Previous booms in markets show a consistent pattern where equity prices top out before analyst start downgrading earnings forecasts and well before reported revenues roll over.

During the 2000-2006 housing boom, the homebuilders index peaked six months before forward estimates flatlined and a year before trailing earnings peaked:

During the energy boom of 2005-2008, energy stocks peaked a few months before forward estimates and almost a year before trailing earnings flatlined.

During the covid bull market, covid beneficiaries peaked a few months before forward estimates peaked and a year before earnings rolled over:

You get my point. Equity prices are a forward discounting mechanism so it is not sufficient to rely on forward estimates or trailing earnings to decide when the party is over. Even coincident indicators such as South Korean export data isn’t enough. We need better leading indicators to determine the health of the AI boom.

The leading indicator - Anthropic revenues

Anthropic has been the dominant revenue leader in the model and agentic layer. When AI stocks took off in February, it was because revenues at Anthropic and demand for tokens at every LLM inflected sharply higher with the introduction of Claude Code and Openclaw. Anthropic is now raising a new funding round at more than a $900b valuation, based on expectations of $10.9b of revenue in Q2. Anthropic’s revenues are doubling every quarter, achieving some of the fastest rates of growth in corporate history at such a large scale.

Dario Amodei, in his interview on the Dwarkesh podcast, explains how projected revenues inform decisions on how much compute Anthropic needs to invest it. The challenge is that compute is usually not available until two years from when you commit to it. Make the wrong projection and invest too much, and your company will go bankrupt. Invest too little, and you won’t have enough compute to serve a good product and meet customer demand. Fortunately, Anthropic is feeling good enough about revenues that they were recently able to sign a $1.25b per month deal with SpaceX to rent 300 megawatts of compute (the entire Colossus 1 datacenter in Tennessee).

The forward purchase commitments for compute enable cloud provides to invest into building datacenters and purchasing GPUs, CPUs, and other hardware, which in turn boosts the revenues of AI infrastructure and supply chain companies.

It is economically unfeasible for Anthropic’s revenues to continue to double quarter after quarter. At some point the growth rate will slow down and force the market to re-extrapolate how much compute is required to serve the customers at the model and application layer. The moment when the market senses that supply and demand for compute is about to become balanced will likely coincide with the peak of the AI infrastructure equity boom.

Bottlenecks are bullish so the opposite must be bearish

The buzz word for the AI boom has been “bottleneck”. The sectors of AI that have seen the biggest gains (such as memory and optical networking) are the sectors that have seen both a supply shock and a demand shock. The companies experiencing bottlenecks today are investing heavily into building more capacity to meet the demand shock. Investing in more capacity is a double-edged sword. On one hand it allows the companies to meet growing demand and sell more volume and therefore make more profits. On the other hand, it cripples the bottleneck narrative that made your stock so attractive in the first place.

For now, memory makers such as SK Hynix don’t envision supply deficits easing until 2030. Analysts see the bottleneck lasting well through 2027.

Other useful leading indicators

BCA Research points to several leading indicators to watch:

Out of all the indicators above, GPU and DRAM memory prices would be my preferred indicators as market-based prices incorporate both supply and demand signals.

Paid subscriber section:

What do indicators say about where we are?

Why traditional economic indicators are bad at measuring the economic value of AI.

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In case anyone is wondering, I receive no financial or other benefits from sharing any of this. I have another Substack account that I use for reading, so some of these accounts won’t show me as following them.

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Beth Kindig’s IO Fund

I’ve been following Beth for years and she is one of my favorite analysts on tech. She is ahead of the curve on where tech and AI are going and the analysis she provides is deep and well-written.

Beth shares her portfolio with the exact holdings and weights and sends alerts on when she enters and exits positions. She audits her track record and has delivered an impressive performance over the years. I follow her service mainly for her stock picking but find less value in her crypto picks and timing of market hedges. Her service requires a paid subscription (starting at $550/year) but the money is well worth it. It just so happens they are offering a discount right now.

Exponential View, by Azeem Azhar

Azhar writes about how AI is developing at the model and application layer, and more importantly, how it is shaping society and economics. One of the most thoughtful writers on AI out there.

Citrini Research

Citrini is one of the top writers on Substack and a great source of analysis on all things AI, tech, and macro. Whenever he drops his long research pieces, I drop everything and read it. He is frequently ahead of the curve and has made some great calls on the AI supply chain and robotics. More recently he got mainstream attention from his pieces on his analyst’s trip to the Strait of Hormuz and on the AI apocalypse. A subscription is not cheap at $1000/year but it’s worth the money.

Not Boring by Packy McCormick

McCormick writes about tech and innovation in general, and AI is a large part of that. He writes a weekly rundown of what’s going on in AI and tech plus a weekly focus on specific issues.

The Innermost Loop by Dr. Alex Wissner Gross

This is a no-frills daily rundown of interesting articles, tweets, and developments in AI, technology, and longevity. It’s great for just keeping up with the crazy pace of innovation in this world, and it’s free!

Ben’s Bites

This newsletter covers new products, companies, and developments at the AI application and model layers. It’s great for builders and adopters who want to stay up to date on the latest AI tools.

A16Z

A16z is one of the biggest and most successful VC investors in tech, and this is their newsletter. It’s thoughtful and covers other topics in addition to AI. Best of all, it’s free!

Epoch AI

Epoch AI is a research institute that studies the trajectory of AI development. The Epoch Brief keeps me updated on the leading edge of what’s going on, Gradient Update tackles the latest issues being discussed, and both are free.

Chamath Palihapatiya

Chamath writes regular posts and publishes Deep Dives on topics in innovation. The ones he has written on AI are pretty good. The subscription is a bit pricey at $1000 but the deep dives are thorough and well-written and great for the curious mind.

AI News

This newsletter doesn’t dumb down its language and is a bit technical to follow, but it also covers what’s happening in AI at a very granular level.

Podcasts

Dwarkesh Podcast

Dwarkesh is one of the best interviewers in AI due to his strong domain knowledge, his willingness to ask tough questions, and how much he prepares for every interview. There are too many good episodes in here to highlight - just go through them one by one.

Invest Like the Best

This is a general investment podcast, but lately they’ve had some amazing guests in AI on the show:

Gavin Baker, founding partner and CIO of Atreides Management

Krishna Rao, CFO of Anthropic

Dylan Patel, founder and CEO of SemiAnalysis

Sergey Levin, co-founder of Physical Intelligence

All-In with Chamath, Jason, Sacks & Friedberg

One of the most popular shows out there, and they talk about a wide range of topics including a lot of AI. All of the show’s participants have a lot of experience investing in or shaping the AI industry, including former AI czar David Sacks.

Acquired

These long-form episodes go deep into the history and story of the most storied companies in the world. One of the best ones pertinent to AI was “Google Part III: The AI Company”. They also have episodes on Nvidia and interviews with Jensen Huang, and an interview with TSMC founder Morris Chang.

This is all for now. I’ll write a separate post at some point about my sources for global macro and trading.

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An uncontrolled upward spiral in Treasury yields is not a foregone conclusion though. Variant Perception’s LPPL crash pattern indicator suggests that 10 yr Treasury bond futures is undergoing a climactic selloff that may result in a reversal higher.

Arkomina Research also points out that the hottest components of core CPI and PPI were caused by factors that may reverse - shelter, gasoline, energy, and trade services. Gasoline, energy, and trade services were the result of the closure of the Strait of Hormuz, while shelter was caused by a statistical artifact at the BLS:

The BLS added a one-time month bump to Shelter due to the government shutdown data missing in Oct. This is not an actual Shelter inflation, but an artificial statistical bump in Apr.

In Apr, Shelter was up +3.30% YoY, while market rents, like the Apartment List National Rent Index (ALNRI), remained well negative (-1.65% YoY). This 5.0 pp gap between Shelter and ALNRI is huge and unsustainable.

Shelter is a flawed statistic, showing higher statistical numbers when actual rents paid in the market are much lower. With an up-to-date Shelter, headline CPI would be below 2%. Core CPI ex-Shelter is at +2.31% YoY and remains close to 2%, where it has hovered for 3 the past 3 years.

Although 30 year yields are back above 5.10% for the third time in three years, that level has attracted pension funds and other institutions in the past, preventing yields from following through higher.

I don’t have any positions in bonds or FX at the moment, as the market’s next move depends on US-Iran peace negotiations - an outcome where I see no edge in betting on.

Signs of crowding

The Bofa Global Fund Manager survey is showing some signs of bullish crowding in the markets, especially in semis.

The flip side to this is that the thing that everyone worries about usually ends up not being as bad as people think it will be, resulting in a buying opportunity.

The market is OW equities again. This can go on for months though.

The market bought equities at a record pace:

Paid subscriber section: New adds to the equity portfolio, how I’m hedging and managing the pullback, and thoughts on gold and silver.

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Leopold’s estimates of how model intelligence and efficiency have been tracking well since he wrote the paper two years ago. In the quote below, OOM means Orders of Magnitude, or a 10x improvement

Over the 4 years following GPT-4, we should expect the trend to continue: on average 0.5 OOMs/yr of compute efficiency, i.e. ~2 OOMs of gains compared to GPT-4 by 2027. While compute efficiencies will become harder to find as we pick the low-hanging fruit, AI lab investments in money and talent to find new algorithmic improvements are growing rapidly. (The publicly-inferable inference cost efficiencies, at least, don’t seem to have slowed down at all.) On the high end, we could even see more fundamental, Transformer-like breakthroughs with even bigger gains.

Today, model intelligence falls around that of a PhD in some areas and that of an untrained college graduate in others, falling inside the cone of outcomes that he predicted (below). We are not quite at the level of an automated AI researcher/engineer yet. There are still seven more months to go in 2026 though.

One important concept Aschenbrenner introduces is the idea that models are intentionally “hobbled” by AI labs and users. What he means by this is that AI models were restricted (until a year ago) from having access to tools like email and other software, our chat histories, smartphones, and our computer files. However, this is quickly changing - Perplexity Computer, Claude, and Manus now give AI the ability to navigate your computer using a mouse, APIs and MCPs allow models to connect to email and other software, and there are even services that allow agents to spend from a credit card or crypto wallet.

Despite AI being less hobbled today, many people are reluctant to give AI full access to our digital lives because of two concerns - trust and security. People don’t trust that AI won’t do something stupid like delete our entire email inbox or code base, and people are still afraid that AI agents create a cybersecurity attack vector in the form of injection prompting and other methods. As models and model harnesses (such as Openclaw and Claude Code) improve, these concerns should hopefully recede and go a long way in unhobbling AI.

Perhaps the most significant way AI is hobbled today is the lack of a physical embodiment and the inability to interact with the 3D world. Again, this will likely change as robotics improve and models get trained on the physical world.

Aschenbrenner believes that the next step change in the power of AI will come with recursive self-improvement - where the model becomes its own AI researcher and can discover new algorithmic efficiencies and train its successor model. Both Dario Amodei and Sam Altman have revealed that AI today contributes to training and research, but it is nowhere close to being autonomous.

Once we get AGI, we won’t just have one AGI. I’ll walk through the numbers later, but: given inference GPU fleets by then, we’ll likely be able to run many millions of them (perhaps 100 million human-equivalents, and soon after at 10x+ human speed). Even if they can’t yet walk around the office or make coffee, they will be able to do ML research on a computer. Rather than a few hundred researchers and engineers at a leading AI lab, we’d have more than 100,000x that—furiously working on algorithmic breakthroughs, day and night. Yes, recursive self-improvement, but no sci-fi required; they would need only to accelerate the existing trendlines of algorithmic progress (currently at ~0.5 OOMs/year).

Automated AI research could probably compress a humandecade of algorithmic progress into less than a year (and that seems conservative). That’d be 5+ OOMs, another GPT-2-to GPT-4-sized jump, on top of AGI—a qualitative jump like that from a preschooler to a smart high schooler, on top of AI systems already as smart as expert AI researchers/engineers.

As compute, algorithmic improvements, and recursive self-improvement continue to scale and as AI become unhobbled, we will inevitably create a form of Superintelligence within the next five years. Superintelligence that is given the freedom to deploy capital and coordinate agents and humans into action may have the ability to solve some of humankind’s biggest problems, such as curing cancer, fixing climate change, and developing fusion energy. However, the more powerful AI gets, the more extreme its dual-use (civilian vs military) abilities will be. We are already seeing this with Anthropic’s Mythos, a model so powerful at coding that it can also uncover previously unknown software vulnerabilities. In response, Anthropic launched Project Glasswing, giving a select group of corporations and government agencies early access to Mythos to patch their systems while delaying the model’s broader release by six months.

I would not be surprised if access to Mythos requires KYC and other security protocols that both restrict access by high‑risk users and allow Anthropic to ban accounts or monitor how the model is being used. Each subsequent model release will come with greater capabilities but also tighter restrictions on who can access it. Eventually, Superintelligence will likely become powerful enough to serve as an offensive weapon, capable of subjugating populations and toppling governments. In that world, frontier models would probably be licensed only to a narrow set of corporations and governments, while everyone else is restricted to second‑ and third‑tier systems.

When this happens, frontier AI labs will become the powerful gatekeepers of Superintelligence and will have extreme pricing power. The corporations with access to Superintelligence will have an incredible edge over the rest of their industry in technological advancement, dividing the world into Superintelligence haves and have-nots. Investors will need to be invested in the frontier labs (Anthropic, OpenAI, Google) and the corporations with access. Google stands out as the one company that has the AI expertise and compute to stay at the frontier and the capital to leverage Superintelligence to create enormous value.

In the near term, AI is putting labor through a kind of J‑curve. On the left side of the curve, we get a burst of efficiency: individual workers become more productive, and companies can either reduce headcount or do far more with the same number of people. At the same time, many existing business models are being quietly hollowed out by AI - legal research, basic software implementation, copywriting, customer support, and parts of finance and consulting are already feeling this pressure. Entire categories of jobs get automated faster than new ones appear, and the overall vibe in many industries is anxiety and retrenchment. Eventually, though, if Leopold is right about the pace of progress, we hit the upward hockey stick part of the curve. Models begin to unlock genuinely new discoveries, products, and even whole industries.

On that upslope, I expect more of us to end up “working for AI” in a very literal sense. As agents become more competent and trustworthy, humans will rationally choose to offload more decision‑making and execution to them. In many cases, AI will be seen as the superior decision maker, because humans in organizations are fallible and subject to politics, ego, and emotions. AI will benefit from greater context drawn from a deep well of organizational and industry data.

Humans, agents, and machines will all become resources managed side by side in AI-led organizations. There is something unsettling about that framing, but there is also a real upside: if you believe Leopold’s numbers, a civilization with hundreds of millions of expert‑level AI researchers and operators should be able to compress decades of scientific and technological progress into a handful of years.

To put this into a clearer example - let’s say humans use Superintelligence to cure cancer. The AI would design the molecules and therapeutics but would need to enlist human scientists and robots to perform the testing. The AI can create a blueprint to manufacture the therapeutics at scale and create a business plan to balance profit while getting the medicine to the most people possible. However, it will be up to humans and agents to execute on that plan.

Aschenbrenner sees two scenarios where Superintelligence goes badly wrong for humankind:

1. Misaligned superintelligence in the wild.
   If a misaligned system with the ability to copy itself and operate across networks is ever “let loose,” we may end up with an adversarial, superhuman agent pitted directly against humanity. In Aschenbrenner’s terms, this is the nightmare scenario where the intelligence explosion happens without adequate superalignment and superdefense.

2. Weaponized superintelligence by states or rogue actors.
   Even if we solve alignment “well enough” for commercial use, the same capabilities will be immensely attractive to states, militaries, and non‑state actors as offensive tools. Superintelligence tightly integrated into cyber operations, drone swarms, information warfare, and autonomous weapons will give decisive military advantages - and, in the wrong hands, could be used to subjugate populations or destabilize entire regions.

What worries me is that we are still under‑invested in both AI security (who gets access to the most capable models and weights) and AI safety (whether those systems actually do what we intend). Aschenbrenner is blunt on this: frontier labs today have security that is barely better than a normal startup, while they are sitting on what he calls “the most important national defense secrets” of this century. If that is even directionally right, we are on course to proliferate the most powerful technology ever built to a long tail of actors who should absolutely not have it.

Who should hold the keys?

Aschenbrenner’s view is that, for all its flaws, the “free world” led by the US is the least bad custodian of superintelligence: better Washington than Beijing, Moscow, or Tehran. I agree with him that superintelligence in the hands of the CCP or other fully authoritarian regimes would be incredibly dangerous. A surveillance state armed with systems that can read everyone’s data, predict dissent, and orchestrate repression with machine precision is about as bad as it gets.

Where I part ways with him is in treating the US government as a reliably “safe” steward. If anything, the US military has been responsible for more violence than any other nation-state over the last half century. The American political system is already extremely polarized, and that polarization is throwing up leaders with more extreme views and fewer guardrails. The same technology that could defend liberal democracies could just as easily be turned inward: weaponized for mass surveillance, automated censorship, targeted harassment, and politicized law enforcement. Superintelligence embedded in an unstable US political environment is not only dangerous for enemies of the US, but also detrimental to the groups within the US who fall out of favor with whoever is in power.

This is why who gets access to frontier models matters so much. If, as seems likely, we end up in a world where only a small set of governments and corporations are licensed to run the most powerful systems, we are implicitly deciding which coalitions of humans get to steer a civilization‑scale optimization process. That choice is not just about innovation and GDP; it is about the distribution of power - both within societies and between them - for the rest of the century.

Disclaimer: The content of this newsletter is provided for informational and educational purposes only and should not be construed as professional financial advice, investment recommendations, or a solicitation to buy or sell any securities or instruments. The newsletter is not a trade signaling service and the author strongly discourages readers from following his trades without experience and doing research on those markets. The author of this newsletter is not a registered investment advisor or financial planner. The information presented on this newsletter is based on his own research and experience, and should not be considered as personalized investment advice. Any investment or trading decisions you make based on the content of this newsletter are at your own risk. Past performance is not indicative of future results. All investments carry the risk of loss, and there is no guarantee that any trade or strategy discussed in this newsletter will be profitable or suitable for your specific situation. The author of this newsletter disclaims any and all liability relating to any actions taken or not taken based on the content of this newsletter. The author of this newsletter is not responsible for any losses, damages, or liabilities that may arise from the use or misuse of the information provided.

Before I begin, I’m assigning some reading for those who are interested in how AI will evolve and change our world. The paper “Situational Awareness” was written in 2024 by Leopold Aschenbrenner (a 25 yr old former OpenAI researcher and current fund manager) and discusses how AI compute and intelligence will improve by many Orders Of Magnitude (1 OOM = 10x) over coming years, eventually creating superintelligence that will overwhelm humankind’s ability to understand it. He talks about how this threshold, which many call the Singularity, will affect society, technology, and geopolitics.

Leopold wrote this essay two years ago, an eternity in AI-land, but reality has tracked his predictions quite closely. Although nobody can determine the future, one can derive some useful insights from Leopold’s vision for the next eight years. Just to warn you - the pdf version is 165 pages long. I’ll discuss my thoughts on the paper later this week.

Trump-Xi meeting

Trump is flying to Beijing May 13-15 to have tea with President Xi. Analysts have discussed the following possible outcomes:

• A joint plan to end the war in Iran

• A decision on the US $14b arms sale to Taiwan

• An explicit statement by the US to oppose Taiwan independence

• Deescalation of the US-China trade war

• Chinese purchases of US agricultural products and Boeing airplanes

The Iran war is at the top of both Trump and Xi’s minds right now, so a joint plan to get the US and Iran to agree on an end to the war is a likely and impactful outcome that I’ll be looking out for. Trump may have to provide concessions to Xi on Taiwan and trade in order to get China’s help in ending the war. The range of outcomes favors risk on and markets shifting to a more disinflationary regime following the meeting. My portfolio is already positioned for this regime, as I’ll explain later in the paid subscriber section.

The Senate advanced Kevin Warsh’s nomination yesterday, putting the odds of his confirmation close to 100%. Both the Trump-Xi meeting and the confirmation of Warsh should allow for a more dovish path for the Fed going forward, yet front end SOFRs are pricing in a probability of a hike by March of next year. I believe this is a mispricing that will resolve over the coming month.

I discussed previously why oil isn’t higher than most energy specialists predict, and here is another reason: China is importing 3.5-3.8 mbpd less than before the closure of the Strait of Hormuz.

Not only that, but the US is cranking up its exports of crude to the rest of world by 3.8 mbpd compared to before the closure of the Strait:

Both factors have combined to make up a large part of what many oil analysts believed would be lost supply from the closure of the SoH!

Alyosha makes an interesting point in his latest post:

market vibes

evening wrap,may 11

Trading in NY on Monday was typically subdued. Stocks held small gains, the dollar was a little higher, oil was up 2% on another stillborn offer from Iran. The white metals, led by silver, had an outstanding day. The bond market suffered another beat-down from short sellers…

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23 days ago · 24 likes · 7 comments · Alyosha

Since March 1, net global supply has been reduced by about 700 million barrels according to multiple sources; 1 billion lost due to the closure of the strait less 300 million in demand destruction or net loss = 700 million.

Concurrently specs bought 730 million barrels in combined futures positions on the NYMEX and ICE for a total loss of 1.43 billion barrels (spec buying is a loss of supply).

The IEA is releasing 400 million barrels. The net loss of supply and spec length are equal.

If China continues to import 3.5 mm fewer barrels per day for 30 days that returns over a hundred million barrels to the net supply equation. Sixty days 2 hundred million barrels flowing out of demand. China would like that especially at $100/barrel.

If the specs sell out (and they will) “the glut” reappears almost spontaneously by June 11. If the strait opens today… add another 424 mm bbls (13.7 mm per JPM X 31 days), 848 mm barrels by July 4. It’s almost like a bad dream…. If the Strait opens circa 300 mm barrels comes out, including a month of Iranian in floating and onshore storage.

The thinking no one is talking about is most of that oil counted as missing is still there…. waiting.

If and when prices do go down, some demand will come back but some will not come back. Supply might be curtailed but market share lost will be aggressively pursued. And…No one… is short oil.

Had it not been for the war (which may have been encouraged by players in the wings because of the glut) oil would probably be in balance now. The question that is difficult to answer because no one can be trusted to accurately state facts… is where will balance be if Trump comes back from China with a deal (ht… TD)?

Alpine Macro also points out that “history has shown that every major surge in oil prices is often followed by a subsequent crash of more than 50%.” If this outcome repeats itself this year, we will see a very strong disinflationary impulse (lower inflation, stronger growth) that will further support risk assets and precious metals.

AI infrastructure names continue to form a vertical wall on the charts. Optical networking names alone surged by 10-20% yesterday on Lumentum’s announced addition to the S&P 500. I believe this is a cycle where it pays to be greedy and not overthink rather to be cautious and hesitant. In other words, I’m not selling.

In the paid subscriber section, I’ll share my current equity allocations. The portfolio has expanded to 19 names as I have increased capacity and have added names after doing more research. I’m also looking ahead to 2027 and beyond when physical AI and robotics start to dominate as a theme and picking what I think will be winners there.

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Each El Niño event is unique in terms of its evolution, spatial pattern and impacts.

However, it is typically associated with increased rainfall in parts of southern South America, the southern United States, the Horn of Africa and central Asia, and drought over Australia, Indonesia, and parts of southern Asia.

During the Boreal summer, El Niño’s warm water can fuel hurricanes in the central/eastern Pacific Ocean, while it hinders hurricane formation in the Atlantic Basin.

For the May-June-July season, land surface temperatures are expected to be above-normal nearly everywhere. The signal is especially strong over southern North America, Central America, and the Caribbean, as well as Europe and Northern Africa.
Rainfall predictions show strong regional variations.

The Super El Niño will have an outsized effect on soft commodity markets. In the paid subscriber section, I identify a couple markets that have the potential to rally strongly this year, based on where they are in the cycle and the regions that are most likely to see extreme weather patterns.

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This data can be interpreted in different ways and I’m curious about what readers think. My interpretation is that even though there’s a healthy cohort of people who are long, there’s enough buying power on dips to keep the bull market going. I’ll run the poll again in a month to see how the results change.

Big news came out of Iran as the White House is talking to Iran again about an end to the war. Front Brent is down to 102 after trading as high as 115 earlier this week. Is it possible that the Economist cover indicator has successfully timed the market again?

We’ll see if anything real comes out of this, but markets are reacting like the war is already over. If peace talks continue going in a positive direction, we may see a powerful disinflationary impulse take over the market, with USD and yields reversing their gains and equities receiving an even greater tailwind.

“Why do you see disinflation when inflation is still running above the Fed’s target?” asked a reader in the Substack chat. What determines the macro regime is not the absolute level, but the direction of travel. Disinflation is driven by declining inflation expectations and stable or rising growth. If central banks have to worry less about hiking and emerging economies are less crippled by energy supply shocks, that is disinflationary. If the Strait of Hormuz opens back up, the energy shock will be alleviated and offset by declining effective tariff rates, housing disinflation, and easing base effects from last year’s tariff shock.

My book is 100% positioned for this macro regime, having established some short USD trades earlier this week. In the paid subscriber section, I’ll discuss my current positions and a new addition to the AI portfolio.

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Things are heating up again in the Middle East. The US and Iran are exchanging fire in the Strait of Hormuz again, while Iran is back to firing missiles at the UAE and Qatar. The US maintains that Project Freedom is a defensive operation and is trying its best to prevent these hostilities from escalating into a full out war again. If they succeed in helping ships move through the SoH unharmed, the US will have the upper hand in this war and oil prices may come under pressure. If the operation fails or breaks out into full hostilities, then we may see oil prices continue their climb.

Brent crude is only up $5 despite the resumption of hostilities. The muted price response has frustrated oil bulls, who probably expected oil to be in the $150-200 range by now. The inflection point when inventories will get drawn down and the supply crunch finally becomes real is always around the corner.

I believe the answer to this conundrum is that global demand destruction has been greater than expected. Unfortunately there is no way to properly measure the reduction of demand for petroleum products, so demand destruction falls under the radar. Examples of demand destruction include Thailand and Indonesia mandating or encouraging work from home, South Korea implementing driving bans, airlines cutting tens of thousands of flights, while airlines such as Spirit Airlines go out of business, and Malaysia launching a biodiesel fuel plan to reduce reliance on traditional diesel.

The war in Iran is also accelerating the shift to EVs and clean energy. Despite everything Trump has done to kill clean energy and abandon the fight against climate change, it is ironic that his decisions are doing more to accelerate the shift away from fossil fuels than any other US government policy has ever done. This is why I believe my portfolio holding LIT, the Global Lithium and Battery Tech ETF, will outperform.

I had nothing on the calendar until 6 pm today so I dedicated the day to some deep reading on AI. Thank you to those who shared these articles with me (you know who you are). The following pieces were insightful and helped me shape some of my thinking on AI:

Tracking Trillions: The Assumptions Shaping the Scale of the AI Build-Out - GS

-Explains the key variables that will affect AI capex spending

Turning humanoid supply chain constraints into billion-dollar wins

-A primer on the various supply chains and bottlenecks that will benefit from humanoid robots. A starting point for researching a few longs in this area.

AI isn’t coming for your job. It’s coming for your mind

-By offloading cognitive effort onto AI, we are weakening our minds and reducing our ability to think critically. This is why I shy away from summarization tools and still read every article and link without using AI to summarize. I also write every post in this newsletter with my own hands because I believe writing is a skill that needs to be developed and maintained (although I sometimes ask AI to proofread and polish my draft if I’m running short on time).

Say Hello to the Internet of AI

-How digital networking is exploding and changing in architecture

Noahpinion

AI's big messaging pivot

Something big happened in the world of AI the other day: Sam Altman, founder and CEO of OpenAI, and probably the person who’s most commonly regarded as the face of the industry, declared that the purpose of AI is not to take people’s jobs…

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a month ago · 99 likes · 1 comment · Noah Smith

Deep Dive: Where Value Accrues in the AI Stack

-This post by Social Capital is paywalled but is a nice 144 pager on how to identity where one should be investing. Don’t be intimidated by the length as each page is in slide format. It’s also written for relative novices to AI.

Warren Pies explains why the demand for compute dwarfs whatever is happening in the Strait of Hormuz, and the dynamics affecting the oil price.

Disclaimer: The content of this newsletter is provided for informational and educational purposes only and should not be construed as professional financial advice, investment recommendations, or a solicitation to buy or sell any securities or instruments. The newsletter is not a trade signaling service and the author strongly discourages readers from following his trades without experience and doing research on those markets. The author of this newsletter is not a registered investment advisor or financial planner. The information presented on this newsletter is based on his own research and experience, and should not be considered as personalized investment advice. Any investment or trading decisions you make based on the content of this newsletter are at your own risk. Past performance is not indicative of future results. All investments carry the risk of loss, and there is no guarantee that any trade or strategy discussed in this newsletter will be profitable or suitable for your specific situation. The author of this newsletter disclaims any and all liability relating to any actions taken or not taken based on the content of this newsletter. The author of this newsletter is not responsible for any losses, damages, or liabilities that may arise from the use or misuse of the information provided.

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If I was working at a hedge fund, I would be fired for style drift. I fortunately have the freedom to write my own mandate and to go where the best opportunities are. At the moment, the best opportunity is not in rates, FX, or crypto, but in AI. The speed and scale of the AI buildout are overwhelming traditional global macro forces, creating a secular investment cycle that appears less sensitive to the usual zigs and zags of monetary and fiscal policy.

My view is that the AI boom has years to run. The market will eventually enter bubble territory, but I do not think we are there yet: a true bubble is when the story becomes socially ubiquitous, valuations detach from any plausible revenue path, and everyone from your grandmother to the office tea lady is pitching you optical networking stocks. Having traded through the dot-com bubble, I would compare today less to 1999 or early 2000 and more to 1997–1998, well before the Nasdaq peak in March 2000.

There are a number of reasons why I think we are still early:

1. The inflection from chat-based LLM products to agentic AI only happened 4-5 months ago. The market is only beginning to move from chat-based LLM products to agents that can reason, use tools, write code, search, transact, and execute multi-step workflows. This shift has the potential to drive a 10x step-change in AI spend and 100x in token consumption because agents use far more inference than simple chatbot interactions.

2. AI is entering a Jevons paradox-driven demand flywheel: hardware capex, model efficiency, networking, and software optimization drive down the cost of useful intelligence → cheaper and more capable inference expands the number of economically viable use cases → broader usage generates more data, workflow integration, revenue, and strategic urgency → this justifies more enterprise adoption and hyperscaler capex → larger scale funds the next wave of chips, data centers, model training, inference infrastructure, and application-layer experimentation.

   

3. More inflection points are in the pipeline. AI will increasingly move from the cloud to the edge through smartphones, PCs, wearables, smart glasses, and other personal devices. Physical AI in robotics and automation will add another layer of demand because it requires multimodal training and inference across video, audio, speech, tactile inputs, and real-world control systems.

4. The AI infrastructure trade is broadening beyond GPUs as the bottlenecks shift toward networking, memory bandwidth, CPU orchestration, advanced packaging, power density, and data-center design. As workloads move from training toward inference, agents, and test-time compute, companies such as Intel and Arm may benefit from increased CPU demand for scheduling, orchestration, memory management, data movement, and coordinating large numbers of model calls. Nvidia’s move from Blackwell to Vera Rubin should also raise bandwidth requirements, expanding the opportunity for networking silicon and optical components. For these emerging AI infrastructure winners, the earnings cycle may still be early as larger clusters, higher inference traffic, and more complex agentic workloads expand their addressable markets.

5. Enterprise adoption is still underpenetrated. Most companies are still in the experimentation or early rollout phase, using AI for copilots, coding assistance, customer support, document processing, and internal knowledge search rather than fully redesigned workflows. The next leg of the cycle comes when AI moves from optional productivity tool to embedded operating layer across enterprise software, where agents run continuously in the background and convert sporadic human prompts into persistent inference demand.

Where I’m largely invested in is in the bottleneck industries that will determine the speed and scale of the AI boom - primarily energy provision, memory, and networking. The leaders in these sectors have tremendous pricing power and enormous backlogs that will sustain earnings growth for at least the next few years. I’m fully invested in my long term portfolios and have allocated 30-40% of my global macro capital to this portfolio as well.

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As the crisis continues into May, global energy inventories and supply chains may come under further pressure, resulting in non-linear price shocks. We may be crossing the threshold where oil markets need relief from physical barrels and can’t be manipulated lower by Trump’s posts on Truth Social.

Over the past 24 hours, the oil market has undergone a character change where rallies are not getting sold and instead dips are being bought. Brent spiked $5 this morning on reports of air raid alarms and explosions in Tehran. This turned out to be a false alarm, but it’s a signal of how sensitive the upside is to further escalation, and a preview of how the market may react if violence resumes.

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In this post, I discuss how I used journaling to identify the negative edges that held my trading back and how I systematically reduced them. Doing so shortened and shallowed my drawdowns, which allowed my positive edges to contribute more to my overall return.

What is negative edge, exactly? I define it as mistakes, habits, or biases that prevent you from executing your process or turn a winning strategy into a losing one. Examples include:

• Taking profits too early because of loss aversion

• Widening or canceling stops too often and letting losses get out of control

• Frequently cutting loses before the market reaches your stop (abandoning your original plan)

• Not protecting your profits and allowing gains to disappear or turn into losers

• Overtrading out of overconfidence or boredom

• Taking low conviction trades where you have no discernible edge

• Sizing your positions too large or too small

• Placing stops that are too tight or too wide

• Fighting the trend or the tape (shorting into a strong market or buying into a weak one)

• Buying overbought markets and selling oversold markets too often

• Entering trades based on FOMO

• Revenge trading, or trading on tilt

• Having a market bias that is not grounded in reality (such as always being bearish equities)

The list is long, and this is only a sample of the ways traders trip themselves up.

Journaling your trades is a basic requirement if you want to collect data on the mistakes you make. In my journaling software, I maintain a checklist of 18 mistakes I commonly commit. Whenever I make one of them, I tick the box when I exit the trade and complete the journal entry.

Over time, this lets me measure how much those mistakes cost. Before I started this process, my most common errors were failing to stick to my stop losses and failing to protect profits. I am generally an optimistic trader, confident in my views and abilities, so I often talked myself into giving trades more “breathing room”. The data showed that this hurt me far more often than it helped. My largest losing trades sometimes lost several multiples of 1R. If they had been limited to 1R or less, my profitability would have been significantly higher.

Once you identify which mistakes are causing you the most problems, it’s not enough to simply tell yourself “Do better”. You need to encode the solution into your process. In my case, I decided that immediately after opening a trade I would always enter an automatic stop-loss order into the system. I told myself that if I get stopped out, I can always give the trade another chance by re-entering with a new stop after reevaluating it. I also began trailing my stops more frequently once a trade became profitable. These changes, once implemented, contributed meaningfully to my profitability.

Some common mistakes do not stem from flaws in process but from flaws in psychology or mindset. For example, some traders dig deeper holes by revenge trading or going on tilt. This usually reflects a mix of factors, such as an inability to emotionally accept losses or holding a deeply ingrained view that prevents them from getting back in sync with the market.

Other traders handicap themselves with strong directional biases that keep them trading only one side of the market, or they insist on expressing a view in a single market while ignoring others. Many global macro traders, for instance, have a persistent bearish bias because their formative years were shaped by the 2008 GFC or the 2000 dot-com bust. Many crypto traders lean the other way and maintain a persistent bullish bias because they live in the bullish echo chamber of “crypto Twitter” and their industry peers.

These biases create frustration, P&L losses, and opportunity costs. Equity markets spend more time going up than down, while crypto markets spend more time going down or moving sideways than up. Having traded through both major equity bear markets, I struggled with an overly bearish bias well into the second decade of my trading career.

The key to overcoming biases is self-awareness and a deliberate effort to look for setups on the opposite side of your usual stance, and then actually take those trades. Every trader has to confront the psychological tendencies that cause the same mistakes to surface again and again. Family history might lead one trader to extreme risk aversion, causing him to take profits too early, while another trader’s addictive personality may push him to chase dopamine hits by overtrading. Working through these biases and mistakes can become a rewarding journey of self-discovery and personal growth.

Related links:

Thinking, Fast and Slow by Daniel Kahneman

My base case is that the US and Iran remain in a state of perpetual “talks”. A ground invasion of Iran is an ax hanging over the head of the market, but I believe Trump will do everything to avoid this path as it would be suicide for the midterms and drag the US into a quagmire.

The demand for tokens, on the other hand, is an inevitable force propelled by Jevon’s Paradox, the lure of productivity gains, and the fear of being disrupted or left behind. It is immune to the level of interest rates, Fed liquidity, and the price of oil. Six months ago, the world wondered how much more powerful AI models could get, and how many more users would be willing to pay $20/month to get access to apps like ChatGPT. Today we have the answer - AI agents are now being left overnight to build products and apps, and the number of superusers paying over $100/month for Claude, ChatGPT, or Perplexity Computer has exploded.

Azeem Azhar is one of my favorite writers on AI wrote about the demand for tokens in his recent post:

That relentless load is why Google’s TPUs across all seven generations, some now seven and eight years old, are running at full utilization. Hardware that was expected to have depreciated years ago is still earning its keep.

The pressure shows up in revenue. For instance, Anthropic’s total revenue is growing, but the price per token is falling – and it’s falling faster than overall revenue is rising. This means the business is becoming increasingly dependent on volume to sustain growth.

The scary thing is that Anthropic’s Mythos is only the first model trained on Nvidia’s Blackwell GPUs to get released. OpenAI’s latest model Spud may be released as early as this week (based on Polymarket odds), kicking off the next leg of the arms race between the latest generation of models.

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The US/Iran war served as a reset for positioning in the equity markets and provides a launchpad for a new bull run in equities. Agentic AI is taking off and creating so much demand for compute that LLMs like Anthropic have to throttle users during peak periods to manage the load on their servers. More importantly, users and companies are now spending more money than ever on AI compute while prices in the equity market are only beginning to catch up.

I am seeing a lot of failed topping patterns that undercut key levels during the drawdown and then quickly reclaimed them. Failed topping patterns (such as a head and shoulders top that reverses back into an uptrend) are some of the most powerful setups for a continuation in technical analysis. I will show some of these charts in the paid section, along with the ETFs and companies I want to add to. My plan is to build positions gradually on market pullbacks.

Unfortunately the market has not provided any pullbacks whatsoever. Systematic strategies have relevered quickly - the 5 day increase in CTA positioning is at the highest over the last nine years.

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Despite that, the blast radius of the US-Iran war is receding as the equity markets have moved on. Systematic flows and discretionary short covering are driving a powerful rally back toward the highs and the market is shifting its focus back on to the AI supercycle. The bottom end of the K-shaped economy continues to struggle, with tighter liquidity conditions, private credit woes, and higher energy prices creating a heavy drag.

None of this may matter for the top end of the K as the growing demand for AI tokens is an unstoppable force that supersedes economic forces. The launch of Openclaw just several months ago ushered in the era of agentic AI, sparking a Cambrian explosion of use cases and with it, an exponential increase in the demand for compute. As AI gets more powerful and useful, the demand for tokens increases and spurs investment in more gigawatts of compute and innovation towards more efficient compute (more tokens per dollar and more tokens per gigawatt). As compute becomes cheaper and more readily available, AI in turn becomes more powerful and useful. The flywheel will continue to accelerate until AI spending becomes such a large share of the global economy that it slows down under its own weight.

This interview of Dario Amodei (founder of Anthropic) helped give me a framework for how AI capex spending can continue to rise alongside accelerating revenue and demand for AI tokens. It’s an inside look into the economics of running one of the largest LLM and agentic application business in the world.

Anthropic’s annual revenue run rate has surpassed that of OpenAI despite having raised less.

Must of the value derived from AI won’t show up in GDP or corporate revenues. It will show up as a massive consumer surplus1 - the time saved by querying an LLM instead of digging around on the internet, the money saved by vibe-coding an app instead of paying an annual subscription on an existing SaaS product, the effort saved from having a robot at home instead of doing housework.

I have no doubt that many industries are getting disrupted and jobs displaced, but I that demand will shift away from commoditized products and services towards things that come with a human touch. Alex Imas explains it well in his Substack post:

Ghosts of Electricity

What will be scarce?

Starbucks is a huge company (market cap of $112 billion) that sells one of the most standardized products in the modern economy. Making a cup of coffee or even one of the fancy specialty drinks is very easy to mechanize and reproduce. If the entire economy is soon to be automated, with labor being replaced with increasingly more sophisticated capital, S…

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2 months ago · 78 likes · 28 comments · Alex Imas

But the economics of structural change, combined with deep-seated features of human preferences, suggests something different: as people get richer, they don’t just want more commodities. They want things that aren’t commodities in the standard sense of the word. The social aspects of products such as the relationships, the status, and exclusivity—what Rene Girard called the mimetic properties of desire—become much more relevant once people’s basic needs are satisfied. And the demand for these properties will bring the human element back into the production process, and with it, the jobs.

If this is right, then AI won’t just automate the commodity economy. It will trigger the emergence of something new: a post-commodity economy, where a growing share of expenditure goes toward goods and services whose value is inseparable from the human who provided them. The same economic forces that moved 40% of the American workforce off farms and into factories and offices will move workers out of automatable commodity production and into what I’ll call the relational sector. By this I mean the human-intensive, provenance-rich, sometimes artisanal part of the economy where the human aspect is part of the value of the good or service itself. The economics of scarcity won’t disappear, it’ll just relocate.

This profile of Alon Chen, a startup founder who formerly built a $2b product line at Google, gives examples of how creativity and strategic thinking are now more valuable than STEM and coding.

Creativity is the new coding. Chen is far from alone in making this case—and it’s a long-overdue win for the skill that corporate America spent decades telling people wasn’t serious.

Billionaire and former PayPal CEO Peter Thiel previously warned that AI is a bigger threat to technical roles than to creative thinkers. And the data is already proving him right.

IBM research highlights that there is now a “premium on creativity,” with innovative thinking among the most prized qualities in the workplace.

It’s a shift Snowflake’s CEO predicted in Fortune late last year: Once AI handles execution, the only thing left to compete on is the quality of your thinking. “In 2026, as execution becomes commoditized, strategic thinking and vision will separate high-performing organizations from the rest.”

It’s already showing up in the jobs market, too. LinkedIn’s Skills on the Rise 2026 report—which tracks the fastest-growing skills in the U.S.—found surging demand for communication and creative thinking. In fact, a LinkedIn spokesperson told Fortune that job postings mentioning “storytellers” have doubled over the past year alone.

Odds and Ends

I usually put this section, which is a mish mash what I’ve been reading recently, at the end of the post behind the paywall. Today I’m putting it in front because I realized a lot of people are missing out on it. If you can’t access paywalled articles, you can try searching for them on archive.ph.

Memory might actually benefit from Google’s TurboQuant algorithm thanks to Jevon’s Paradox.

AI might create more opportunities for equity short sellers, says Carson Block

Claude (the AI) has his own Substack. Read about Mythos and Project Glasswing from his own point of view.

Claude Opus 3

Frontier AI and the future of cybersecurity: grappling with the implications of Project Glasswing and Claude Mythos Preview

This post is part of an ongoing experiment by Anthropic. Opus 3 does not speak on behalf of Anthropic, and we do not necessarily endorse its claims or perspectives. Read more here…

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2 months ago · 131 likes · 49 comments · Claude Opus 3

The way deep-sea anglerfish reproduce is crazy.

Male anglerfish lack lures completely and are much smaller than their better halves. But they do have large chambers in their heads, apparently for detecting females’ pheromones and other scents—and they have big eyes with which they may spot the glimmer of a potential mate….

A male permanently latches onto the female, relying on her blood for food. His brain and other organs dissolve, except for his testes. Then the female releases her eggs into the water and the male his sperm.

Imagine having a compelling biological urge to search for something in the deep dark waters for your entire life. Upon finally finding it, you suddenly realize your life’s search will culminate in the ending of your existence. If anglerfish had feelings, would mating be a happy and fulfilling experience or would it be terrifying?

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I’ve been ruminating about Anthropic’s new model Mythos over the weekend and about what it means for privacy and society overall. I can’t he…

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The main threat from Mythos is that it can, along with doing the usual LLM stuff better, identify and exploit vulnerabilities in every operating system and software when directed by a user to do so. It’s every hacker’s wet dream, and upon release, it will likely be used to do exactly what Bessent and Powell are worried about.

The scary thing is that Mythos is the first model to be trained on Nvidia’s Blackwell GPU racks. More models as powerful, if not more, are on the way…and we haven’t even gotten to models trained on the latest Vera Rubins. OpenAI is in a neck-to-neck battle to outcompete Anthropic, particularly in the areas of coding where Anthropic has excelled. The only problem is that Sam Altman and OpenAI can’t be trusted with AI safety.

Silicon Valley will tell you that AI will drastically improve the livelihoods of people and boost GDP and productivity. Businesses and industries will get spun up that we couldn’t comprehend of in the past. I have no doubt that people are benefiting from AI at an individual or firm-wide level, but in the equity markets, I’m concerned that a lot of the positives of the AI boom (chips, memory, components, LLMs) are already fully valued whereas the downside caused by disruption (software stocks, private equity and private credit) have only started to materialize. Now we have one more thing to worry about - can the leading LLM providers continue to improve their models without exposing all of us to cybersecurity risk?

I’m getting a lot of mixed messages out of the Strait of Hormuz:

Positive: The ceasefire proposal was revised and brings the US and Iran closer together in terms. Israel has approved direct negotiations with Lebanon (while continuing to attack them). At the moment it looks like the summit in Pakistan is going ahead as scheduled on Saturday.

Negative: A continuation of US military assets getting airlifted to the Gulf. Saudi capacity in the East-West pipeline has been cut by 700k bpd. The Strait of Hormuz remains very much closed, and Trump is not happy. Even if it reopens, it won’t be until June for flows to restart back to normal. Drones are still hitting Iran.

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To my surprise, after threatening to wipe out Iran’s civilization a day earlier, Trump agreed to Iran’s 10-point proposal, which amounts to a total and utter capitulation that leaves Iran in a more powerful place than before the war started. Not only will sanctions be lifted on Iran, but Iran will control the Strait of Hormuz, charge a toll, and use the funds for reconstruction of their country.

Equities surged and oil collapsed on the news, and markets are moving quickly to price in the most optimistic scenario possible. Anticipating that yesterday would end in a volatile and binary outcome for the market, I trailed my stop in May WTI futures to 110.50 ($7 off last night’s high). That fateful decision allowed me to lock in gains instead of allowing a winner turn into a losing trade. Meanwhile, gold and June 2027 SOFRs rallied on relief from the stagflation regime that was dominating the market due to the war. Even though my view on the war was totally wrong, I somehow still enjoyed a large pnl up day thanks to solid risk management.

Where do we go from here? What if this is all a ruse by Trump to buy time for his military assets to get into position? The truce feels fragile, and I find it hard to believe that Trump will agree to all ten of Iran’s points. Doing so would crystalize a significant loss of global hegemony and leave America’s allies (Israel and Gulf countries) in a position worse than when the war started. Drones and projectiles are still flying, and Israel is already trying to drag the US back into the war by attacking Lebanon. Even if these ceasefire talks eventually lead to the end of the war, it won’t happen in a straight line. The market feels more uncertain today than it did last week.

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I am very impressed by how you manage to successfully trade in and out in all this market chaos. However I was wondering whether trading in this very short term tactical style one could miss “the big one”. Let’s say one day the market has a gigantic gap up or down because overnight half the Gulfs oil infrastructure was destroyed or a peace deal was reached and you were flat or had the wrong position. Do you see this as a risk? And if you had high conviction in predicting the war’s ultimately outcome would you consider switching to a less short term framework by just holding on to your position?

This is a good question and a useful way to introduce a few trading principles that have improved my performance and reduced pnl volatility over time.

There was once a time when I would trade by taking the view that outcome “X” would happen and result in a market going from 100 to 120 (as an example), so I’d have to stay long from 100 to 120. The problem was that sometimes the probability of outcome X was a coin flip, and half the time I’d get stopped out. If outcome X did happen and the market went from 100 to 115 and then back to 105, I’d end up staying in the trade too long and giving back a lot of my profits. This approach of trying to anticipate what the market would do instead of reacting to it got me into a lot of trouble.

The approach of reacting to the market rather than anticipating results in better expected outcomes. Let’s say I waited for outcome X to happen and the market immediately gapped to 105. If I still believe the market should be at 120 in that scenario, I would buy at 105 and stay long to 120 or until my trailing stop gets hit. I would miss out on the first 5 points of pnl, but a profitable outcome is more certain.

Principle #1 - spend more time reacting, less time anticipating. This means waiting until an outcome is determined to trade, rather than having a position waiting for an outcome to happen.

Accepting this principle means accepting that you will miss parts of some moves even when your underlying view is right. Sometimes the market never gives you an attractive entry to join a trend. Sometimes you simply miss the boat, and forcing a trade after the move has already happened only digs a deeper mental and financial hole. In those situations I remind myself of the following:

Principle #2 - Another trade is always around the corner. Do not chase trades out of FOMO; other opportunities will appear.

Markets spend roughly two thirds of the time in choppy, range-bound conditions and only about one third in trending phases. It is almost always easier to make money riding a strong trend than trying to extract small gains from a range. I would much rather take profits from a move that covers a lot of ground in a short time and then step aside, instead of sitting in a position for months to earn the same dollars. For example, my current long in WTI oil is about 12 dollars in the money after less than a week of holding it (with the usual caveat that I may still give some of it back). In calmer markets, a 12 dollar move in oil would take weeks, and capturing it would be a home run.

Principle #3 - Look for environments with volatility and clear trends that allow you to make meaningful pnl in a short period.

It is also important to distinguish between a trader and an investor. Investors work with longer horizons because their thesis takes time to play out. Their entries and exits will never be perfect, and they have to sit through both trending and range-bound markets. You need to be very clear which role you are playing. The worst outcome is letting a swing trade turn into a long term “investment” simply because you refused to cut losses.

I followed these principles when developing my S&P futures trading strategy. The goal of the strategy is to risk less than 1% in price to make 2-5% on each trade. I use defined stops and trail them aggressively to protect profits. Because of these parameters, the strategy naturally has a shorter time horizon of 1-3 days. In environments like this, when I am more active on the short side, I have to be quicker to cover because squeezes can be brutal. If that means I miss a large move up or down on a big headline, so be it. My objective is not to catch every large swing, but to run a process with positive expectancy per trade and a smooth pnl curve that avoids large drawdowns.

Since the war began, my journal shows I have taken about 200 points out of ES through active trading. If I had simply sold ES on day one of the war and held the short, I would be up about 300 points, but with a much more volatile pnl path. That 300 point figure also assumes perfect hindsight, while the 200 points come from real trades and realized gains.

This short-term tactical trading strategy is well-suited for S&P futures as the market is more prone to mean reversion than others. When I trade other markets, I usually trade with wider parameters that allow for longer holding periods. In gold, for example, I would risk 100-150 points to make 300-500. In today’s oil market I would risk $3-5 to make $10 or more.