Signals: The Invisible Language of Search, Life, and Intelligence

• Signals are everywhere and they are noisy.
• Memory is a Signal Both for Humans and Machines
• Models vs. Systems: Why AI Will Become Personal Through Shared Signals
• GenAI is not Intelligence, It's a Knowledge Engine for Inspiration
• Building Signal-first Systems
• Signals as a Human-AI Choreography

I'm fascinated by signals. Not the dramatic or cinematic types, but the subtle, often messy hints and traces that, when recognized and compiled, create meaning. Signals were what made Google Search effective in the first place: a chaotic world of links and clicks transformed into relevance by learning to recognize patterns in behavior. They also drive recommendation algorithms, where the subtle cues of what you've watched, clicked on, lingered over, or ignored are turned into personalized suggestions.

However, signals aren't just important for search engines or recommendation systems; they are the essential building blocks of human relationships, businesses, and — increasingly — artificial intelligence.

Here's why signals matter, why they are fundamentally unpredictable, and why the future of AI is less about monolithic models and more about shared, experience-driven systems that inspire action.

Signals are everywhere and they are noisy.

Signals are the traces we leave behind—such as a like, lingering gaze, purchase, repeat visit, or a subtle cue in body language. These signals are often small, incomplete, and sometimes contradictory. However, when combined, they begin to form narratives.

Recommendation algorithms exemplify this concept well. A single movie you click on doesn't accurately reflect your preferences. Still, when combined with thousands of micro-signals—like your viewing times, rewinds, skips, and even the titles you considered but didn't choose—they create a probabilistic picture of what you might want to watch next.

This inherent noise is significant. For instance, a girl smiling at you isn't a promise; a spike in web traffic doesn't necessarily indicate genuine interest, and a recommended video isn't guaranteed to engage you. The real value emerges when multiple weak signals converge. The true magic lies not in any individual data point but in the context, correlation, and patterns that we derive from them.

Memory is a Signal Both for Humans and Machines

Memory is often viewed as a static store where you either remember a fact or you don't. However, memory is more accurately described as a signal: a record of interactions, choices, and outcomes that shapes our expectations.

For humans, memory is personal, emotionally charged, and selective. For machines, memory is composed of signals such as past searches, previous clicks, playlists, and conversation history. Recommendation systems constantly utilize this memory — your prior ratings or interactions influence the next suggestion, just as your earlier conversations affect how you interpret someone's new comments.

The key is to recognize memory as a type of signal rather than something separate. Memory guides interpretation, provides context, and influences actions. In both humans and machines, it is the accumulation of weak signals that culminates in strong patterns.

Models vs. Systems: Why AI Will Become Personal Through Shared Signals

When people discuss artificial intelligence today, the emphasis often falls on "the model." Large language models analyze patterns from extensive datasets and can generate plausible responses. However, models alone are just one part of a larger picture.

The true strength of AI lies in systems that integrate various signals: your personal history, the interactions of people similar to you, real-time behaviors, and the curated insights from experts. This is precisely how modern recommendation engines function—they combine collaborative filtering (signals from individuals like you) with your personal data to provide fresh recommendations.

The future of AI will be personalized not just because the model was adjusted specifically for you, but because the system continuously interprets and integrates your signals with those of others. It's about harnessing the combined signals of many lives—filtered, weighted, and made actionable.

GenAI is not Intelligence, It's a Knowledge Engine for Inspiration

Instead of viewing Generative AI (GenAI) as a wise oracle that "knows" everything, it's more beneficial to think of it as a system that nudges, recombines, and inspires. It's better described as a source of inspiration rather than intelligence. Signal-driven systems do not simply answer questions; they foster creativity.

Consider how a recommendation algorithm sometimes presents you with something you didn't even know you wanted—a book, a song, or a documentary. That spark of discovery doesn't arise from strict logic; it comes from combining various signals and experiences. When Large Language Models (LLMs) are integrated into such systems, they serve a similar purpose: uncovering possibilities based on shared experiences that we may not have previously considered.

This can be quite powerful. A knowledge engine built from signals acts as a catalyst for action. Entrepreneurs, creators, and everyday individuals are not solely looking for perfect answers; they seek the nudge that transforms curiosity into experimentation, insight into products, and minor signals into informed decisions.

Building Signal-first Systems

If we accept that signals + systems matter more than models alone, a few priorities emerge:

1. Prioritize provenance and context: Signals are easy to collect and easy to misinterpret. Always attach context — who, when, why — to make them useful.
2. Blend personal and shared signals: Personal memory + community footprints = stronger recommendations. The sweet spot is respecting personal boundaries while leveraging communal knowledge.
3. Surface uncertainty: Recommendation engines are already experimenting in this space, offering "because you watched…" explanations. AI systems should go further, showing why specific signals influenced an output.
4. Design for serendipity: Signals should not only confirm preferences — they should introduce productive surprises. The best recommendations feel like discoveries.
5. Keep feedback in the loop: Every skipped suggestion is a signal. Every ignored output matters. The more actively feedback is integrated, the stronger the system becomes.

Signals as a Human-AI Choreography

Signals are the rhythm of both life and technology. They can be messy, probabilistic, and instrumental. Search engines, recommendation systems, and now AI all thrive on interpreting these signals.

The AI we should develop is not just a mythical oracle but a choreography of signals: models that interpret data, systems that remember and share information, and interfaces that inspire action.

If you appreciate signals as much as I do, focus less on training a perfect model and more on designing a system that allows signals to connect with people—safely, insightfully, and provocatively. When you shift your perspective in this way, AI becomes not only more intelligent but also more human in its value: an amplifier for the small, uncertain sparks that inspire individuals to act, create, and change the world.