Esijooes

The sun shall rise again.

[Atlas Press @realAtlasPress]

Alexander Hamilton, where his genius comes from

[Esijooes @esijooes]

"If you bind yourself to process—to the decisions you can point to, the logic you can trace—then results sink to the background like emotions, fall back to the level of the external environment. Then you are internally defined and internally actualized".

[Simon Russo @SimonRusso__]

https://t.co/ngOP7mLHAS

[Jeff Sun, CFTe @jfsrev]

When the drive to execute your plan flawlessly outweighs the desire for immediate gains, you undergo a subtle yet profound psychological shift from an amateur to a professional trader that treats trading like a long-term business.

The path to reaching this mindset involves experiencing losses so often that you are sitting through drawdown most of the time. But the hard truth is this is a necessary phase, and it represents a genuine recognition of the fundamental truths inherent in trading.

"the best loser is the long-term winner" - Phantom Of the Pit

[Richard Rohr @RichardRohrOFM]

I appreciate Connie Zweig and Steve Wolf's approach to shadow work:

"Relate to the shadow as a mystery, rather than as a problem to be solved or an illness to be cured. When the Other arrives, honor that part of yourself as a guest."

#DailyMeditations https://t.co/kdJ8ivRMDO

[Gavin Baker @GavinSBaker]

OpenAI had a significant lead from summer 2022 through spring 2024 when Google and Anthropic caught up to GPT-4. 7ish quarters of dominance as a result of being the first to aggressively bet on the traditional scaling “law” for pre-training.

Being first to reasoning with o1 only led to a few months of advantage.

Deepseek, Google and xAI are at rough parity with OpenAI today. xAI arguably in the lead. Google and xAI will likely decisively surpass o3 soon as their base models are better. So urgent need for GPT-5 as the basis for a putative “o5”reasoning model.

Sam noted that OpenAI would have a narrower lead going forward and Satya essentially stated that a unique period where they had a tremendous lead in model capability was ending.

IMO, this is why Satya is opting out of funding $160b of pretraining for OpenAI per @theinformation. Instead he will make money by providing inference to OpenAI.

Google and Xai both have unique, valuable sources of data that will increasingly differentiate them from Deepseek, OpenAI and Anthropic. As does Meta if they catch up from a model capability perspective.

I have paraphrased @ericvishria many times and noted that frontier models without access to unique, valuable data are the fastest depreciating assets in history. Distillation only amplifies this.

It seems like Satya shares this belief; hence opting out of the $160b of pre-training, the rumored datacenter cancellations and his statement on a recent podcast that there is a datacenter overbuild coming and better to lease than buy. Might be a sound decision for Microsoft from an economic perspective and at some point Microsoft might even use an open-source model to power CoPilot.

There may not be any ROI on future frontier models that do not have access to unique, valuable data like YouTube, X, TeslaVision, Instagram and Facebook. Zuckerberg’s strategy also seems much more sensible from this angle. Unique data might end up being the only basis for differentiation and ROI on pre-training multi-trillion or quadrillion parameter models.

If this is correct, only 2-3 companies will be pre-training frontier models and we will only need a few giant datacenters for the coherent clusters that are needed for pre-training. The rest of AI compute would be smaller datacenters that are geospatially optimized for low latency and/or cost-effective inference. Cost effective inference = cheaper, lower quality power (less premium for Nuclear), less of an imperative for liquid cooling in ST, etc. A very different world from one where 6-10 companies are pre-training frontier models.

Note that reasoning models are extremely compute intensive. Test-time compute means that compute is literally intelligence. So in this scenario there might be even more compute required than in the “pre-training” centric compute scenario that was the base case for the market throughout 2023-2024. But it would be a very different kind of compute as noted above. Instead of a 50/50 split between pre-training and inference it would be 5/95.  Lots of Hondas, very few Ferraris. Infrastructure excellence would be paramount.

And all of this without even considering the implications of on-device inference and/or full quantization - the Deepseek R1 paper was not the most important paper to be published by a Chinese lab in the last year. IYKYK.

The economic returns to superintelligence are definitionally unknowable. I hope they are high, but a 140 IQ model running on device with access to unique data about the world might be enough for most use cases. ASI isn’t needed to book travel, etc.

I’ve done my best to be dispassionate, but I do have my own biases, both personal and economic, when it comes to xAI and OpenAI. If OpenAI is still one of the leaders in 5 years, then likely a function of first-mover advantage, ChatGPT becoming a verb and scale being even more of an advantage for reasoning models in that users generate and verify(ish) reasoning traces.

As ever, time will tell.

[Andrew D. Huberman, Ph.D. @hubermanlab]

Chess prodigy Josh Waitzkin developed a process to achieve peak performance in any craft or career. He’s applied it to the world of investing, professional sports, science and more. The MIQ Process. It is not a quick fix, but rather a rewiring of your default settings.

[Jeff Sun, CFTe @jfsrev]

My Pre-Open Routine (30 Minutes Before Market Opens) Using Free Tools

In addition to the usual position management (stop adjustment), post-market screening, reviewing the daily biggest movers list (their catalysts) and, ETFs price action development, there are specific activities I adhere to 30 minutes before the market opens. These include identifying substantial premarket movers as potential ideas and quickly scanning economic or stock news that could act as catalysts.

I hope you find some of these tools useful.

[Jeff Sun, CFTe @jfsrev]

As the clock struck midnight in the Asia Pacific time zone, I celebrated not only my 35th birthday but also my 13th year of learning within the FinTwit community.

Within the realm of solitary trading, there are often instances when the sole source of joy, particularly during a lacklustre market period, is through acts of generosity. With that in mind, I would like to take this moment to express my deep gratitude for the years of selfless education I have received as a silent reader, along with the intellectually stimulating discussions held within this community.

Furthermore, I wish to take this opportunity to delve into the purpose and significance of maintaining a daily market diary, as it plays a vital role in your development and growth as a trader. It serves as a navigational tool, keeping you closely on track with market movements, situational awareness and helping to curb over-trading beyond your prepared watchlist.

Finally, I would like to extend my gratitude to the following Twitter accounts that have greatly contributed to the development of my market diary over the years.

1. @finitrades - Selfless sharing of Notes & Watchlist Template, with trading style heavily influenced by @Qullamaggie. His google doc also has a whole list of great hyperlinks to @Quallamaggie's material

2. Ben Bennett - Mid-Week market technical analysis on youtube and twitter, was watching it since 2015.

3. @cfromhertz - Daily Market Recap video is the first thing I watch in my morning since i found it on 2017! Demonstrating genuine passion for the market by consistently producing a 20-minute video every day since 2015!

4. Mike Prechter - An ideal mentor who effectively communicates about risk back in 2010s in twitter. Rumored to be the Phantom from 'Phantom of the Pits' . Greatly missed!

5. @alphacharts365 - State of The Market (SOTM) video is my Sunday evening's ritual. No BS, simply clean charts on both daily and weekly of market, key sectors, bonds, vix, credit spread, and I love the new HG1!/GD1! relative strength between copper and gold to access economic growth.

6. @RichardMoglen - after SOTM, I will be watching Richard's Stock Market Outlook. Richard deserves recognition not only for his skill as an interviewer of great traders but also for his dedicated efforts in creating informative watchlist and stock market outlook videos.

By increasing the playback speed to 1.25 for both @alphacharts365 and @RichardMoglen videos, you can effectively cover the content within 20 minutes. This allows you to not only obtain a second opinion but also a third opinion, enabling you to keep your own market analysis and biases in check.

7. @LeifSoreide - An aspect of my diary is the breakdown of the sub-market such as $IJS, $IJT, $IJJ, $IJK, $IVE, and $IVW. This was an influenced from Leif.

8. @PrimeTrading_ - Alex consistently shares his daily leading sector scans and a meticulously curated watchlist of stocks. His watchlist comprises only those stocks that meet stringent criteria based on fundamental metrics, relative strength, technical analysis, and his proprietary scoring system known as PRIME Score. Additionally, his proficiency in spreadsheet formulas has inspired me to maintain my own spreadsheet in a sleek and organized manner.

So here we go!

1/n

[OpenAI @OpenAI]

Announcing The Stargate Project

The Stargate Project is a new company which intends to invest $500 billion over the next four years building new AI infrastructure for OpenAI in the United States. We will begin deploying $100 billion immediately. This infrastructure will secure American leadership in AI, create hundreds of thousands of American jobs, and generate massive economic benefit for the entire world. This project will not only support the re-industrialization of the United States but also provide a strategic capability to protect the national security of America and its allies.

The initial equity funders in Stargate are SoftBank, OpenAI, Oracle, and MGX. SoftBank and OpenAI are the lead partners for Stargate, with SoftBank having financial responsibility and OpenAI having operational responsibility. Masayoshi Son will be the chairman.

Arm, Microsoft, NVIDIA, Oracle, and OpenAI are the key initial technology partners. The buildout is currently underway, starting in Texas, and we are evaluating potential sites across the country for more campuses as we finalize definitive agreements.

As part of Stargate, Oracle, NVIDIA, and OpenAI will closely collaborate to build and operate this computing system. This builds on a deep collaboration between OpenAI and NVIDIA going back to 2016 and a newer partnership between OpenAI and Oracle.

This also builds on the existing OpenAI partnership with Microsoft. OpenAI will continue to increase its consumption of Azure as OpenAI continues its work with Microsoft with this additional compute to train leading models and deliver great products and services.

All of us look forward to continuing to build and develop AI—and in particular AGI—for the benefit of all of humanity. We believe that this new step is critical on the path, and will enable creative people to figure out how to use AI to elevate humanity.

[Dylan O'Sullivan @DylanoA4]

Nick Cave on cynicism vs hope... hell yes

[Esijooes @esijooes]

@Fathers_Writes Explain the science of fasting

[Benzinga @Benzinga]

This Week's Earnings Calendar:

Get updates at: https://t.co/1S9ZI3bb3v

[Jeff Sun, CFTe @jfsrev]

The Benefits of Maintaining Fixed % Risk Relative to Equity for Long-Term Trading Success

Discipline and patience are crucial in trading to facilitate the compounding of returns, as supported by the law of large numbers. Compounding can be achieved through a straightforward risk management principle: Maintaining a fixed percentage risk relative to equity.

In the two-line chart below, I simulate the outcomes of 1,000 trades, showing consecutive 1R wins and -1R losses based on the simple principle of maintaining a fixed 0.3% risk relative to realized equity. This approach can propel your equity in a parabolic trajectory during a strong winning streak over a large trade sequence, while a losing streak results in a gradual decline as the dollar risk per trade adjusts automatically in line with the %-to-equity principle. Additionally, you get rewarded to increase/reduce risk in an automatic based on your trading performance without discretion. This risk principle carries a minimal risk of ruin at just 0.01%, making it highly valuable for testing strategies without the need to top up your trading account.

Account Start: $100,000
Risk to Equity: 0.3%
Dollar Risk (Start): $300

After 1,000 Trades
Risk to Equity: 0.3%
Dollar Risk (End For Win Graph): $1,337 (+$1037)
Account End: $447,164 (Gain +$347,164)

Dollar Risk (End For Lose Graph): $67 (-$233)
Account End: $22,263 (Loss: -$77,737)

I hope this post serves as a reminder of two key trading principles that are often overlooked in the context of long-term success: 'Treat trading as a business, not just a series of isolated trades,' and 'Focus on the process rather than the outcome of individual trades.'

Do retweet of you find this post helpful.

[Esijooes @esijooes]

@Benzinga https://t.co/qz6Hb1cEZi

[Esijooes @esijooes]

AST SpaceMobile shares are trading higher after the company announced a partnership with Vodafone for its global broadband connectivity through 203.4 $ASTS https://t.co/F2clMD1kO1 @benzinga
https://t.co/qz6Hb1cEZi