What If AI Could Forecast Your Life Like It Forecasts a Star’s Orbit?

Over lunch today, I happened to watch an episode of the Chinese sci-fi animation Swallowed Star. A line at the end caught my attention: "A star dozens of light-years away will one day disrupt our entire solar system."

I looked it up. It wasn’t fiction. The star—Gliese 710, currently about 62 light-years from Earth—is expected to pass through the edge of our solar system in 1.3 million years, potentially disturbing the Oort Cloud and sending a wave of comets toward Earth. This prediction isn’t guesswork—it’s based on data from over seven million stars, modeled using physics and AI-powered simulations.

This made me wonder: If AI can project the orbit of a distant star millions of years into the future, could it also predict something far more chaotic—human behavior? AI already forecasts stock volatility and simulates economic policies. Could it also predict how individuals or organizations evolve? If planets move under gravitational pull, might we move under social gravity—the influence of networks, norms, and behavior patterns?

This article explores that idea. Starting with how AI models celestial movement, we’ll draw parallels to human networks—how influence spreads, how knowledge flows, and how tools like LinkedIn and platforms like LEAD (my company) help map and steer those dynamics. Most importantly, we’ll ask: How far should AI go in predicting people? Because unlike stars, humans come with emotion, intention, and unpredictability.

From Stellar Orbits to Human Trajectories

At first glance, astrophysics and human systems seem unrelated. One studies stars governed by gravity. The other, people shaped by culture, intention, and chance. Yet structurally, the parallels are striking.

In astronomy, AI uses known inputs—mass, velocity, and gravitational pull—to simulate star movements. That’s how we predict Gliese 710’s path. Similarly, if we treat a person as a “node” in a social universe, their behaviors and relationships form a kind of trajectory shaped by influence fields—colleagues, communities, and systems. Of course, humans aren’t celestial bodies. But we do show behavioral inertia. We’re pulled by peer norms, organizational constraints, and information flow. AI excels at spotting these invisible forces—trends in communication, micro-patterns in behavior, and shifts that signal bigger changes to come.

The difference isn’t whether prediction is possible—it’s how complex the rules are. Planetary systems are “many objects, few rules.” Human systems are “many objects, many evolving rules.” That makes prediction harder—but also more valuable, especially when the right signals can be extracted.

In both domains, two techniques are converging:

• Simulation: Astrophysics simulates gravitational interactions among millions of bodies. Human systems simulate how ideas, policies, or narratives spread and trigger chain reactions.

• Pattern Recognition: AI can detect the subtle wobble of an exoplanet—or the early signs of employee disengagement: fewer messages, missed meetings, reduced collaboration.

In this way, predictive modeling becomes a bridge—linking orbital mechanics with organizational dynamics, extending gravitational thinking to networks of influence.

Once you can model someone's behavioral patterns, thinking habits, and social connections, you can begin forecasting inflection points—career changes, decision shifts, even systemic risks. At a macro level, this approach could illuminate market dynamics or uncover feedback loops in investor behavior driven by tight-knit social groups.

What once sounded like sci-fi is rapidly becoming a real-world, interdisciplinary toolset.

The Gravity of Influence: Organizational Network Analysis (ONA)

Organizational Network Analysis (ONA)

If gravity guides the motion of planets, then influence shapes how organizations move.

Every company has two structures: the formal org chart, and the invisible network—who people trust, collaborate with, and turn to for decisions. Organizational Network Analysis (ONA) maps this hidden structure. Think of it as a telescope for workplace dynamics, where people are nodes and relationships are connections. ONA reveals an internal “influence field”: who serves as the gravitational core, who’s on the margins, and whether knowledge flows smoothly or stalls. Those with strong, central connections act like massive stars—pulling others in, shaping how information and trust travel through the company.

Some researchers have even adapted gravitational models to social networks: influence equals “mass,” and relationship proximity determines the strength of pull. These models often outperform KPIs in identifying hidden contributors—those who may not hold formal power but are critical to collaboration. The concept is intuitive: the more connected and centrally positioned a person is, the more likely they are to drive momentum. Meanwhile, isolated teams drift like cold stars—cut off from the information ecosystem.

When visualized, the result is a “social universe map” of the company. Leaders often find that influence doesn’t align with title. Studies show 3–5% of employees often account for 20–35% of high-value cross-functional work. These people aren’t always executives—they’re the connectors and trusted hubs. In one study, a mid-level employee named "Mitchell" wasn’t a leader by title, but sat at the intersection of multiple key teams. When he left, collaboration fractured—like a system losing its gravitational center.

ONA also reveals organizational silos—teams that appear side-by-side on paper but rarely interact. These "structural vacuums" slow down innovation and trap potential; And this doesn’t stop at companies. At scale, ONA can be applied to industries, political ecosystems, even nations. While social media offers surface-level signals—likes, followers, noise—ONA combined with AI can identify who truly shapes ideas, decisions, and outcomes. As this lens expands, so does the scope of influence modeling. Human behavior isn’t only shaped by people—daily micro-events, from weather to personal stress, also have impact. And through digital footprints—shopping data, movement patterns, health trackers—we’re getting closer to mapping these subtle influences too.

In the near future, influence modeling may go beyond people, incorporating behavioral triggers, emotional states, and environmental signals. But first, let’s look at a living example—LinkedIn—and how it has quietly become a real-world training ground for AI-driven influence prediction.

Amplifying Network Effects: Predicting Influence on LinkedIn

With over 900 million users, LinkedIn is the largest professional graph on the planet. A few years ago, researchers posed a bold question: Could we predict the 100 most influential people in the U.S.—purely from network data, not media mentions or public votes?

Instead of popularity, they studied connection patterns, social proximity, interaction frequency, and content spread. Their goal was to locate the real “influence gravity wells”—individuals who, like massive stars, bend the network around them and shape the behavior of others.

Using LinkedIn’s Economic Graph, they treated each user as a node and each relationship as a connection. Analyzing cross-industry links, clustering, and influence spread, they uncovered not just famous executives, but “structural influencers”—people who sit at the intersections of industries, moving talent, ideas, and capital behind the scenes. 

This was essentially ONA at a national scale. And just like astronomers track gravitational ripples to find new comets, network analysis can spot emerging influencers before they reach the spotlight. More advanced models could include call records, transaction data, even blockchain wallet activity. This kind of multi-dimensional modeling gives AI a powerful edge in mapping future influence. For strategy teams, investors, and political campaigns, this is transformative. When you can see where influence is actually concentrated, you no longer need to bet on noise—you invest where momentum is most likely to emerge.

Predicting Life Trajectories with AI: Promise and Peril

If we can forecast a star’s orbit or identify supernodes in social networks—can we predict a person’s life trajectory?

It’s a compelling yet unsettling idea. In theory, with enough behavioral, social, and semantic data, AI can model individuals and forecast career shifts, relationship changes, or even tendencies like entrepreneurial drive or emotional burnout.

This isn’t science fiction. Years ago, IBM developed a predictive attrition model that analyzed internal data—promotion cycles, skill growth, attendance—and accurately predicted which employees were likely to leave within six months. Management used this to intervene early, saving the company an estimated $300 million in potential turnover costs.

Now imagine scaling this to society. AI could combine LinkedIn networks, social media activity, spending patterns, and location data to cluster individuals into behavior groups—and forecast whether someone is likely to change industries, move cities, or start a business. It’s not fate—it’s pattern recognition across millions of behavioral signals. In this sense, we’re not isolated individuals—we’re particles in multiple systems: social networks, geographic flows, economic cycles. AI doesn’t predict a single event, but the directional gravity of everything pulling on us at once.

But humans aren’t stars. Our lives are shaped by free will, randomness, and cultural nuance. Any prediction is probabilistic. No model can eliminate the possibility of reinvention. And that’s where ethics matter. When AI flags someone as “high-risk” or “likely to leave,” is that used to support them—or quietly limit their opportunities? Prediction should guide, not judge. Responsible organizations use these insights to offer better paths, not preemptive exclusion.

Case Study: How LEAD Applies AI Prediction to Organizational Strategy

To see how AI-based prediction works in practice, look at LEAD—a company I founded to turn complex network analysis into strategy tools for organizations. We build enterprise knowledge maps to identify talent flow, influence nodes, and information bottlenecks. Think of it as Waze for knowledge—showing where it moves freely, and where it’s blocked.

In one case, LEAD analyzed collaboration and meeting behavior across a company. The results revealed that less than 1% of employees were serving as cross-functional connectors—far below the healthy benchmark of 5–15%. The impact was immediate: poor collaboration, inaccessible knowledge, and a 42% new hire attrition rate.

LEAD advised two key steps:

• Mentorship pairing between high-influence employees and new hires

• A cross-functional project accelerator to break down silos

Within months, retention rose—especially among those who joined our "knowledge sharing coffee chats." Engagement scores climbed, and for the first time, the company was named a "Best Place to Work." Executives credited the shift to improved “knowledge gravity.”

In another case, following a global merger, LEAD uncovered cultural silos—employees from the two legacy firms remained isolated, like galaxies yet to collide. Leadership launched bridge ambassador programs and cross-network collaboration, improving network integration and organizational efficiency within a few quarters.

These examples show that AI doesn’t just highlight where knowledge is—it pinpoints where it’s missing, and where intervention will have the most impact.

Conclusion: AI as a Strategic Navigation System

From the fixed orbits of stars to the messy trajectories of human systems, we’ve always asked the same question: What’s next?

In physics, prediction is possible because the rules are clear. In human systems, uncertainty dominates—but even limited foresight can shift outcomes. Spotting just 10% of a trend might be enough to see who the system is quietly positioning to win.

The best leaders won’t just read data—they’ll read the room. In an AI-shaped world, prediction doesn’t eliminate uncertainty. It helps us choose better, sooner.