AI BUBBLE BURSTING
Data Centers Remain a Threat
The Data Center Disaster
The above recent video warns about an economic and ecological disaster looming due to market speculation over AI. So I decided to ask AI for further info. Following are my questions and the AI replies. AI is undeniably useful for many things, but it’s surely over-hyped by the usual scammers in the ruling class and their lackeys. I thought about delving a little into the likely effects of IT tech etc. on human health and the environment, but that would make the post too long for my taste, and take too much time for now.
CONTENTS — FIND IN PAGE
WHEN AI BUBBLE IS LIKELY TO BURST (VERY SOON)
WHEN AI BUBBLE IS LIKELY TO BURST (VERY SOON)
How soon is AI bubble likely to burst taking down big AI companies?
The prevailing expert consensus is that the AI bubble is already showing major signs of strain and could be approaching its bursting point within the next year, likely between late 2025 and 2026, with smaller startups and pure-play AI companies at highest risk of collapsing first, while diversified tech giants may weather the downturn but face substantial write-downs and valuation drops.
Key Signs and Timeline
Multiple major institutions, including Goldman Sachs, IMF, and JPMorgan, have issued simultaneous warnings about the AI bubble beginning in October 2025.
Most analyses point to the fourth quarter of 2025 or early 2026 as the expected period for significant market correction, with some commentators like Ed Zitron specifically forecasting a Q4 2025 bust. {Q4 is running out of time though.}
The collapse is likely to start with pure-play AI startups and smaller chipmakers who are heavily dependent on speculative funding and lack diversified revenue streams, with survival rates in the sector predicted to be as low as 30-45%.
Unlike previous bubbles, the AI boom is mostly funded by equity/cash, not debt, so systemic collapse of the entire economy is considered unlikely, though large losses for investors and thousands of job cuts in the AI sector are projected.
Impacts on Big AI Companies
Giants like Microsoft, Google, Amazon, and Meta may survive because of their diversified business models, but their AI divisions are expected to suffer massive write-offs, and total company valuations could drop by up to 40% or more in severe scenarios.
The bubble’s burst may happen through a combination of rapid startup failures, sharp correction in stock valuations, abandonment of unprofitable AI projects, and slowed uptake as the “hyped” use cases fail to materialize meaningful profits.
Some experts, including Ray Dalio, say the final trigger may hinge on an external factor—such as monetary tightening by the US Federal Reserve—leading to liquidation and a sharper impact. {The Fed is controlled by the Deep State & Co., so they will figure out how to provide the most windfall profits for the ruling class, coming from the losses of common folks/investors.}
What Could Happen Next?
Institutional retreat, cancelled projects, and premature market exits will likely remove unprofitable players and sharply reduce investment inflow to AI.
The overall technology market may experience a correction of 10-30%, while AI-centric companies—especially those built solely on generative AI business models—could see far larger impacts, possibly losing up to $10 trillion in combined market value.
Investors and executives are already seeking proof of real returns, with many anticipated to face negative answers and react quickly to withdraw funding, forcing a rapid contraction.
DATA CENTERS PROSPECTS
What will then happen to the many data centers that have been built and the ones being built or planned to accommodate AI power needs?
After the AI bubble bursts or significantly cools down, the fate of the many data centers already built or under construction will be mixed but generally challenging, with significant consequences for the industry and local economies.
Near-Term Impact
Many AI-focused data centers may face underutilization or even become stranded assets as demand for AI-specific compute power evaporates quickly due to company collapses and project cancellations.
Some facilities will be powered down to cut costs, while new or half-completed construction projects may be halted, leaving empty warehouses and unfinished infrastructure.
Operators of highly leveraged data centers could face financial stress, leading to auctions of hardware at heavily discounted prices, especially specialized AI accelerator chips and servers.
Long-Term Outlook, not as “bad”
The data center industry as a whole is unlikely to collapse because general demand for cloud computing, video streaming, gaming, and enterprise IT remains strong and growing, supporting baseline utilization even if AI-specific load declines.
Large tech firms with diversified cloud businesses will likely consolidate underused facilities, shutting down some but repurposing others for traditional cloud services, web hosting, and enterprise needs.
The industry may experience repricing and slower growth, with contracts and expansions delayed, but the fundamental infrastructure need continues due to increasing digitization across sectors.
Economic and Utility Effects
Some localities that invested heavily expecting decades of tenancy in AI data centers face economic fallout from empty buildings and underused infrastructure, passing costs to utility ratepayers.
Electricity demand for data centers is sizable and continues growing structurally, meaning utilities may suffer from underutilized capacity but still see progressive energy demand overall as general digital economy expands.
Are free AI websites like this, likely to remain free, as or after the AI bubble bursts?
Free AI websites like this one are likely to remain free even as or after an AI bubble bursts, though with some important caveats related to their business models and sustainability.
Why Many Free AI Websites Will Likely Stay Free
Many free AI services operate on a freemium model, where basic features remain free to users while advanced features, customization, or higher usage limits require paid subscriptions or upgrades. This model has proven resilient even through industry fluctuations because the “free” offering acts as an entry point to convert users to paid plans gradually.
Free AI tools and websites are often supported by large technology companies or platforms (e.g., Google, OpenAI) that have diversified revenues outside AI services alone. These companies can afford to offer core AI functionalities for free to attract users, collect data, and build ecosystems, which helps justify continuing free access despite AI industry volatility.
Free access also supports increased AI adoption broadly, which sustains demand for premium services and partnerships, creating a self-reinforcing revenue model that is less likely to disappear quickly even if overall AI hype cools down.
Potential Risks to Free AI Sites
If the AI bubble burst causes deep funding cuts, smaller independent AI websites or startups solely dependent on speculative investment may struggle to stay operational and could reduce free offerings or close. But well-backed major platforms will likely maintain free tiers to maintain user base and market presence.
Free websites might scale back on infrastructure, update frequency, or newly released features during downturns, but shutting down all free access would risk alienating users and losing long-term market position.
LOCAL AI
What’s Local AI?
Local AI refers to running AI models and applications directly on local devices such as smartphones, laptops, desktops, or embedded hardware, instead of relying on centralized cloud servers. This means the processing, data handling, and AI computations happen locally on the device without needing to send data to remote servers over the internet.
The main benefits of local AI include:
Data privacy and security: Data remains on the device and never leaves, reducing the risk of exposure in transit or on external servers.
Reduced latency: Local processing provides instant, real-time responses without network delays.
Offline functionality: AI can work without internet connectivity, which is useful in remote areas or situations with unreliable networks.
Cost efficiency: By avoiding cloud computation and data transfer fees, local AI saves ongoing costs.
User control: Users decide how AI workloads are managed, including what data is used, enhancing customization and autonomy.
Local AI is increasingly feasible due to advances in edge computing, more powerful processors, and specialized AI chips that allow complex models to run efficiently on less powerful hardware. It’s useful for applications needing real-time interaction, privacy, and offline use cases such as voice assistants, healthcare diagnostics, industrial automation, and augmented reality.
Is Local AI customizable, so users can tell the AI program to avoid mainstream bias?
Yes, Local AI is customizable in a way that allows users to reduce or avoid mainstream biases in the AI’s responses. Because Local AI runs on the user’s own hardware and is open or semi-open in structure, users can control and modify the training data, prompt templates, and filtering rules. This makes it possible to diversify training datasets, implement fairness or bias-aware algorithms, and continuously audit and update the AI’s behavior to mitigate bias.
Specifically, users can:
Customize the training data by including diverse, balanced sources and excluding biased or narrowly framed content.
Adjust prompts and response rules to avoid politically or culturally mainstream biases.
Use bias-detection and fairness metrics tools built into many local AI platforms to regularly monitor outputs and refine them accordingly.
Incorporate feedback loops where diverse user inputs help identify and correct emerging biases.
Opt for transparent and explainable AI methods, making it easier to see where biases arise and take steps to address them actively.
Because the AI is locally controlled, users are empowered to shape the AI’s worldview to better match their values and reduce biases commonly embedded in large centralized models. This level of customization is difficult with commercial, cloud-only AI services, which are less transparent and more rigid.
Is local AI free now? If so, will it likely remain free?
Local AI tools and services are currently largely available for free or with core features free in 2025, especially those that run offline or on local devices without requiring cloud-based subscriptions. Examples include LM Studio, Jan, Ollama, GPT4All, and others that provide powerful AI functionalities with no recurring cost. Many of these tools enable users to run language models and AI tasks directly on desktops or personal hardware with zero ongoing fees, although in some cases, optional paid upgrades or enterprise support packages are offered for advanced use cases.
Will Local AI Likely Remain Free?
Local AI tools benefit from their open-source or community-supported nature and the lower cost structure of offline operation, which makes it feasible for them to remain free or have very low cost barriers in the foreseeable future.
Because these tools run on user-owned hardware rather than costly cloud infrastructure, ongoing operational costs and price pressures are much lower than for cloud AI services, supporting sustainability of free access.
These local AI projects often have vibrant developer ecosystems and are not primarily dependent on speculative investment funding, making them more resilient to market downturns or AI bubble bursts compared to cloud-only or startup-dependent offerings.
However, premium features, enterprise integrations, or support services might become monetized or restricted behind paywalls, but the core local AI tools are expected to keep free accessibility intact due to demand for privacy, control, and offline capabilities.
Where is Local AI available online now?
Local AI is available online now from several prominent platforms and tools that enable users to run AI models locally on their hardware. Some of the best-known and widely used Local AI platforms include:
Ollama: User-friendly, cross-platform local LLM platform supporting models like Llama 3, DeepSeek, and Phi-3 with simple commands and OpenAI-compatible APIs.
GPT4All: Desktop application optimized for Windows with pre-configured models and local document analysis features, popular for polished offline experiences.
LocalAI: Developer-centric platform offering API compatibility with OpenAI models, Docker support, extensibility, and multi-model support for language, image, and audio tasks. 1,2
Jan AI: Fully offline ChatGPT alternative that runs popular LLMs locally, with an extensible plugin system and API compatibility.
LM Studio: Multi-platform tool with strong GPU support, widely used for running local AI models with graphical interfaces. 3
Others: Tools like HugstonOne, oobabooga, KoboldCpp, AnythingLLM, and PrivateGPT offer various feature sets ranging from privacy to coding and retrieval-augmented generation support.
These platforms provide downloads, installation instructions, and active community support, making local AI accessible to developers, researchers, and enthusiasts who want privacy, customization, and offline capability without relying on cloud infrastructure.
Are they all likely to be available after the AI bubble bursts?
Most Local AI platforms available today are likely to survive and remain accessible after the AI bubble bursts, though with some changes in scale, funding, and consolidation.
Are any of the Local AI platforms high risk?
Yes. The Local AI platforms that are considered higher risk due to known security vulnerabilities and ongoing security challenges include:
Ollama (due to limited authentication and exposure risks if not properly secured)
LM Studio (similar risks around unsecured access and API vulnerabilities)
MCP clients including Gemini-CLI, MCP Inspector, Cherry Studio, VS Code, Windsurf, Smithery.ai, Lutra.ai, Glue.ai (due to recent critical flaws found such as remote code execution and account takeover risks). 4,5
These platforms require careful security setup, such as firewalls, strong authentication, and constant patching, to mitigate risks. Users deploying these should be cautious and implement strong security best practices.
IT FUTURE
When is a demographic or other limit on cloud computing and robotics likely to be reached? By demographic I mean will there be enough workers and customers to sustain growth or even a steady state in the industry?
The cloud computing industry is expected to experience strong growth through 2030, with market size projections reaching between $1.6 trillion to over $2 trillion, fueled by AI, big data, and digital transformation in many sectors. This growth will drive ongoing demand for skilled workers in cloud engineering, DevOps, AI operations, data center management, and more.
However, demographic concerns around workforce availability are becoming increasingly relevant:
There is evidence of talent saturation and skill shortages simultaneously; while many new CS graduates enter the market, the workforce suitable for specialized cloud roles (DevOps, SREs, cloud architects) remains insufficient and competitive due to complexity and evolving technologies.
Many current IT and cloud professionals face high work-life imbalance and burnout risks, which could impact retention and long-term industry capacity growth.
The rapid pace of cloud and AI innovation requires continuous retraining, and the availability of digitally skilled workers varies significantly by region, impacting sustained growth inequality.
Customer adoption is broadening to small-medium businesses globally, increasing demand for cloud services but also requiring scalable support and education ecosystems for successful utilization.
DATA Centers = Treason to "We the People".
Garbage in..garbage out. AI has apologized to me when I catch it in a lie. Question? Who controls the facts and how do we eliminate the millions of fictious research articles that were created to miss inform? Then they want AI to improve on healthcare?. Quality of healthcare is already diminishing as the hospital corps and etc maximize their profitability and burn out their care givers.