporting dramatic drops in traffic. News sites like derwesten.de lost 51 percent of their search traffic, express.de 35 percent, and focus.de 33 percent. The reasons are multifaceted: In addition to technical changes at Google, AI-generated reviews also play a role, leading users to click less on external websites.
Motivation behind Google's strategy
The reasons for Google's decision are multifaceted. On the one hand, the change significantly reduces server load, as less data needs to be processed per request. On the other hand, it makes it more difficult for bots and scraping services to collect data on a massive scale, which is particularly relevant given the increased interest of AI companies in Google's data.
Another aspect is user experience: Google wants to steer users back towards the classic search experience, where they click through multiple pages and spend more time within the Google environment. This strengthens Google's position as a central source of information and makes the company less dependent on redirecting users to external websites.
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OpenAI's desperate cost control: The dramatic decline in citations
In parallel with Google's change, OpenAI implemented an equally significant change to ChatGPT. Since September 11, 2024, the AI chatbot displays considerably fewer source citations and references to external websites. This reduction affected all industries and content types equally, regardless of content quality or the domain authority of the sources.
The figures are dramatic: According to analyses, citations in ChatGPT have dropped by up to 90 percent. Free ChatGPT accounts are particularly affected, while users of paid versions experience significantly fewer limitations. This development exacerbates the existing discrepancy between the traffic websites receive from Google and the considerably lower traffic from ChatGPT.
The explosive cost increase in web search features
OpenAI is under enormous financial pressure. The company anticipates losses of around five billion US dollars for 2024, while operating ChatGPT costs up to 700,000 US dollars per day. Reducing web searches and citations is an obvious cost-cutting measure, as each web search requires additional computing resources and API calls.
The cost of OpenAI's web search feature has increased significantly. While earlier models offered free access to web searches, newer models charge full price for search tokens. An example illustrates the cost dilemma: A query using GPT-40 cost $0.13, while the same query using GPT-5 with the more comprehensive web search tokens cost $74.
For its current models with internet search capabilities, OpenAI charges $25 per 1,000 requests for gpt-4o and gpt-4.1, while the more powerful models like GPT-5 and the o-series cost as much as $10 per 1,000 requests. This pricing clearly illustrates why OpenAI is making drastic cost savings in the provision of web-based information.
The shrinking word count: Less is cheaper
In addition to the reduction in source citations, users have noticed another, more subtle change: ChatGPT's responses have become noticeably shorter and less detailed. What might initially appear to be an optimization for greater conciseness is, in reality, another effective cost-saving measure. Every generated word—or, more precisely, every token—consumes computing power and thus incurs direct costs. By systematically shortening the responses, OpenAI lowers the operating costs per request.
This trend becomes particularly clear when compared directly with competitors like Anthropic's Claude or Google's Gemini. These models often continue to deliver more detailed, nuanced, and in-depth answers to the same queries. While the competition still sometimes relies on a wealth of detail as a quality criterion, OpenAI seems to be deliberately reducing the word count to manage the financial burden of its immense user base.
For users, this means additional effort. Instead of a comprehensive answer, they often receive only a superficial summary and have to extract the desired level of detail themselves through targeted follow-up questions (so-called "prompt chaining"). Each of these follow-up inquiries is a new transaction, which, while cheaper individually, costs the user more time and effort. This measure fits seamlessly into the strategy of reduced citations: Both represent a deterioration of the user experience, which, in aggregate, are intended to lead to significant cost savings and offset the company's massive financial deficit.
Shorter answers, fewer sources: Do you also notice how ChatGPT is secretly cutting costs?
The loss-making business of premium subscriptions
What's particularly problematic is that even the more expensive ChatGPT Pro subscription, at $200 per month, is incurring losses because users are utilizing more services than anticipated. CEO Sam Altman described the situation as "crazy," thus confirming the challenges of covering costs.
OpenAI CEO Sam Altman admitted that the company is currently losing money on its $200 subscription: “People are using it far more than we expected.” This surprising finding illustrates how difficult it is for AI companies to predict the actual cost of usage and calculate appropriate prices.
The surprising connection between the two changes
The close timing of these two events is more than a coincidence. ChatGPT frequently relies on current web information for its responses, accessing Google results directly or indirectly via scraping services. Disabling the num=100 parameter significantly hinders ChatGPT and other AI systems from efficiently collecting web data.
AI applications like ChatGPT rely on extensive, up-to-date web data to generate relevant and accurate answers. The num=100 parameter enabled these systems to quickly and cost-effectively collect large amounts of search results and select the best sources for their answers.
Disabling this parameter forces AI systems to perform significantly more individual queries, exponentially increasing costs. This explains why OpenAI simultaneously reduced its citation frequency – the cost of providing up-to-date web information was simply no longer economically viable.
The $800 billion funding gap
Bain & Company's alarming forecast
A recent study by Bain & Company reveals a threatening funding gap in the AI industry. By 2030, AI companies like OpenAI, Google, and DeepSeek will need to generate approximately $2 trillion annually to cover the rising costs of computing power and infrastructure. However, the consultants expect the industry to fall about $800 billion short of this target.
David Crawford, Chairman of Bain & Company's Global Technology Practice, warns urgently: "If current scaling laws prevail, artificial intelligence will increasingly burden global supply chains." This discrepancy between required and expected revenues raises fundamental questions about the valuation and business models of the AI industry.
Massive investments versus unclear returns
Major US technology companies are driving their AI investments to unprecedented levels. Microsoft, Meta, and Google are planning a combined $215 billion for AI projects by 2025. Amazon has announced an additional $100 billion in investments. These expenditures will primarily go toward expanding data centers and developing new AI models.
Investments have more than doubled since the launch of ChatGPT. By 2024, the four largest tech companies had invested a combined $246 billion in AI—a 63 percent increase over the previous year. By the early 2030s, annual spending on AI could exceed $500 billion.
Energy demand and infrastructure challenges
Bain & Company predicts that the additional global demand for computing power could climb to 200 gigawatts by 2030, half of it in the United States. The electricity consumption of AI data centers will increase from 50 billion kilowatt-hours in 2023 to around 550 billion kilowatt-hours in 2030 – an elevenfold increase.
This massive expansion will have significant environmental impacts. Despite the expansion of renewable energy, greenhouse gas emissions from data centers will rise from 212 million tons in 2023 to 355 million tons in 2030. Water consumption for cooling will nearly quadruple to 664 billion liters over the same period.
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The DeepSeek shock as a turning point
Cost-effective innovation from China
The Chinese startup DeepSeek has shaken up the AI industry with its R1 model. With an estimated development cost of just $5.6 million, the company has developed a model that can compete with the significantly more expensive US models. By comparison, OpenAI's GPT-4o cost approximately $80 million to develop.
DeepSeek's pricing massively undercuts the competition. The company's models are 20 to 40 times cheaper than corresponding models from OpenAI. DeepSeek's Reasoner model costs 53 cents per million input tokens, while OpenAI's o1 model costs $15 for the same number.
Impact on industry dynamics
DeepSeek's success challenges previous assumptions in the AI industry. The company proves that cutting-edge AI is possible even without billion-dollar budgets, putting established providers under considerable pricing pressure. This development highlights an interesting side effect of the US export restrictions: The technical limitations forced the company to engage in software innovations to make optimal use of the available hardware.
Within a few weeks, DeepSeek's AI assistant captured 21 percent of the global LLM user share and displaced ChatGPT as the most popular free app in Apple's App Store. This rapid market penetration highlights the volatility of the AI market and the danger for established providers with cost-intensive business models.
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AI creates winners and losers: Who will survive the new internet order?
The dramatic impact on website traffic
The Decline Through AI Overviews
A study by Authoritas shows a significant drop in click-through rate (CTR) for publisher websites in the UK due to AI Overviews, by approximately half. According to the study, the CTR falls by 47.5 percent on desktop and 37.7 percent on mobile devices when AI Overviews are present. Even when a website ranks in the top position in Google AI Overviews, there is only a minimal improvement in clicks.
A new study by SEO expert Kevin Indig and UX researcher Eric Van Buskirk systematically examines the use of Google's AI Overviews. In a complex setup, 70 users were observed performing eight realistic search tasks. This resulted in approximately 400 AI-based interactions, providing insights into how significantly web search behavior is changing under the influence of AI.
AI overviews significantly reduce the click-through rate on external links. On desktop, the click-through rate drops by up to two-thirds, and on mobile devices by almost half. Users increasingly rely on the information in the AI overview – especially for simple or standardized queries.
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The collapse of traditional web traffic
Since Google integrated generative AI answers into its search results in the US in May 2024 and in Germany at the end of March 2025, content providers such as news sites, blogs, and forums have been observing a significant decline in their visitor numbers with concern. If the answer is already displayed on the search page, users often don't click on the original source.
Mail Online's click-through rate (CTR) dropped by over 56 percent due to AI Overviews. Some websites saw a decline in organic traffic of 18-64 percent. Organic CTR can fall by up to 70 percent when AI Overviews are in use.
Built In reports that organic search traffic for publishers could decline by 25 percent by 2026. Even top-three positions are experiencing massive click-through rate (CTR) drops due to AI-driven in-app purchases (AIOs); positions four through ten are seeing declines of up to 50 percent. A study of 1,000 small and medium-sized enterprise (SME) websites found that 68 percent suffered significant organic traffic losses after implementing AI search capabilities.
The focus on a few sources
Referral traffic from ChatGPT to websites has decreased by 52 percent since July 2024. Reddit citations have increased by 87 percent and now account for over 10 percent of all ChatGPT citations. Wikipedia jumped 62 percent from its July low and achieved almost 13 percent of the citation share.
The top three websites – Wikipedia, Reddit, and TechRadar – accounted for 22 percent of all citations, a 53 percent increase in just one month. ChatGPT now favors a handful of “answer-first” sources, while branded websites lose visibility and miss out on millions of potential referral clicks.
Mishaps under time pressure
Google's image generation problems
The current problems with Google Gemini are manifesting themselves on several levels. For weeks, users have been reporting fundamental malfunctions in the Imagen technology, particularly when generating images in desired formats. The widespread issue primarily affects the creation of 16:9 images, which was previously possible without any problems but is now no longer implemented.
Even more serious is the phenomenon that images are supposedly generated but cannot be displayed. Users receive confirmation of successful image creation but only see empty areas or error messages. This problem occurs in both the web version and the mobile app, rendering the image generation function practically unusable.
Communication failure and lack of transparency
Google's handling of these obvious system errors is particularly problematic. The company is not proactively communicating these issues to users, even though they have existed for weeks. Instead, the system continues to claim that all functions are working correctly, while actual performance is significantly impaired.
The current difficulties should not be viewed in isolation, but rather as part of a series of problems with Google's AI systems. Back in February 2024, Google had to completely disable the human-to-human representation in Gemini after the system generated historically inaccurate images. German soldiers were depicted with Asian features, and Vikings were given dreadlocks – errors that revealed fundamental problems in the preparation of the training data.
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Structural problems in AI development
The haste of the publications
The recurring problems point to systemic weaknesses in Google's AI development. The company appears to be under immense time pressure to keep pace with competitors like OpenAI, resulting in hastily released products. This "move fast and break things" mentality may work in other technology sectors, but it proves problematic for AI systems, as errors have a more direct impact on the user experience.
The working conditions at the subcontractors responsible for content moderation and system improvement exacerbate these problems. Reports of time pressure, low wages, and a lack of transparency in the supply chain raise doubts about the quality of manual system optimization.
Fragmentation of the system architecture
The lack of integration between different Google services reveals structural deficiencies. While Google Photos receives new AI image editing features, basic image generation in Gemini doesn't function properly. This fragmentation points to insufficient internal coordination and exacerbates the problems for end users.
The economic consequences
Impact on different user groups
The problems described have a concrete impact on various user groups. Content creators and marketing professionals who rely on reliable image generation are forced to resort to alternative solutions. This not only leads to workflow interruptions but also to additional costs for other tools.
The situation is particularly problematic for users of the paid Gemini Pro version. They pay for extended features but often receive worse performance than promised. Many have already canceled their subscriptions because the promised improvements have not materialized.
The loss of trust in AI providers
The unreliability of the systems is leading to a loss of trust in Google as an AI provider. Users who rely on the accuracy and availability of the services are increasingly turning to alternative providers. This could weaken Google's position in the highly competitive AI market in the long term.
While OpenAI's DALL-E or Anthropic's Claude deliver more consistent results, Google struggles with fundamental functional flaws. It's particularly striking that even free alternatives often work more reliably than Google's premium offerings.
Parallels to the dot-com bubble
Similar market dynamics
Current developments show striking parallels to the dot-com bubble around the turn of the millennium. Back then, the internet hype led to extreme valuations and ended in a spectacular crash. Today, AI companies face similar challenges: astronomical valuations collide with unclear business models, while the gap between investments and actual revenues continues to widen.
The current valuation of the S&P 500 is 38 times its earnings over the past ten years. Only during the dot-com bubble was the valuation higher, as strategists at Morgan Stanley point out. Henry Blodget, a former star analyst of the dot-com era, warns of eerie parallels to the current AI boom.
The bizarre truth behind the US economic boom
George Saravelos of Deutsche Bank puts it in a shocking way: “AI machines are literally saving the US economy right now.” The analysis reveals a paradoxical situation – economic growth is not coming from revolutionary AI applications, but from the mere construction of “factories for generating AI capacity.”.
Particularly alarming: The bank notes that without this technology-related spending, America would already be in recession. A house of cards built on billions in investments is keeping the world's largest economy afloat. This extreme dependence on AI investments carries systemic risks that extend far beyond the technology sector.
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Solutions and future perspectives
Alternative business models
Rising costs are forcing AI providers to develop new business models. OpenAI is testing usage-based fees, where customers only pay for the computing power they actually use. Batch APIs already offer cost savings of up to 50 percent for non-time-critical requests.
To achieve profitability, OpenAI is reportedly considering raising prices for its various subscription tiers. The company is planning drastic price increases: ChatGPT Plus is expected to rise from $20 to $22 by the end of 2024 and even cost $44 per month by 2029.
Specialization as a survival strategy
The rising costs of general-purpose AI systems could lead to the development of specialized solutions. Instead of trying to answer all queries with current web data, AI systems could proceed more selectively and only resort to costly web searches for specific query types.
This would encourage diversification of the AI market, with different providers developing different specializations. Some might focus on current information, others on in-depth expertise without an internet connection.
New cooperation models
New cooperation models are already emerging between AI providers and content creators. Some publishers are negotiating direct licensing agreements with AI companies to receive appropriate compensation for the use of their content. This development could lead to a new ecosystem in which content creators are directly compensated for the use of their content in AI systems.
Recommendations for various stakeholders
For website operators and content creators
Website operators should diversify their strategies and not rely solely on search engine traffic. Building direct relationships with users through newsletters, social media, and other channels is becoming increasingly important. At the same time, they should improve the quality of their content to be mentioned in AI-generated responses.
Instead of focusing on quantity, quality is becoming more important. Content creators should concentrate on producing helpful, unique content that offers real added value. The days when pure SEO optimization was enough to reach the top 100 positions are over.
For SEO agencies and tools
SEO agencies need to adapt their services and focus more on the top 20 positions, as these generate the majority of real traffic. The era of comprehensive top 100 analyses is coming to an end, which could free up resources for more in-depth optimizations.
Tool providers like Semrush and Accuranker are working feverishly to adapt their systems, but the increased costs will inevitably be passed on to customers. Many established tools currently display incomplete or erroneous data because their crawling logic was based on the old num=100 parameter.
For AI companies
AI companies face the challenge of developing sustainable business models that work for both them and content creators. Current practices of free content usage are not sustainable in the long run if they undermine the foundation of their data sources.
To regain user trust, companies like Google need to make fundamental changes to their approach. First and foremost, more transparent communication about system issues and planned maintenance is essential. Users have a right to know when features aren't working properly.
Hidden cost-cutting measures and their consequences: The new power of the tech giants
The seemingly minor technical changes at Google and ChatGPT mark a fundamental turning point in the digital information landscape. They demonstrate how dependent the entire internet ecosystem is on the decisions of a few large technology companies.
The combined effect of both changes accelerates the transformation from a link-based to an AI-mediated internet. This development brings both opportunities and risks: users receive faster answers to their questions, but the economic foundations of content creation are fundamentally challenged.
The industry is undergoing a period of reorientation, in which new balances between technology providers, content creators, and users must be established. The coming years will reveal which players can successfully adapt to the changing conditions and which new business models will emerge.
The $800 billion funding gap predicted by Bain & Company could lead to industry consolidation. Only the financially strongest companies would survive, while smaller providers and startups could disappear from the market. Whether the AI bubble develops into a controlled correction or a dramatic crash depends on whether the industry can develop viable business models in time.
The hidden cost-cutting measures at major AI providers are just the tip of the iceberg of a much larger structural crisis. While the public remains fascinated by the possibilities of artificial intelligence, these companies are desperately fighting for their financial survival behind the scenes. The silent revolution is already well underway – its effects will shape the digital landscape for years to come.
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