The amount of analysis available to you right now is greater than at any time in human history.
And yet most people have less clarity about what is actually happening than they did five years ago.
What changed is the scale. When analyzes were expensive to produce, there was a natural filter. The people who produced it had to know something because the cost of getting it wrong was reputational and financial. Now that price is basically zero. Anyone can generate a macro recording that sounds like it came off a Goldman desk in five minutes. The noise grows exponentially, while the real signal remains fairly constant.
The insidious thing is that the noise doesn’t feel like noise anymore. It looks like a signal. Bad analysis used to be blatantly bad. Now it’s polished, structured, uses the right terminology, cites the right data. The tools most people use to produce it are optimized to sound right. Whether the output is actually correct is another question entirely.
Telling the two apart is the whole game now. The same systems that flood markets with noise can be used to cut through it. That’s what I’ve spent the last two years proving – publicly, on X, with every call time-stamped and nothing deleted, across geopolitics, energy, macro, crypto and wider markets simultaneously.
The account grew from nothing to over 140,000 followers organically, with no paid promotion and no name attached. Signal Core on Substack, home of the full forecasting operation, became the #3 best selling crypto publication on the platform within nine months. In a market drowning in noise, the signal alone was enough.
The moment
The signal-vs-noise problem has arrived at the worst possible time.
The next twelve months will reshape more of the financial, technological and geopolitical order than the past decade combined. Digital assets are integrating with the traditional financial system at a pace that would have seemed impossible eighteen months ago. Regulatory frameworks that have stalled for years are being rewritten in real time. AI is transforming how capital is allocated. Geopolitical orders are changing. Monetary policy is at a turning point. The labor market is being restructured before us.
These are fundamental shifts that occur simultaneously and reinforce each other. And this is precisely the moment when the ability to see clearly has collapsed. There has never been more at stake and never less clarity about what is actually going on.
The convergence problem
It’s actually worse than a noise problem.
AI is all converging towards the same wrong answers simultaneously. When a thousand people use these tools to analyze the same event, they don’t get a thousand different perspectives. They get smaller variations of the same standard output. The tools don’t just fail to produce signals – they produce fake deals.
Before AI, if five analysts said the same thing, it meant something. If five hundred accounts say the same thing, it might just mean they all used the same tool.
How it looks in practice
In January of this year, the prevailing view was that a direct confrontation between the US and Iran was unlikely. The diplomatic channels were still open. The market did not price meaningful conflict risk. The oil acted as if nothing happened.
The structural picture told a different story.
More than a month before the strikes began, indicators were already pointing to a confrontation more likely than not. We flagged this publicly on X, January 13, while the public still dismissed the risk. When the strikes hit and oil nearly doubled, the movement captured most of the market. The signal was there. The audience just wasn’t looking at it.
The inputs we saw were not exotic. Public statements, internal economic pressures in Iran and the absence of certain de-escalation patterns. Anyone with access to the open internet could see the same things. The advantage was in synthesis—reading these inputs as a single converging system rather than as separate news streams. That synthesis is the hard part. The inputs are only inputs. The bottleneck has never been technology. It has been how the technology is used.
This is the pattern. The information was available. The tools to treat it were available. What was missing was the ability to read the signal before the crowd formed around the wrong interpretation.
The scarce resource
Most people use AI to generate. Very few use it to watch.
Signal is when you can look at a situation that has the whole market confused and see the structure underneath. It’s when you can hold a position that every feed tells you to give up, and hold it anyway because you can see something they can’t.
The challenge for most people is not generating signals themselves. It is recognizing who actually has it. Most analysis is stripped down to meaninglessness – strategies to avoid accountability dressed up as analysis.
The old filter to get past this was credentials. It no longer predicts who sees clearly. Many of the biggest calls in recent years have been missed by traditional institutions and caught by people working outside them. What matters now is whether someone actually sees what’s going on—recognizes patterns the crowd misses, names what’s real before it’s obvious, and gets it right often enough that it lasts over time. When you can see clearly, you start operating on a different timeline than the rest of the market.
What comes next
We are entering an era where signal is the most valuable and least understood asset in the market. The investors, builders and allocators who figure this out first will have a structural advantage that will compound over the years. Those who continue to consume the flood without questioning it will continue to agree with the crowd. And the crowd will keep getting it wrong in the moments that matter most.
It’s getting harder to find rooms where the right signal still shows up. Most of the venues that claim to have gathered market intelligence are just amplifying whatever the models are already spitting out.
Consensus 2026 in Miami is one of the few that still functions as a filter rather than an amplifier. The people who show up have skin in the game. Their differences are real. Their deals were not made from the same five models that everyone else uses. This kind of space is becoming harder to find elsewhere. That’s why I’ll be there – hosting a small invite-only session on what signal mining at scale actually looks like.
The edge will not belong to whoever has the most information, the fastest tools, or the highest platform.
It will belong to the one who can see clearly when everyone else is drowning in noise.
It is the scarcest resource in the markets right now.
And it’s only getting rarer.
