← back to explore

Documented path

Rohil Khare

Founders / operators · Founder/Entrepreneur · milestone at age 21 ·Field-leading
Selected age-relative milestone · age 21
Filed provisional patents in IoT in high school; published at ICRA during undergrad; conducted research at BAIR; completed EECS at UC Berkeley in 3 years; co-founded SigmanticAI (YC S25) at ~21, backe

Based in San Francisco Bay Area; described as 'young minds' and 'fresh UC Berkeley grads' by IEEE; completed EECS in 3 years; deep hardware/ML intersection expertise

Starting point

Education: UC Berkeley, EECS (2022-2025, 3 years); filed provisional patents in IoT as a high school student

Current position (2026)

Founder at SigmanticAI

Where the conditions came from

Three sources, read side by side

Each is placed on a −1 to 3 scale from documented evidence, and the three are never added together. A combined total would rank Rohil Khare against other people. Held apart, they explain why this path ran differently from another one—which is the only comparison this project supports.

The marble itself

What they brought

+2Tailwind

What capability, drive, or early skill is documented in the person rather than their surroundings?

Filed provisional IoT patents in high school. Published at ICRA during undergrad. Completed UC Berkeley EECS in 3 years. Research at BAIR (Berkeley AI Research). Strong early technical achievement in hardware/ML intersection.

Where it was dropped

What they were handed

+1Tailwind

What money, family standing, network, or permission was already in place before the work began?

Based in San Francisco Bay Area. No specific family information available. Bay Area residence suggests tech-connected family but unconfirmed. Early patent filing suggests supportive environment.

The shape of the track

What surrounded them

+2Tailwind

What place, timing, institution, or peer group made the next step available?

UC Berkeley EECS — top-tier program. BAIR (Berkeley AI Research). BWRC and Architecture Group research. YC S25. Bay Area tech ecosystem. Access to Berkeley's hardware/ML research community and Silicon Valley resources.

A −1 is a documented headwind, not a missing value; a 0 means the sources record nothing notable either way. Annotation confidence: Medium. These are analyst readings of what the sources record, not measurements of merit, talent, or effort. The twenty-two scored dimensions remain available inside the deeper research detail.

What moved through the conditions

Perseverance and luck stay visible—not scored.

Documented perseveranceBased in San Francisco Bay Area; described as 'young minds' and 'fresh UC Berkeley grads' by IEEE; completed EECS in 3 years; deep hardware/ML intersection expertise

This records repeated behaviour or recovery described by sources; it is not a grit or merit score.

Luck and unobserved varianceNo discrete luck event is documented in the reviewed biographical summaries.

A successful-only archive cannot recover all encounters, avoided setbacks, or alternative outcomes.

Open the legacy 22-field research annotation
How this path compounded
01 Starting advantages

3/24 starting-position score

Strongest documented signals: Frontier geography, Exceptional peer / cofounder, Direct domain exposure.

Describes the starting position, not what the person later made of it.

Cohort percentile: 52
02 Built or converted leverage

10/25 multiplying-capacity score

Strongest observed levers: Complementary team, Capital safety, Domain proximity.

Measures what was present, not whether it was inherited, earned, self-built, external, or mixed.

Cohort percentile: 68
03 Compounding trajectory

3 documented steps

The timeline below shows the sequence of work and transitions around the selected early milestone. It is evidence of a path, not proof that every step was necessary.

Milestone at age 21
04 Observed career standing

T2 · Field-leading

Dominant figure at the top of a field. The tier summarizes documented career recognition through the data cutoff—not Rohil Khare's worth or future potential.

Question four · where did the leverage come from?

Rohil Khare's leverage provenance

Each non-zero lever gets a best-supported origin, evidence signals, and confidence. Unresolved is the honest default when the biography cannot distinguish self-built, advantage-enabled, earned, external, or mixed.

Complementary team1/2
Advantage-enabledmedium confidence

One or more documented starting advantages plausibly enabled this lever.

Exceptional peer / cofounder (1/2)
Capital safety1/2
Unresolvedlow confidence

No current annotation distinguishes self-built, enabled, or earned origins for this lever.

No decisive linked signal
Domain proximity1/2
Advantage-enabledmedium confidence

One or more documented starting advantages plausibly enabled this lever.

Direct domain exposure (1/2)Frontier geography (1/2)
Prior reps1/3
Unresolvedlow confidence

No current annotation distinguishes self-built, enabled, or earned origins for this lever.

No decisive linked signal
Scarce skill depth1/3
Unresolvedlow confidence

No current annotation distinguishes self-built, enabled, or earned origins for this lever.

No decisive linked signal
Native distribution1/3
Unresolvedlow confidence

No current annotation distinguishes self-built, enabled, or earned origins for this lever.

No decisive linked signal
Elite ecosystem network1/3
Advantage-enabledmedium confidence

One or more documented starting advantages plausibly enabled this lever.

Frontier geography (1/2)Exceptional peer / cofounder (1/2)
Structural wave / timing1/3
Externalmedium confidence

A structural wave is external to the person, even when their position improved access to it.

Frontier geography (1/2)
Concentration intensity1/3
Unresolvedlow confidence

No current annotation distinguishes self-built, enabled, or earned origins for this lever.

No decisive linked signal

This is a bounded inference from the current annotations—not a claim about private effort, merit, or the percentage of Rohil Khare's outcome attributable to any origin.

Luck is not a leftover score.

Structural luck, Encounter luck, Event luck, Outcome variance can change every arrow in the path. This successful-only dataset cannot observe the near-identical paths that did not break through, so luck stays visible and unscored.

Within Founders / operators, Rohil Khare's starting-advantage total is at the 52th percentile. Separately, their built or converted leverage total is at the 68th percentile. Other T2 profiles average 7.8 / 24 starting advantage and 12.4 / 25 leverage. Similar scores appear in other tiers, so these figures describe positioning—not a cause.
Trajectory
  1. 2004 · age 0
    Born
  2. 2022 · age 18
    UC Berkeley, EECS (2022-2025, 3 years); filed provisional patents in IoT as a high school student
  3. 2025 · age 21
    Founded SigmanticAI
    YC Summer 2025
Primary leverage engine
Technical depth
Scarce technical / intellectual depth
Secondary engine
Timing/platform wave
Built/converted leverage
10 / 25
evidence: Low
Built or converted leverage

Multiplying capacity documented later in the path. Measures what was present, not whether it was inherited, earned, self-built, external, or mixed.

Started serious reps before 20
0/1
Prior reps
1/3
Scarce skill depth
1/3
Native distribution
1/3
Elite ecosystem network
1/3
Complementary team
1/2
Structural wave / timing
1/3
Concentration intensity
1/3
Capital safety
1/2
Domain proximity
1/2
Starting-advantage scores (0–2 each)

Access or conditions documented near the beginning of the path. Zero means "no clear evidence in reviewed sources," not "advantage was absent."

Family financial platform
0/2
Parent / family domain
0/2
Inherited audience / network
0/2
Elite institution pipeline
0/2
Frontier geography
1/2
Rare early tools
0/2
Dedicated mentor / coach
0/2
Exceptional peer / cofounder
1/2
Early online platform
0/2
Direct domain exposure
1/2
Prodigy / innate ability
0/2
Adversity / constraint catalyst
0/2

Family context

Not documented.

Parent / family domain

Not documented.

Archetype & tags
Elite performance pipelineFrontier ecosystemElite peer/collaborator
Evidence summary

Based in San Francisco Bay Area; described as 'young minds' and 'fresh UC Berkeley grads' by IEEE; completed EECS in 3 years; deep hardware/ML intersection expertise

advantage confidence: Low · source count: 5 · audit: not_independently_audited · status: founder_research_beta

Sources
Related — same primary engine