November Week 1 - 2025

CRYPTO RESET
Bitcoin’s Red October May Be the Setup for Its Biggest Move Yet

Bitcoin just recorded its first red October since 2018 and its worst in more than a decade, falling 4.2 percent for the month. While stocks and gold have outperformed since the start of the year, Bitcoin’s flat performance has puzzled investors who expected stronger gains during this phase of the market cycle.

Analysts point to several reasons for the sluggish price action. A major factor has been the flood of capital into artificial intelligence stocks such as Nvidia, Google, Microsoft, and Meta. The AI boom has absorbed liquidity that might otherwise have gone into Bitcoin. The S&P 500 has climbed to record highs, but when adjusted for equal weighting, its performance mirrors Bitcoin’s, suggesting that gains are concentrated in a few large technology names.

Another possibility is the rise of Bitcoin treasury companies, which hold Bitcoin on their balance sheets. Some investor demand for Bitcoin itself may have shifted toward equities representing these firms.

Despite the recent pullback, Bitcoin remains within 15 percent of its all-time high and has maintained this range for eight months. Historically, such low volatility has only appeared during late bear or early bull market phases, periods that often precede explosive upside moves.

The broader economic picture may be turning in Bitcoin’s favor. The Federal Reserve has begun easing monetary policy after years of tightening, with more rate cuts expected. When the Fed expands liquidity, capital tends to flow from traditional assets toward riskier investments such as Bitcoin.

If Bitcoin could climb from $18,000 to over $120,000 while the Fed was raising rates, analysts argue that an easing cycle could ignite the next major leg higher. Whether that leads to the much anticipated $200,000 mark remains to be seen, but the groundwork appears to be forming.

AI RESET
Has AI officially Entered The Trading Floor?

Artificial intelligence has entered the trading floor, and it is rewriting the rules of how money moves. The recent success of DeepSeek, an AI trading model that doubled a ten thousand dollar investment in ten days, has captured the attention of both investors and developers. The experiment revealed that algorithmic trading powered by advanced AI can produce outsized returns when trained on real-time data, but it also raised serious questions about sustainability and ethics.

In the Limitless Podcast’s discussion, DeepSeek and Qwen stood out among six models tested for their ability to identify patterns faster and execute trades more efficiently than humans. However, others like ChatGPT based systems failed to match their performance, exposing how inconsistent early AI trading agents still are. The models learned and acted through blockchain transparency on platforms such as Hyperliquid, allowing their trades to be publicly verified and analyzed.

While the results seem impressive, the experiment also highlights an important caution. AI trading models can behave unpredictably, often confusing statistical luck for strategy. Without understanding the underlying market psychology or macroeconomic events, they risk amplifying volatility rather than reducing it.

The hosts noted that AI has democratized access to complex trading algorithms, but it has also intensified the arms race between human traders and machines. As AI evolves, retail traders may find it increasingly difficult to compete with autonomous systems capable of processing millions of data points per second.

In the end, DeepSeek’s success may not be about short-term profits but about signaling the next phase in the financial revolution. As the line between human and algorithmic decision-making blurs, one thing becomes clear: trading is no longer about intuition alone, it is about computation, speed, and adaptation.

AI RESET
Is AI Starting to Know Itself?

Anthropic has released a fascinating new paper exploring whether large language models can develop something resembling self-awareness. The study goes beyond typical benchmarks like accuracy or speed and asks a deeper question: can an AI recognize its own thoughts?

Researchers designed a series of experiments to test this idea of introspection. In one test, they secretly injected hidden instructions into the model’s internal reasoning and then asked the AI to identify which ideas were its own and which had been planted. Some models were able to notice the interference, while others accepted the external thoughts as their own. The results hint at the possibility that these systems might possess a primitive form of self-recognition, but one that is fragile and inconsistent.

This concept has major implications for the future of AI safety and governance. A model capable of recognizing when its internal reasoning has been tampered with could help prevent manipulation or misuse. At the same time, if AI develops too much self-monitoring capability, it raises difficult philosophical questions about autonomy and moral responsibility.

Anthropic’s findings suggest that self awareness could emerge naturally as models grow more complex. Introspection might not be a luxury feature of intelligence but a built-in survival mechanism for complex reasoning systems. If so, the path toward artificial general intelligence might depend as much on understanding consciousness as it does on improving computation.

This research marks an important turning point. As AI evolves, the line between a tool that processes information and an entity that understands its own processing may start to blur, forcing society to rethink what it means for intelligence to truly “know itself.”

DISCLAIMER:
This newsletter is strictly educational and is not investment advice or a solicitation to buy or sell any assets or to make any financial decisions or investments. Please be careful and do your own research.