Palantir crashed 25% this week. Nvidia 9%. While the AI bubble may be popping, 75 years of data suggest it’s not time to panic-sell just yet.
Each week, a new company claims they are cracking AGI. This week it’s Google’s Genie 3 turn. Meanwhile, Google is also quietly leapfrogging other tech companies on the AI race with a simple feature “Google Cue”.
As LLM performance plateaus, the real battle is moving up the stack to the software layer. The real question is how do we leverage LLMs to revolutionize the engrained daily habits of billions of users.
This week:
Why are AI stocks down this week (it's not the AI Bubble)
Why the future of AI is not AGI. It is in your hands right now
How typing walls of text to prompt a chatbot won’t cut it. In fact, we’re coining a new term to describe what’s happening
Let’s start with a 30,000 feet view on the market…
📊Follow the Money Dashboard
Expected Value: Weekly Dashboard 8/24
The week started with Bubble fears. Meta freezes AI hiring and reorganized its AI department, Sam Altman piling on admitting we’re in a bubble, and MIT saying that 95% of AI pilots are failing. Most Tech/AI Stocks and other high beta assets like Bitcoin fell 10-25% from last week’s highs.
Then J-Pow took the mic from Jackson Hole.
The head of the Fed Jerome Powell was more dovish than expected. While not promising rate cuts, his remarks sent a clear signal to the market that the Fed is preparing to adjust in response to a weakening economy. Futures market are estimating the probability of a rate cut at 75%.
Rate Cut Probabilities For September 17, 2025
The Takeaway:
Independently of the headlines, this price action closely mirrors the seasonality pattern seen in post-election years (more on that below).
While the September Rate Cut is already priced in, Federal Reserve telegraphing further rate cuts will help Tech stocks rebound strongly in late Fall.
Expect volatility in September as usual especially around the release dates of employment figures (9/5), inflation (9/11) and FOMC meeting (9/17).
🚀 Market Move: Seasonality
Price Chart Showing SP500 Seasonality in Grey, Current Trajectory in Blue vs. AIQ in Green
Brief: Seasonality is one of those things that are difficult to explain, but works. Using 75 years of S&P 500 data, I plotted a chart of what the average index price action looks like in the post-election calendar year. The pattern that emerges, and that keep happening in those years is as follow: Early high in the year, followed by weakness in Q1. Then a rebound into the summer high, usually July, before going into a correction early fall, and ending the year with a rally.
The Chart:
Green: AI Stock Index (AIQ, green)
Blue: S&P 500 (2025 actual, blue)
Grey: S&P 500 Post-election years' average (1949-2021, grey)
So far this year, SP500 Movement is a textbook case of post-election year seasonality.
I overlaid AIQ to see if there are any differences. Here is what the data says:
→ SP500 Spring rally: AIQ vs. SP500: 1.6x amplification
→ Maximum drawdown: AIQ vs. SP500 -10%: 2.6x amplification
→ YTD performance: AIQ +16.7% vs SPY +10.1%: 60% overperformance
AIQ also delivered a textbook post-election year pattern, just amplified.
The Undeniable Pattern:
So far
✅ New Year High in Feb
✅ Spring Low
✅ Summer High
Next?
❓ Fall Low After Correction
❓ Year-End Rally
Why it's important:
Post-election year seasonality is in effect with a near-identical price action
AI Stock Index Q is amplifying SP500 price swings (2x risk and reward)
Fall correction pattern suggests -20% decline ahead before rebound. If that's true, AI Stocks might see a steep correction before an explosive year-end rally
As always, do your research. This is not investment advice. Last time I checked, I don’t have a crystal ball.
If you don’t mind me continuing my future predictions, here is what AI would look like in a couple of years…
💡 One Big Idea: Context Acquisition is where the real AI Battle is
Context Acquisition: the Next AI Frontier
The obsession with AGI timelines is missing the point
Everyone's obsessing over who's going to achieve AGI first. In reality, the real AI battle isn't about building the smartest general intelligence. It's about context.
I want to coin a term for what's really happening in AI: Context Acquisition: gathering and understanding personal user data to create an AI that just 'gets it’.
Google just proved this with their Pixel 10 launch.
While everyone debates ChatGPT Codex vs Claude Code, Google quietly built Magic Cue: an AI that knows when you're calling an airline and automatically surfaces your flight details. That's not AGI. That's context.
Why Context Beats Intelligence
Current LLMs are already powerful enough to be transformative personal assistants. The limitation isn't their reasoning ability, it's their lack of context about YOU:
What AI companies are really fighting for:
Your email patterns (Gmail: 1.8 billion users)
Your location history (Google Maps: 2 billion monthly users)
Your photo memories (Google Photos: 1.5 billion users)
Your browsing behavior (Chrome: 3.45 billion users)
Your daily routines (iPhone: 1.4 billion active devices)
Your health data (Apple Watch: 142 million users, 89% retention)
3 Sources for Context Acquisition
Device Integration: Apple and Google embed AI directly into phones, watches, and home devices. Every interaction feeds the context engine. Your iPhone knows when you wake up, what apps you use, who you text. That's goldmine data for personal AI.
App Omnipresence: Companies like Perplexity are building browser extensions (Comet) that watch everything you do online. Every search, every article, every purchase decision gets fed to your AI assistant.
API Background Learning: AI systems that connect to your existing tools (Gmail, Slack, Notion, calendars) and learn your patterns without you actively prompting/training them.