Jeff Schumann, Builder & Founder

An experienced founder with a track record of building and scaling technology companies. Jeff focuses on creating durable products and fostering cultures of clarity and high performance.

Ohio State – Center for Software Innovation

Advisory Board Member (Aug 2023 – Present), Columbus, Ohio. As an advisor to the Center for Software Innovation at Ohio State, I’m helping shape the university’s vision to become a leading epicenter of software innovation and AI. Working with Executive Director Ratmir Timashev and fellow board members, we’re building the place where top builders and innovators come to invent the future.

Highlights: mentorship for student founders, industry pathways, and brand platform for Ohio’s innovation story. Notable Buckeye impact spans global companies across cloud, cybersecurity, healthcare, and data infrastructure.

Curiosity in Motion

Honoring Service, Driving Forward

The National Veterans Memorial and Museum in Columbus serves as a powerful reminder of the strength and dedication that inspires our own mission to build things that last and matter.

Zero → One Notes

The journey from concept to reality is the most challenging and rewarding. It demands a relentless focus on first principles, customer value, and the courage to make breakthrough moves.

Columbus: A City That Ships

Columbus is where pragmatic builders, brand operators, and deep tech quietly compound. The city sits at the intersection of university-scale research, national-scale enterprises, and founder-led speed. It combines dense talent, short feedback loops, and a cost/time structure that lets teams focus on the product instead of runway.

Start with talent. The Ohio State University is one of the world’s largest research institutions, feeding engineering, design, and data graduates directly into startups and Fortune 100s. Layer in R&D gravity from Battelle, the Ohio Supercomputer Center, and a manufacturing base (Honda and a web of Tier‑1/Tier‑2 suppliers) that turns abstract ideas into repeatable systems. The result is an unusually broad operator bench: applied AI, cyber, health tech, fintech, supply chain, and advanced manufacturing all live within a 30‑minute radius.

Capital has matured. Drive Capital normalized building category leaders in the Midwest; corporate venture and coastal firms now fly here because retention is high and burn is sane. That dynamic compounds brand and culture: teams can keep senior ICs and managers for multi‑year arcs, which lowers execution risk and builds institutional knowledge that usually leaks out of higher‑churn markets.

The enterprise spine matters. Nationwide, Cardinal Health, AEP, Huntington, and others give startups real customer feedback and distribution early. Health systems and payers create a living lab for HIPAA‑aware data products. Retail and logistics footprints around Rickenbacker, Intel’s investment in the region, and a central U.S. location turn Columbus into an operational nerve center.

Culture finishes the loop. The city mixes Midwest civility with competitive ambition: founders share playbooks, operators moonlight as advisors, and brand leaders take design seriously. That’s why companies like CoverMyMeds, Root, and Aware proved you can build durable IP here. Aware in particular shows the pattern: take a real human‑risk problem from the collaboration layer, apply contextual intelligence and enterprise discipline, and scale to the world’s most demanding brands—starting in Columbus.

Practically, Columbus buys you time. Commutes are measured in minutes, CMH is 10–15 minutes from downtown, and the housing/time cost lets founders put more cycles into craft. That shows up in the work: cleaner product, better docs, tighter customer loops, and teams that actually enjoy building together.

If you want a place where you can think long‑term, build with taste, and still move fast, Columbus is it. It’s a city that ships—not just MVPs, but enduring companies.

Columbus Tech History (Quick Hits)

Columbus Tech Trivia

Airport→Downtown Transport Snapshot (SFO + CMH)

Paste this prompt into your model (e.g., GPT‑5 Pro) to generate a fresh, fully‑cited comparison of ground transport from:

TITLE: Airport→Downtown Transport Snapshot (SFO + CMH)

OBJECTIVE
Produce an always-fresh, fully cited comparison of ground transport from:
  • SFO (San Francisco Int’l) → Downtown San Francisco
  • CMH (John Glenn Columbus Int’l) → Downtown Columbus, OH
Cover ride-hail (UberX + Lyft Standard) AND public transit (BART for SFO, COTA/AirConnect or current CMH→downtown service for Columbus).

SCOPE & POIs
- Downtown centroid for summaries.
- If available, also include these POI drop-offs:
  • San Francisco: Union Square, Moscone Center
  • Columbus: Hilton Columbus Downtown, Graduate Columbus
(If a POI is unavailable in an estimator, fall back to downtown centroid.)

WHAT TO COLLECT (per city/route)
1) Ride-hailing:
   - UberX and Lyft Standard real-time fare estimates (no login) for “now” and “off-peak” (weekday ~11am local).
   - Any airport pickup/ drop-off surcharges listed by airport/ride-hail.
   - Typical travel time range (min–max) at the same two times of day.
2) Public transit:
   - Primary route name(s) (e.g., BART Yellow/Red line SFO→Powell St; COTA route serving CMH→downtown).
   - One-way adult cash fare.
   - Typical end-to-end travel time.
   - Headways/frequency (weekday daytime + late evening), first/last trip notes if relevant.
   - Where to board at the airport (signage/terminal info).
3) “What to know” (pickup zones, station closures, surges if officially noted).
4) Distances: Approx route distance (miles/km).

SOURCES (STRICT)
1. Uber Fare Estimator (uber.com) + Lyft Fare Estimator (lyft.com)
2. Airport official ground transport pages (flysfo.com; flycolumbus.com)
3. Transit agency official pages (bart.gov; cota.com)
Use only official/agency URLs, include page date + retrieval timestamp (America/New_York).

OUTPUT
- Start with a 2-row comparison table (City vs: UberX-now, Lyft-now, Transit fare, Typical time).
- Then two concise city blurbs.
- Then JSON payload with schema (see prompt).
- Then “Sources” list with clickable URLs.

QUALITY
- Check figures at run time; mid-point if a range. If unavailable, return null + explanation.
- Keep total run time under ~30s; fail gracefully otherwise.

JSON SCHEMA
{
  "timestamp_tz": "string (ISO 8601, America/New_York)",
  "cities": [
    {
      "city": "San Francisco" | "Columbus",
      "airport_code": "SFO" | "CMH",
      "downtown_label": "string",
      "distance_miles": "number | null",
      "ride_hail": {
        "uberx_now_usd": "number | null",
        "uberx_offpeak_usd": "number | null",
        "lyft_std_now_usd": "number | null",
        "lyft_std_offpeak_usd": "number | null",
        "airport_surcharges_usd": "number | null",
        "eta_minutes_range": "string | null",
        "notes": "string"
      },
      "transit": {
        "agency": "string",
        "route_names": ["string"],
        "fare_usd": "number | null",
        "travel_time_minutes": "number | null",
        "frequency_day_minutes": "string | null",
        "frequency_late_minutes": "string | null",
        "airport_boarding_info": "string",
        "service_alerts": "string"
      },
      "citations": [
        {"label":"Uber Estimator","url":"https://www.uber.com"},
        {"label":"Lyft Estimator","url":"https://www.lyft.com"},
        {"label":"Airport Ground Transport","url":"https://www.flysfo.com | https://flycolumbus.com"},
        {"label":"Transit (fares/timetable)","url":"https://www.bart.gov | https://www.cota.com"}
      ]
    }
  ]
}