The 🔥H.E.A.T.🔥 Formula

AI Driven Insights to Spark Your Portfolio

January 27, 2025

The H.E.A.T. Formula is a radically different way to look at investing your portfolio.

‍H- Hedges, you should always have hedges and be agnostic as to being long or short. Bonds are not a hedge

‍E-Edges, you should always look for edges. Preferably these are edges with some sort of psychological underpinning, structural edges, or some sort of barrier to entry.

‍A-Asymmetric. Everything you do, be it trades or your overall portfolio, should be designed so that heads you win a lot, tails you lose a little.

‍T-Themes. You should always be invested in the top themes. Most everything else is just noise.

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Market Recap

Markets don’t go up in a straight line, which is why I’ve been adding hedges and urging you to as well. This morning is precisely the reason why. I’ve got an analysis of DeepSeek below, but the bottom line is that nobody really knows what the impact is going to be.

Lot of data this week headlined by FOMC on Wednesday and PCE on Friday.

DeepSeek

Below is a high-level summary and analysis of the Jefferies report on DeepSeek, along with commentary on whether the arguments are convincing and how they might shape winners and losers.

Jefferies had a pretty good report this morning so I had GPT summarize it and provide it’s own analysis…

Key Takeaways from the Jefferies Report

  1. DeepSeek’s Model Efficiency

    1. DeepSeek, backed by China-based quant fund High-Flyer, claims a large-language model (LLM) comparable to GPT-4o and Claude 3.5 Sonnet in performance but trained at far lower cost (under $6 million) using H800 GPUs.

    2. Its architecture (Mixture of Experts + Multi-head Latent Attention) activates fewer parameters per query, lowering compute and inference costs.

    3. The model is open-source, potentially letting the entire AI community replicate its efficiency.

  2. Implication: Less Need for Top-Tier Chips

    1. If DeepSeek’s cost-effective approach is broadly adopted, demand for high-end GPUs (e.g., Nvidia’s H100) and data-center expansions could slow.

    2. Lower HPC requirements could reduce AI capex, hurting semiconductor supply chains—especially those reliant on top-tier GPU cycles (e.g., TSMC, ASML, Nvidia’s HPC partners).

  3. Market Reaction

    1. Jefferies sees investors selling semiconductor and data-center names (especially HPC-related) while rotating into internet/software companies that benefit from cheaper AI training and inference.

    2. Japanese semicap firms (Advantest, Tokyo Electron, Ibiden) reportedly sold off on fear that HPC chip demand will fade. Meanwhile, Chinese internet stocks rose, anticipating lower AI capex and faster adoption.

  4. No Proven Monetization Boost

    1. Despite DeepSeek’s model efficiency, Jefferies notes that it has not accelerated AI commercialization. The ROI question remains: will cheaper compute truly speed up profitable use cases, or will it just fuel more model experiments?

  5. China’s Chip Constraints

    1. DeepSeek’s pursuit of efficiency is partly driven by U.S. export controls limiting access to top-tier GPUs. Jefferies suggests stricter bans might nudge China to innovate faster in AI.

    2. Trump’s administration is aware that restricting chips could backfire, pushing Chinese labs to develop even more efficient methods—potentially undermining the U.S. AI hardware advantage.

  6. Inconclusive Demand Outlook

    1. Jefferies posits two industry directions:

      1. Same or Higher Compute: Big labs still demand top-end HPC to leap ahead in model performance.

      2. Efficiency Focus: Investors push for better ROI on AI spending, reducing HPC demand as soon as 2026.

    2. The result: HPC supply chains face near-term skepticism, especially if cost-saving AI gains traction. But some analysts argue labs might use any gains in efficiency to train even bigger models—not necessarily cut total spending.

  7. Potential Beneficiaries

    1. Cloud & Internet: Less capital needed for HPC expansions, possibly faster AI adoption and ROI. Chinese internet giants (Alibaba, Tencent) with H800 fleets could incorporate DeepSeek’s approach and bolster their own AI services.

    2. U.S. Internet: Possibly reduce future AI capex, though it’s less clear how soon or how large the effect will be.

  8. Potential Losers

    1. Nvidia & GPU Supply Chain (including TSMC, memory substrates, test/packaging equipment): If the market believes advanced HPC demand could slow, these names face near-term valuation pressure.

    2. High HPC Capital Spend: Data-center expansions aimed at large GPU clusters might be reevaluated if new models run on fewer resources.

My Analysis: Are Jefferies’ Conclusions Convincing?

  1. Technical Plausibility

    1. DeepSeek’s claim of training a top-tier model cheaply with H800 GPUs is not implausible, especially using advanced techniques like MoE and low-precision (FP8) frameworks. Efficiency breakthroughs can reduce compute needs.

    2. However, it’s important to note that big U.S. labs hold back their best tech from the public. Performance comparisons are never apples-to-apples.

  2. Market Reaction

    1. The immediate sell-off in HPC-oriented semis reflects the fear that “less GPU” is needed to achieve GPT-4-level models. But historically, improved efficiency often leads labs to build even bigger or better models. AI demand might stay robust, just more optimized.

    2. Meanwhile, a shift toward “ROI over raw HPC” could slow HPC expansions in 2026 or beyond—particularly if capital markets demand profitability.

  3. Commercialization Gap

    1. The question of AI monetization remains unresolved: cheaper or smaller HPC might not automatically yield new revenue streams. If corporate adopters are cautious, HPC demand could stagnate. If new use cases emerge, HPC spending might continue.

  4. China’s Approach

    1. Export controls forced Chinese labs to innovate on mid-grade chips. DeepSeek’s success might embolden future Chinese AI breakthroughs. But the real test is at scale: can China replicate these results in mainstream enterprise or consumer use-cases without HPC supply strain?

Winners & Losers

Winners

  1. Cloud & Internet Platforms

    1. Alibaba, Tencent: Gains in AI efficiency plus large H800 inventories could allow them to roll out improved AI services faster and cheaper.

    2. U.S. Internet Majors (Meta, Google, Microsoft): If they can adopt or adapt similarly efficient architectures, they can cut AI cloud costs or accelerate product dev.

  2. Software / Application Providers

    1. More affordable AI can spark broader adoption, benefiting software developers who integrate these efficient models. ROI might drive real enterprise traction.

Losers

  1. Semiconductor & HPC Supply Chain

    1. Nvidia: Fear of reduced HPC demand. Although many argue Nvidia hardware is still central to advanced AI, any negative shift in HPC budgets spooks investors.

    2. TSMC, ASML, Advantest: HPC/AI chips (and equipment) are a major growth driver. A pivot to efficiency may reduce high-end tool orders and expansions.

  2. Data-Center Infrastructure Providers

    1. If AI labs need fewer racks of GPUs, expansions (especially HPC-specific) may slow or smaller. Substrate makers, test/packaging providers, and certain liquid-cooling specialists might see reduced demand growth.

Bottom Line

  • Jefferies’ “Sell Semis / Buy Internet” reaction is rooted in the notion that AI can remain highly profitable with fewer HPC resources. In the near term, that fear can weigh on HPC-exposed semiconductors.

  • Do I Agree? Yes and no. DeepSeek’s breakthroughs are real enough to challenge the assumption that bigger is always better in AI. That can spook HPC supply chains in the short run. However, historically, better efficiency often leads to more advanced modeling, not less. Labs may pour those efficiency gains into building bigger models or reaching more users, sustaining HPC demand over time.

  • Winners: Major cloud/internet names who can quickly integrate cost-effective AI, plus software innovators leveraging open-source breakthroughs.

  • Losers: HPC chipmakers and equipment suppliers may see near-term market sentiment turn negative, though the ultimate impact depends on whether efficiency leads labs to cut HPC budgets or simply deploy more advanced projects.

In short, DeepSeek spotlights a new era of AI cost-efficiency. That’s bullish for AI adopters (especially if they can adopt these new open-source models) and may weigh on HPC suppliers if buyers demand smaller clusters or more justification for capex. The full story likely unfolds over the next 12–24 months, as we see whether labs opt to lower costs or push model boundaries with their HPC capacity.

I then asked for a list of specific winners and losers……

Winners

1. Alibaba Group (BABA)

  • Rating: 9/10

  • Why: Significant AI cloud investments (Qwen LLM) and a large H800 GPU fleet. If DeepSeek’s approach lowers training and inference costs, Alibaba can accelerate its AI service rollout across e-commerce and cloud solutions.

2. Tencent Holdings (TCEHY / 700 HK)

  • Rating: 8/10

  • Why: Major AI initiatives in social media, gaming, and cloud. Cheaper AI models can boost WeChat features, gaming AI, and enterprise cloud adoption.

3. Baidu (BIDU)

  • Rating: 7/10

  • Why: AI leader (ERNIE model, autonomous driving). Incorporating DeepSeek-like efficiency reduces HPC overhead, potentially speeding up product development.

4. Microsoft (MSFT)

  • Rating: 7/10

  • Why: Azure invests heavily in generative AI (OpenAI partner). If HPC requirements moderate, Microsoft’s AI ROI could improve, freeing capital for broader deployment.

5. Alphabet (GOOGL)

  • Rating: 7/10

  • Why: Google’s Bard, Vertex AI, and internal data-center scale. Lower HPC outlays help margins and let Google ramp more AI services globally.

6. Amazon (AMZN)

  • Rating: 6/10

  • Why: AWS powers many AI workloads. Lower HPC costs might attract new AI projects, though Amazon invests heavily in high-end training too. Net effect is positive if AI usage broadens.

7. GDS Holdings (GDS)

  • Rating: 7/10

  • Why: Leading China-based data-center provider. Even if DeepSeek cuts HPC hardware demand per model, more AI developers could jump in, driving overall colocation and managed services growth—particularly in China.

Other Data-Center Players

  • Chindata Group (CD) – 6/10

    • Carrier-neutral data centers in Asia. Could gain from broader AI uptake, though HPC expansions may be smaller if each project needs fewer GPUs.

  • 21Vianet (VNET) – 5/10

    • Another Chinese data-center operator. Gains if AI adoption expands among smaller players.

  • Equinix (EQIX) / Digital Realty (DLR) – 5/10

    • Global data-center REITs. Despite fewer HPC racks potentially needed, more widespread AI usage could still spark incremental colocation demand.

Losers

1. Nvidia (NVDA)

  • Rating: 8/10 (Negative)

  • Why: Nvidia dominates top-tier AI GPUs (H100). If labs opt for midrange (H800) or find more efficient approaches, HPC orders for H100-class chips might slow. However, Nvidia still profits from broader GPU demand, making the long-term impact uncertain.

2. TSMC (TSM)

  • Rating: 7/10 (Negative)

  • Why: TSMC’s growth has leaned on advanced node production for HPC GPUs/ASICs. If HPC expansions moderate, advanced-node wafer demand might cool off.

3. ASML Holding (ASML)

  • Rating: 7/10 (Negative)

  • Why: Sells EUV lithography tools for advanced semiconductor production. A slowdown in AI chip capex could reduce growth in high-end tool orders.

4. Advantest (ATEYY / 6857 JP)

  • Rating: 6/10 (Negative)

  • Why: Major supplier of test equipment for advanced AI chips. Fewer top-tier GPU wafers tested = fewer test-equipment orders.

5. Tokyo Electron (TOELY / 8035 JP)

  • Rating: 6/10 (Negative)

  • Why: A key provider of lithography, etch, deposition equipment. HPC chip expansions have been a crucial driver of advanced fab tool sales.

Mixed Cases

AMD (AMD)

  • Rating: 6/10 (Mixed)

  • Why: On one hand, if labs skip the highest-end GPUs, AMD’s Instinct or midrange GPU solutions may benefit. On the other hand, a broad HPC slowdown could weigh on AMD’s potential share gains in data-center AI.

Data-Center Equipment Suppliers (Cooling, substrates, HPC niche)

  • Some could see demand wane if HPC expansions scale back. Others might see new interest from a proliferation of smaller AI clusters. The net effect is uncertain.

Final Observations

  • DeepSeek’s Big Picture: A more cost-effective AI model might reduce top-tier HPC chip demand but expand AI usage across a broader swath of enterprises—especially in China.

  • Winners: Major internet/cloud companies, plus data-center operators that cater to varied AI clients.

  • Losers: HPC-centric semiconductor and equipment suppliers, worried about a dip in “bigger is better” GPU expansions.

  • Mixed: Some GPU players (like AMD) or niche HPC solution firms could find upside if midrange-based adoption soars, or downside if the overall HPC cycle softens.

Ultimately, the longer-term outcome depends on whether cost savings lead labs to cut HPC budgets or reinvest in bigger, more advanced models. In the short run, fear of lower HPC spending drives a “sell semis, buy internet” mood—but real-world demand trends could evolve if total AI usage balloons.

TikTok

Earlier in the day, Reuters reported two people with knowledge of the discussions said Trump’s administration is working on a plan to save TikTok that involves tapping software company Oracle and a group of outside investors to effectively take control of the app’s operations.

Bottom Line

  • Revenue Upside: Substantial, but not transformative in the near term. Hosting one of the world’s most popular apps definitely raises Oracle’s cloud growth outlook.

  • Stock Price: Likely positive impact, driven by new growth narrative and potential multiple expansion for Oracle’s underappreciated cloud business—assuming the deal (or a similar arrangement) secures official approvals and sustains user momentum on TikTok.

  • Key Watch Factors: Final terms of any investment or JV, regulatory timelines, performance in hosting large-scale consumer traffic, and possible follow-on deals from other big tech or social-media platforms.

Overall, an Oracle–TikTok partnership can be a solid catalyst for Oracle’s revenue growth and investor sentiment, though much hinges on regulatory sign-offs and how well Oracle handles large-scale consumer workloads in the long run.

Emerging Market Outflows?

Not sure there is anything actionable here yet, but something to keep an eye on…

Final Thoughts

  • Macro Environment: The capital outflow from EM stems from the strength of the U.S. economy and expectations of higher rates, rather than a typical “risk-off” flight caused by a global downturn.

  • Country Selection: Investors with exposure to Romania, Malaysia, South Africa, and Hungary should be alert to currency volatility, rising bond yields, and possibly weaker equity markets.

  • U.S. Bias: Because the U.S. is receiving inflows, this can support the dollar and U.S. assets broadly. Investors might tilt portfolios to reduce EM currency risks if the “sudden stop” scenario accelerates.

Ultimately, while JPMorgan’s warning might not cause an immediate crisis, it underscores how quickly capital flows can shift when the U.S. is growing robustly and raising rates. That dynamic can create clear winners (U.S.-focused assets) and losers (EM countries most reliant on foreign capital) in the near to medium term.

Economy

Two interesting headlines back to back in the Wall Street Journal…

My thinking is that we have two economies, one for the rich, who keep getting richer, and one for the poor, who are getting slammed by inflation leading to higher credit card balances, which leads to a spiral. The big question is whether that will create a systematic event that brings the economy down. I asked GPT for it’s thoughts….

Below is a high-level economic perspective on whether rising credit-card balances—particularly among lower-income Americans—could “tip the economy” in a manner analogous to the housing crisis of 2008. The short answer is that while growing consumer debt is a significant headwind, it is unlikely to trigger a systemic meltdown on the scale of the subprime mortgage crisis—but it can still create broader ripple effects that hamper economic growth and disproportionately hurt lower-income households. Here’s why:

1. Understanding the Two “Economies”

  1. The Well-Off vs. The Struggling

    1. Wealthy households have generally benefited from rising asset values (stocks, real estate) and have more savings and investment income.

    2. Lower-income households have borne the brunt of inflation, with wages that don’t keep pace with rising costs of living. Many in this group are turning to credit cards to cover essential expenses, driving balances higher.

  2. Implications for Aggregate Demand

    1. Wealthy households can maintain or even increase consumption when interest rates rise, because they are less dependent on credit.

    2. Lower-income households need credit to sustain spending, so higher rates and bigger balances can translate directly into financial stress.

    3. Over time, if a large swath of consumers is forced to reduce discretionary spending to cover credit-card bills, aggregate consumer demand may soften.

2. Why 2025 Is Not 2008

  1. Nature of the Debt

    1. Housing Crisis (2008): The entire U.S. financial system became entangled with mortgage-backed securities and complex derivatives. Rapidly falling home prices wiped out collateral for trillions in loans, while banks were overleveraged. This posed an existential threat to major financial institutions and global markets.

    2. Consumer Credit: Credit-card balances, while large and rising, are relatively smaller in aggregate than the mortgage market. Lenders typically set lower credit limits and risk is dispersed among multiple issuers (JPMorgan, Capital One, etc.). The level of securitization, while relevant, is not as systemic or opaque as subprime mortgage-backed securities were.

  2. Bank Preparedness

    1. Post-2008 reforms (e.g., higher capital requirements, stress tests) have left major banks more resilient. If credit-card delinquencies spike, banks have bigger capital cushions and better risk modeling to absorb losses.

  3. Strong Labor Market

    1. So far, U.S. employment remains robust, which tends to keep consumer credit losses in check. Even if lower-income Americans are stretched, the ability to service debt is still supported by steady paychecks.

  4. Less Securitization Complexity

    1. In 2008, toxic mortgage debt spread globally via complex financial instruments. Today, credit-card debt is generally more straightforward: lenders hold much of the paper or package it in simpler asset-backed securities that are easier to track.

3. Still a Serious Concern—Why It Matters

  1. Disproportionate Impact on Lower-Income Households

    1. A significant share of the population is carrying higher monthly balances with near-record interest rates (around 20%+). This group also faces rising rents, gas, groceries, and other costs.

    2. As more income goes to debt service, discretionary spending falls, possibly dragging down sectors that rely on lower- and middle-income consumers (e.g., discount retail, certain services).

  2. Potential for a Consumer Slowdown

    1. Consumer spending is roughly 70% of U.S. GDP. If a noticeable segment of consumers cuts back, overall economic growth could slow, especially if inflation remains sticky and interest rates high.

  3. Broader Credit Stress

    1. While a direct meltdown is less likely, higher defaults on credit cards can spill over into auto loans, personal loans, etc. That can tighten financial conditions further, raising borrowing costs and reducing credit availability for everyone.

  4. Inequality and Political Risks

    1. The concern over a “two-tier economy” where the rich continue to flourish while the poor fall further behind can breed political tensions, policy interventions, or populist sentiments.

    2. Proposals like capping credit-card interest rates at 10% (as mentioned by the Trump campaign) are an example of how political forces might respond to rising consumer debt burdens.

4. Could This “Tip the Economy”?

  • Short Answer: Possibly into a mild or moderate recession, but not into a 2008-style collapse.

    • Housing’s Unique Role: Real estate is a far larger asset class. A meltdown in residential mortgages affects household wealth (home equity) on a massive scale, and banks hold huge amounts of mortgage debt on their balance sheets.

    • Consumer Debt Is Fragmented: Credit-card risk is more evenly distributed among many lenders, with credit limits that are relatively lower.

    • Countervailing Factors: Unemployment remains low, inflation—while still high—has shown signs of easing, and banks have built in bigger provisioning buffers for consumer credit defaults.

That said, if unemployment rises or an external shock emerges (e.g., a severe global downturn, geopolitical crisis, another pandemic wave), individuals already stretched by credit-card balances and inflation could default in greater numbers. That might not create a single “Lehman moment,” but a broader slowdown in consumer spending and retail revenues could still be a significant drag on the economy.

5. Policy & Macro Outlook

  1. Monetary Policy: If the Federal Reserve continues to keep rates high to combat inflation, credit-card APRs stay elevated, pressuring vulnerable households further.

  2. Fiscal Policy: In times of consumer distress, expansions of unemployment benefits or tax relief can cushion spending. If not forthcoming, defaults might climb more steeply.

  3. Long-Term Inequality: Persistent wage stagnation among lower earners, combined with inflation, erodes consumer confidence and spending power over time. In a worst-case scenario, growing inequality can weigh on long-term economic growth.

6. Conclusion

We likely have a two-speed economy, where well-off households see minimal disruption and continue to spend, while lower-income households face escalating financial strain from inflation and high debt service costs. This divergence alone may not cause a 2008-level systemic financial crisis, because consumer credit is neither as leveraged nor as interconnected as the housing/mortgage market was.

However, a persistent squeeze on lower-income households can slow consumption growth, denting GDP and contributing to pockets of mini recessions in certain sectors. If unemployment rises or interest rates remain high for an extended period, we could see more widespread pain and a deeper downturn—though still not likely a subprime-style collapse. The growing income and wealth gap, if left unchecked, poses long-term risks to social and economic stability, potentially undermining the broader economy down the road.

o1-pro

Investing in Europe?

I think the US is such a target rich environment right now I have avoided Europe, except for aerospace and defense (EUAD). GPT seems to agree, see below. What I am looking for are any situations akin to what happened in Argentina. Nothing yet.

Summary of Potential Beneficiaries

  1. Countries: The Netherlands and Ireland stand out as the big winners if EU reforms go through, because they already have business-friendly environments and are well-placed to attract more foreign capital. Germany and France might benefit to a lesser extent if they embrace cross-border mergers, though internal politics are constraints.

  2. ADR-Listed Companies:

    1. European Banks (Deutsche Bank, Barclays, BNP Paribas, SocGen) could see upside if cross-border banking consolidation or capital-markets reforms materialize.

    2. Exchanges (like Euronext, Deutsche Börse) would be prime beneficiaries if capital markets union actually simplifies listings and trading across borders.

    3. Multinational Industrials & Consumer Giants might see mild benefits via a more vibrant European economy but are less reliant on local reforms.

Overall, even if there’s talk of “making Europe investable again,” history suggests only limited or incremental reforms will occur unless a true crisis forces widespread agreement. For now, the best bet is on smaller-scale improvements (e.g., postponing new environmental reporting requirements) rather than a sweeping deregulation wave. Thus, while there’s upside potential if reforms really happen, investors remain justifiably cautious about Europe’s ability to deliver on these ambitions.

Should you be buying value stocks?

I keep seeing articles like this…..

Investing is now about themes, not styles. Value stocks in and of themselves give you no benefit. Undervalued, or not way extended, stocks that represent a theme is another story entirely.

We run TGLR, where one of the key themes is old economy companies that could benefit from integrating AI. If you want value stocks, this is the type of way I would look at it.

I asked GPT to give me a list of stocks that would be classified as value and have the potential to get the biggest lift from integrating AI. Here are the names…

  • IBM

  • GM

  • INTC

  • F

  • CAT

  • FDX

  • UPS

All of these companies have been classified (at various times) as value stocks by major index providers or research firms, thanks to moderate valuation multiples, established businesses, and reliable (though sometimes cyclical) earnings. Yet they each have meaningful “game-changing” AI potential (score of 8 or higher), whether in the form of autonomous vehicles, advanced logistics, predictive maintenance, or enterprise solutions.

Not saying you should buy these stocks, I do own IBM and INTC for full disclosure (and kicking myself for missing the CAT move, should have seen that one). I am saying is that is the type of analysis you want to be doing.

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