Signals are the new black

I had the pleasure of attending my first LSSO – Raindance Conference a few weeks ago where at least a half dozen times (I honestly lost count) presenters talked about signals.

Last week, I hosted an episode of Harbor’s Legal Soundings Podcast and signals came up.

There were the headlines about Kirkland’s AI investment being a signal of something, and talk of Microsoft signals.

I’m starting to think if I had a nickel for every recent mention of “signals” I’d be as rich as the people collecting nickels related to agentic AI.

Signals are not new.  Military intelligence has had the Signals Corp since the invention of radios.  Competitive intelligence professionals have always been in the business of finding signals to avoid mistakes and predict opportunity.

For decades, intelligent analysts have sifted through vast amounts of information to separate meaningful developments from background noise. The job has never been simply to gather information; it has been to transform information into intelligence. For example, at one of the firms I worked at, a Practice Group Leader called me one day and asked me to “look for the signals to determine which National oil company would invest in the Canadian oil sands next.”  Those weren’t the exact words he used, but you get the point.

What’s different in the AI era is that the economics of information have fundamentally changed.

Information itself is no longer scarce.

Every law firm and their CI/ BD practitioners now have access to AI tools that can instantly summarize earnings calls, SEC filings, regulatory developments, news articles, LinkedIn activity, job postings, patents, podcasts, analyst reports, and social media conversations. The barriers to access have largely disappeared.

The competitive advantage is no longer who has the information. The competitive advantage is who can identify and act on meaningful signals before everyone else.

This may be the single most important shift occurring in business development and competitive intelligence today.

AI Hasn’t Eliminated Analysis. It Has Raised the Bar.

For years, many organizations equated competitive intelligence with information gathering: collect the data, build the dossier, distribute the report, repeat.

AI now performs much of that work in seconds. Summarization is becoming commoditized. Research is becoming commoditized. Even synthesis is becoming increasingly accessible.

As AI lowers the cost of analysis, human judgment becomes more valuable, not less.

The question is no longer ‘What do we know?’ The questions become ‘What matters, and what is likely to happen next?’ ‘Who will this impact and how can we help?’

That is a signal-detection and analysis paradigm shift.

Business Development Is Becoming a Timing Function

Business development has always been about relationships, and it still is. But passive relationships, the kind where contact is only made when a suit is filed, a transaction is imminent or there is a sporting event happening, will no longer suffice.  Success today will   depend on engaging clients at precisely the right moment.

Companies continuously emit signals: new executive hires, geographic expansion, product launches, website changes, patent filings, strategic partnerships, job postings, and regulatory disclosures, to name a few.

Individually, these data points are unremarkable. Collectively, they tell a story.

Historically, legal business development has been largely relationship-driven and reactive: build relationships, stay visible, wait for a legal event, and receive the call.

The AI era invites a different question: What signals indicate a client is about to face a legal challenge before they realize they need outside counsel?  We used to set up early warning signals at my previous firm but we were still later than we could be in today’s world. We had to wait for a class action to be filed to find it. Today, AI tools can monitor consumer complaints, regulatory investigations, product recalls, data breaches, and court filings in near real time.

The firms that recognize that story first gain an advantage because timing matters.

Law Firms Have a Unique Opportunity

Lawyers are already trained to think in this scenario planning kind of way.

They instinctively ask: What changed? What are the second-order consequences? What risks are emerging? What is likely to happen next? What similar things have happened in the past?

These are signal-detection skills. The opportunity is to apply that thinking earlier in the client lifecycle.

What Legal Signals Might Look Like

Strong signals are easy to spot. Everyone sees the merger announcement, major funding round, or significant litigation filing.

Weak signals are more interesting: a handful of AI governance hires, a subtle website update, a revised privacy policy, or participation in a new industry consortium.

Weak signals may seem insignificant but they reveal a strategic shift months before it becomes obvious. The organizations that consistently connect these dots early will outperform those that wait for certainty, because by the time certainty arrives, everyone else can see it too.

This Is Also a Talent Question

Law firms have traditionally rewarded relationship builders, rainmakers, and network strength.

Those skills remain indispensable, but firms may need to elevate curiosity, pattern recognition, industry fluency, strategic questioning, and the ability to connect weak signals into actionable hypotheses.  These may not be skills that lawyers readily possess; some firms are already creating hybrid teams that combine business development professionals, competitive intelligence specialists, knowledge management professionals, and practicing lawyers to do exactly this. Others will find that to properly detect and action the signals they need to upskill their teams, hire or outsource to stay competitive.

Conclusion

Information is no longer a scarce resource.

In the AI era, every firm can gather more, summarize faster, and monitor more broadly. The advantage belongs to the firms that can identify which signals matter, understand what they mean, and act before the need becomes obvious.

For law firms, that changes the role of competitive intelligence and business development. The goal is not simply to report what happened. It is to help lawyers and clients see what may happen next.

AI can surface the signs. Human judgment turns them into signals.

And given how often signals seem to be appearing lately — in conferences, client conversations, headlines, podcasts, and product pitches — I wonder if the The Five Man Electrical Band was song writing in 2026 instead of 1971, they would have been singing about signals instead of signs… But there is an important distinction. Signs tell you where things are. Signals hint at where things are going.

“Sign, sign, everywhere a sign.”

A few weeks ago I ran the numbers on the token cost panic. I took the scariest figure in legal AI, the finding that agentic workflows burn a thousand times more tokens than a chat query, and followed it all the way down to a dollar amount on a real deal. The panic did not survive the arithmetic. The piece is here if you want the full walk-through.

This is not that piece. The panic has moved on since I wrote it, and the new versions are smarter than the old one. The thousand-times number has quietly retired, because a thousand times almost nothing is still almost nothing. In its place are three fresher anxieties, and they deserve a real answer. The first says the model makers have a monopoly now, the price of a token is climbing, and it will climb forever, so you had better lock in a flat rate or build your own models before it does. The second says forget the price of a token, watch the meter: every time the AI reads your contract it ticks, and a long agentic session reads your contract over and over and over. The third does not bother with an argument at all. It just points at a number. One company spent five hundred million dollars on AI in a single month, and the number is so large it does the panicking for you.

All three are wrong. They are wrong in more interesting ways than the original, which is the only reason I am writing this down instead of linking to the first piece again. But underneath the new costumes it is the same body. Every version of this panic makes the same mistake and reaches the same conclusion. So let us stop swatting the individual numbers and name the thing that keeps generating them.

The Mistake Underneath All of It

Here is the error, stated once, because everything below is a variation on it.

A token is the unit a model uses to bill you. It is not the unit your work is measured in, it is not the unit your client pays for, and it is not the unit anything you care about is denominated in. It is a meter reading. The entire genre of token panic consists of staring at the meter reading as though it were the fare, the destination, and the quality of the ride all at once.

It is not any of those things. It is the meter. And a meter, by itself, tells you nothing about whether you are getting a good deal. A taxi meter reading of forty dollars is a bargain to the airport and a robbery around the block. The number on the meter is the least informative number in the entire transaction, because it means nothing until you put it next to what the ride was worth. Every piece in this genre forgets that, and forgets it in a slightly different way. Let me take them in turn.

“Prices Only Go Up”

Start with the monopoly story, because it has a real fact inside it. Yes, the newest frontier model costs more per token than last year’s newest model. That part is true. What the story does with it is the problem.

It draws a line through two dots and calls it a trend. Frontier prices up, therefore prices up forever, therefore lock in a flat rate before the meter eats you. But you are watching the wrong number. The price of a frontier token is not your cost. Your cost is what it takes to finish a task, and the cost of finishing a given task has been in freefall for two straight years. The same capability that ran on the most expensive model available in 2022 runs today on something on the order of two hundred and eighty times cheaper. Last year’s frontier is this year’s mid-tier is next year’s free default. The token at the very tip of the frontier gets a little pricier each release; everything behind the tip collapses in price behind it. Gartner expects another ninety percent drop in inference cost by 2030.

Watching the frontier price and concluding that AI is getting more expensive is reading the thermometer and announcing a fever, while ignoring that you are holding the thermometer over a candle. The evidence that the baseline is getting cheaper often sits right there in the same articles raising the alarm, quoted from the experts and then left unaddressed. You do not build a cost strategy on the one number in the system that is engineered to always be the highest.

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