The AI House of Cards: Why Today’s AI Boom Could Unravel Fast

Wall Street is all-in on AI, with trillion-dollar expectations pushing tech stocks to extreme valuations. Oracle’s recent claim of a $523 billion cloud backlog – largely tied to AI infrastructure – perfectly captures the excitement. Big deals with OpenAI, Nvidia, and Meta sent shares soaring.

But behind the hype is a fragile setup that investors should understand: circular financing.

What Is Circular Financing – and Why It Matters

Circular financing happens when the same group of tech giants effectively fund each other’s growth. Oracle builds data centers for OpenAI. OpenAI relies on Nvidia chips. Microsoft  and Meta help fund OpenAI. Money moves around the same circle, creating the appearance of massive demand – even though little outside cash is entering the system.

On paper, revenues look strong. In reality, risk is quietly building. If one company stumbles, the entire loop can break.

Oracle Shows How Fast Things Can Turn

Oracle is a prime example. More than half of its cloud backlog – about $300 billion – is tied to OpenAI. That story pushed Oracle shares up 36% earlier this year.

Then reality hit.

• Capital spending surged to $12 billion per quarter
• Total debt climbed above $100 billion
• Revenue missed expectations
• Guidance was cut
• The stock fell 40%, wiping out roughly $360 billion in market value

Moody’s now says Oracle has its highest exposure to OpenAI and the weakest credit metrics among peers. Delays tied to OpenAI projects only add to the pressure.

OpenAI: The Center of the Risk

OpenAI sits at the heart of this entire ecosystem. It has committed roughly $300 billion to Oracle infrastructure – but its finances are stretched.

• Projected cash burn: $115 billion by 2029
• Annual losses expected to reach $14 billion by 2026
• Compute costs alone near $100 billion
• Expected 2026 revenue: just $30 billion

In simple terms, costs are rising far faster than profits. OpenAI’s survival depends on continuous funding in a market that’s becoming more cautious.

If One Falls, Others Follow

If OpenAI runs into trouble, the ripple effects could be severe:

• Oracle could lose a major chunk of future revenue and face debt stress
• Nvidia could see GPU demand weaken and margins compress
• Microsoft, with a $13 billion stake, could face large write-downs
• Amazon and Google wouldn’t be immune as AI spending slows
• Corporate bond markets could tighten under $1+ trillion in Big Tech debt

AI-focused stocks now make up roughly 30% of the S&P 500. Even a small pullback could shake the entire market.

The Bottom Line for Investors

The AI boom is real – but it’s being built on heavy debt and tightly linked balance sheets. Analysts at major banks are already warning that AI spending is becoming too circular, a trait seen in past market bubbles.

For retail investors, the lesson is simple:

• Follow the cash, not just the headlines
• Watch debt levels and capital spending
• Be cautious when growth depends on everyone propping each other up

AI will still create long-term winners – but in a system built on circular money flows, one break in the chain could bring the whole structure down.

Intel is starting to look much stronger in the semiconductor industry, and several big trends are working in its favor. We are watching Intel closely as a potential addition to our Trend Disruptors service—but as we mentioned yesterday, we’re also being patient. Nvidia reported very strong earnings last night, and while the stock initially jumped this morning, both Nvidia and the entire semiconductor sector turned negative as the day went on. This reinforces our view that the sector may still be in the early stages of a pull back, if not a full blown correction.

Why Intel is Looking More Interesting

1. Intel’s new chip-making technology is finally improving.
   For years, Intel lagged behind top competitor TSM (Taiwan Semiconductor). But Intel’s new “18A” manufacturing process is making real progress. If Intel can mass-produce chips at this level, it will help close the gap that has held the company back.
2. Big chipmakers want more manufacturing options — and the US government is heavily backing Intel.
   Companies like AMD and Nvidia don’t want to rely on only TSM, especially with the geopolitical risks surrounding Taiwan and China. Any supply disruption could ripple through the entire tech world.

Intel is becoming a more important alternative, and the US government has made a large financial investment to help Intel build advanced chip factories. This backing gives Intel stronger footing as it rebuilds its foundry business.

3. A shift in the AI industry could benefit Intel.
   Nvidia currently dominates in AI ‘training,’ which requires massive computing power. But many experts believe the next wave of spending will be in AI inference—the everyday running of AI models. Inference doesn’t always require Nvidia’s powerful GPUs. CPUs from Intel and AMD could play a bigger role, giving Intel a chance to regain market share.

Bottom Line

Intel is benefiting from improving manufacturing technology, strong US government support, customer demand for more diverse chip suppliers, and a potential shift in AI computing toward areas where Intel competes well.

We see real potential here—but given the semiconductor sector’s stretched levels and the risk of a broader pullback, we are watching closely and waiting for a better entry point before pulling the trigger on Intel for Trend Disruptors subscribers.

Stay tuned!

The artificial intelligence (AI) landscape is on the brink of a seismic shift with the advent of DeepSeek, a Chinese startup that has developed an innovative AI model capable of rivaling top-tier offerings from global leaders such as OpenAI and Meta. DeepSeek’s groundbreaking approach, which combines cost-effective hardware with optimized software solutions, threatens to upend the industry’s reliance on ultra-expensive infrastructure. This development has far-reaching implications for the AI sector, democratizing access and reshaping the competitive dynamics.

Cost-Effectiveness at the Core

DeepSeek’s most compelling proposition is its ability to deliver high-performing AI at a fraction of the cost. While companies like OpenAI and Meta have poured billions into developing their models and rely on advanced chips manufactured by Nvidia and Taiwan Semiconductor Manufacturing Company (TSMC), DeepSeek claims to have achieved comparable results with an investment of under $6 million.

This dramatic reduction in cost is made possible through the use of less advanced hardware paired with resource-optimized software. Instead of focusing on the most cutting-edge chips, DeepSeek leverages innovative methodologies to maximize efficiency, reducing overhead and making AI development more accessible to smaller players.

Implications for the AI Market

1. Democratization of AI The cost barrier to AI development has historically excluded smaller organizations and emerging economies from participating in cutting-edge research and applications. DeepSeek’s model opens the door for more players to enter the field, fostering innovation and competition. This democratization could accelerate the development of localized AI solutions tailored to unique cultural, linguistic, and economic contexts.
2. Pressure on Industry Giants The AI chip market, dominated by Nvidia and TSMC, faces mounting pressure as DeepSeek’s approach gains traction. If companies can achieve similar performance without relying on high-end processors, demand for these expensive chips may decline, forcing industry leaders to adapt their strategies. This shift could lead to a re-evaluation of pricing models and greater investment in alternative, cost-effective technologies.
3. A Paradigm Shift in AI Development DeepSeek’s success signals a broader trend towards optimizing efficiency over sheer computational power. This paradigm shift encourages a rethinking of AI’s development pipeline, emphasizing smarter resource allocation and sustainable scaling. Companies that adapt to this model are likely to thrive in a more cost-conscious market.
4. Open Source as a Balancing Force One significant way to mitigate the geopolitical and market risks associated with DeepSeek’s dominance is through open-source AI development. By openly sharing methodologies, software optimizations, and frameworks, other companies and researchers can replicate and enhance similar cost-effective models. This approach reduces reliance on a single player or region, spreading innovation globally and fostering a collaborative environment. Open-source initiatives also encourage transparency and trust, ensuring that advancements are not limited to specific geopolitical interests.

Challenges and Considerations

While DeepSeek’s claims are promising, several challenges must be addressed before their approach can be widely adopted:

• Performance Validation: The long-term viability and performance of models developed using less advanced hardware remain to be fully validated. Any significant gaps in reliability or scalability could temper enthusiasm for this approach.
• Adoption Barriers: Established players with entrenched investments in high-end infrastructure may be slow to adopt or recognize the benefits of DeepSeek’s methodology.
• Geopolitical Concerns: As a Chinese company, DeepSeek’s rise may spark geopolitical tensions, particularly in the context of ongoing tech rivalries between the U.S. and China. Trade restrictions and intellectual property disputes could impact its global influence.
• Open-Source Dynamics: While open-source development has enormous potential, it also introduces challenges related to intellectual property rights, monetization, and the risk of misuse by malicious actors. Ensuring that open-source models remain secure and responsibly utilized will require coordinated global efforts.

Conclusion

DeepSeek represents a bold new direction for the AI industry, challenging assumptions about the resources required for cutting-edge innovation. By proving that high-performance AI can be achieved cost-effectively, DeepSeek has the potential to democratize access, disrupt industry giants, and inspire a more inclusive and sustainable AI future. The promotion of open-source development could further amplify these benefits, reducing risks associated with geopolitical dependencies and fostering global collaboration. As the industry continues to evolve, all eyes will be on DeepSeek to see if it can deliver on its promises and truly change the game for AI.

Stay tuned!

In market analysis, closely monitoring industry leaders is vital, particularly in flourishing sectors like Artificial Intelligence (AI). As AI continues its rapid evolution, drawing parallels to the internet’s growth becomes increasingly evident. Just as the internet’s development unfolded in stages—from the establishment of hardware infrastructure such as modems, personal computers, and fiber optic cables, to the rise of software dominance, and finally, the global presence of high-speed internet—AI is following a similar trajectory.

At the forefront of AI’s hardware infrastructure is ASML, a Dutch semiconductor giant renowned for its cutting-edge chip manufacturing technology tailored for AI applications. Recent earnings reports from ASML have revealed potential hurdles in semiconductor demand, with bookings falling below anticipated levels. This development has prompted market observers to scrutinize the underlying factors contributing to this slowdown, as it could signal shifts in the broader semiconductor landscape.

Here is how the market reacted to ASML’s earnings report:

Meanwhile, Taiwan Semiconductor Manufacturing Company (TSM) stands out as a pivotal player in the AI sector, boasting the title of the world’s largest semiconductor manufacturer. Specializing in the production and packaging of semiconductors, TSM plays a crucial role in meeting the flourishing demand for advanced AI chips. Despite witnessing strong demand for AI-related chips, TSM has also observed a slowdown in other sectors, such as smartphones and automotive semiconductors. This wide angle perspective highlights the multifaceted nature of the semiconductor industry, where fluctuations in demand across various sectors can influence overall market dynamics.

Here is how the market reacted to TSM’s earnings report:

While the AI trend remains robust, as evidenced by the performance of industry leaders like ASML and TSM, the importance of managing market expectations cannot be overstated. Investors must navigate potential market volatility with caution, recognizing the inherent complexities and uncertainties.

The good news is that we have many stocks on our watch list and once the current sell-off completes, we will have some great buying opportunities.

Stay tuned!

As an investment newsletter, dedicated to great investment ideas such as trend disruptors, we scour the market to identify companies poised to become technology disruptors. In a recent update, we introduced to our subscribers three new entities that align with our criteria. Today, we shine a light on one of those companies:  GSI Technologies

Using AI technology requires vast amounts of data to be processed quickly and efficiently in order to be useful in applications that depend on it. In the AI world there are huge amounts of data in play, but processing that much data in the standard cache-to-processor chips can be too slow.  The illustration below shows how standard server processor cores struggle with large datasets, due to the narrow connection with the large on-chip memory.

GSI Technologies, a recent pick for TREND DISRUPTORS subscribers has developed a first-generation chip (GEMINI-l) to speed things up, and are developing the next version (GEMINI-ll), aiming to bring this solution to market in the second half of 2024.

GSIT is a leading provider of semiconductor memory solutions for in-place associative computing applications. The company deals directly with high-growth markets like AI and high-performance computing, which includes both natural language processing and computer vision.

To be clear, this is not traditional processing. GSIT’s Gemini technology uses associative processing unit (APU) products focused on applications using similarity search, which are used in visual search queries for things like e-commerce, computer vision, drug discovery, and cybersecurity. Of course, advanced AI chip solutions can serve many markets, like networking, telecommunications, and the military.

GSIT has just announced they have completed the tape-out for Gemini-II and will evaluate the first silicon chip by the end of 2023.  Gemini features millions of cores that can all access memory at once, allowing a much greater flow of data, generating much faster results.

Gemini-I APU’s architecture features parallel data processing with two million-bit processors per chip. The massive in-memory processing reduces computation time from minutes to milliseconds, while significantly reducing power consumption, all in a scalable format. A comparison of leading AI chips, shows that the Gemini chip excels when compared to products from Intel, Nvidia, and Graphcore.

Gemini-I excels at large (billion item) database search applications, like facial recognition, drug discovery, Elasticsearch, and object detection. Gemini-II will bring significant performance enhancements over Gemini-I with more than ten times the processing performance with eight times the memory density compared to Gemini-I. Gemini-II’s combination of high processing power, large built-in memory, tremendous bandwidth, reduced power consumption, and latency, provides a best-in-class solution for AI applications.

Gemini-II contains 1 million Bit Processors in six megabytes of associative compute memory tightly connected to 96 megabytes of distributed SRAM with a huge 46 terabyte per second bandwidth. The Gemini APU implements bit-granular processing, which allows users fully flexible cycle by cycle data format operation, an inherent advantage versus other parallel processors. Gemini-II is a complete package that includes a DDR4 controller and external interfaces for PCIe Gen4 by 16, and PCIe Gen4 by 4. This integrated solution offers substantial processing capabilities, being suitable for both low power data center expansion and enabling data center functions at the edge. This empowers local execution of computationally intensive tasks, increasing the capabilities of edge applications like advanced driver assistance systems for automobiles, and HPC in delivery drones, autonomous robots, unmanned aerial vehicles, and satellites.

“This major achievement showcases our continued commitment to pushing the boundaries of AI chip technology,” said Lee-Lean Shu, CEO and Chairman of GSI Technology. “We’re in talks with a leading Cloud Service Provider to further explore the APU architecture benefits highlighted by Gemini-II. Large language models, such as ChatGPT, Microsoft BING, and Google’s Bard, are pushing natural language processing boundaries. We’ve just begun to tap into their transformative potential across many industries and applications. Abundant opportunities await Gemini-II and future APU implementations in the AI market.”

Founded in 1995, GSI Technology, Inc. is a leading provider of semiconductor memory solutions. The Company recently launched radiation-hardened memory products for extreme environments in space and the Gemini® Associative Processing Unit (APU), a memory-centric design that delivers significant performance advantages for diverse AI applications. The Gemini APU architecture removes the I/O bottleneck between the processors and memory arrays by performing massive parallel searches directly in the memory array where data is stored. The novel architecture delivers performance-over-power ratio improvements compared to CPU, GPU, and DRAM for applications like image detection, speech recognition, e-commerce recommendation systems, and more. Gemini may be the ideal solution for edge applications with a scalable format, small footprint, and low power consumption where rapid, accurate responses are critical.

GSIT has announced its receipt of a Phase I SBIR contract in the 23.5 cohort by AFWERX, which is a United States Air Force program with the goal of fostering a culture of innovation within the service. Encompassing a number of programs supported with relatively small amounts of funding, the initiative is intended to circumvent bureaucracy and engage new entrepreneurs in Air Force programs. Under this contract, GSIT will perform a feasibility study to adapt, modify, and enhance its commercially proven Gemini® APU to propel Air and Space Force computing at the edge. They are embarking on a mission to explore high-performance edge processing. At the heart of this collaboration is the Gemini® APU, a dual-purpose compute-in-memory chip crafted to unleash the potential of various AI applications, including inference and high-performance computing workloads. While the APU is well suited for data center applications, its exceptional power efficiency will allow GSI to pursue this remarkable capability to the edge as well.

Given all of the above, it is no surprise that TREND DISRUPTORS has an eye focused on this company, as GSIT has the potential to be a major Trend Disruptor in the hugely Disruptive AI market. The goal of TREND DISRUPTORS is to discover and monitor technical developments that have the potential to DISRUPT market sectors. We look for the best ideas, and we generate actionable investment recommendations for subscribers. As a general rule, these recommendations are speculative, and we advise caution, discretion, and thorough research. We strive to identify investment opportunities that can lead to success for the well-informed investor.

Stay tuned!!