Lift Rate: Founder Leverage Framework

It hit home at 11 a.m. one day, interviewing with Kate(a consultant I met). She cut straight: “I’m going to be frank; you’re smart, but you’re not South East Asian or white, didn’t attend an Ivy League, and don’t even live in the US”. Her point wasn’t personal; she was just giving me some reality shock, describing how the market pattern matches(not that I enjoy reality much). Though it locked in me the challenges ahead and sparked a way to measure them.

I developed a set of linear equations to calculate what I call lift rate.

Lift rate I believe to be the speed to moment your startup takes off, that momentum after some months or years you get as a team and things are moving really fast either the wrong way or great way.

L = αR + βO + γN — μM

L(t) = (t)αR + (t)βO + (t)γN — μM (time-based outcomes)

Where:

L= Lift rate

(t) = time in years

R = Resources (capital, skills, knowledge, energy, experience): 0–5 points

O = Opportunities (background, location, race, legal, market timing): 0–5 points

N = Network (team, mentors, investors, connections, community): 0–5 points

M = Missing factors (number of factors missing from each category)

α, β, γ, μ = Weighted coefficients: 0–3

Let’s dissect each category before I give scenarios.

Resources

Capital, energy, knowledge, skill, and experience: Outside or personal funding can go a long way in getting you off the ground. Opportunities easily slip away without the necessary skill set, knowledge, and capital. And if you lack energy, especially in the beginning, either the startup world isn’t for you or you haven’t found that problem worth chasing down the rabbit hole or a sector that piques your interest so much that you find problems in it to solve. I believe every founder who wants to succeed should have a high battery bar and a tremendous work ethic, as this can also influence the capital needed.

Opportunities

Opportunities in my Lift Rate framework capture the structural factors that influence a founder’s starting position. Different founders, depending on their background, geography, or proximity to established tech hubs, begin with different baseline conditions. These baselines can meaningfully affect early momentum.

Race/Gender: Statistically, certain groups raise less venture capital not because of talent gaps, but due to historical patterns in network density, capital concentration, and pattern-matching in the ecosystem. For instance, Black founders and women founders receive <2 percent of US venture funding (Crunchbase).

Background: Graduating from institutions like Stanford, MIT, Harvard, Cambridge, or ETH often acts as a signaling advantage. Not because these founders are inherently better, but because the market disproportionately rewards certain credentials with immediate trust, which affects Lift Rate.

Location/Legal: Environment can determine a lot; what you do, when you do it, why you do it, who you do it with and even how you do it. It’s the highest determining factors of one’s life.
A founder in Silicon Valley, where capital density, ambition density, and network density are high, naturally lifts faster than someone operating in regions where those factors are sparse.

Market Timing: It is very likely that if the market isn’t ready for your solution, it might affect your timeline for lift. By “not ready,” I mean the technology may not be mature, its adoption hasn’t taken off, regulations and policies haven’t shifted to favour your market, or society hasn’t yet found reasons to shift from old systems to new ones (e.g. Airbnb took off because people had become accustomed to sharing things online via Facebook, eBay, etc. amidst other things). These are what Mike Maples Jr. calls “inflections.”

Network

I dread networking events, it drains me . I am finding that distribution is a network play, just this time around on the customers side(consumers and businesses). Who knows you go a long way into helping product success. And also that the investor world functions mainly through referrals.

Scenarios to calculate Lift rate from the founder POV

Scenario 1 (L= αR + βO + γN — μM): Refugee Founder in Portugal

R:Capital=0, Skills=1(unrecognized), Energy=1, Knowledge=1, Experience=1

O:Race/Gender=0, Background=0, Location=0, Legal=0, Market Timing=1

N:Team=1, Investors=0, Mentors=0, Connections=0, Community=0

M= 9/15

α = 1, β = 3, γ = 1, μ = 1 (O-Heavy)

L= (1×4) + (3×1) + (1×1) — (1×9) = 8–9 = -1

Scenario 2: Refugee Founder in Silicon Valley (US)

R:Capital=0 (limited), Skills=1, Energy=1 (SV demands it), Knowledge=1, Experience=1

O:Race/Gender=0, Background=0, Location=1, Legal=0, Market Timing=1

N:Team=1, Investors=0, Mentors=1, Connections=0.5, Community=0.5

M = 5/15

α = 2, β = 1, γ = 2, μ = 1 (R-Heavy)

L= (2×4) + (1×2) + (2×4) — (1×5) = 13

L(t) = (t)αR + (t)βO + (t)γN — μM (Time-Based Outcomes)

Portugal(Time based)

T(0)

L(0) = (0×1×4) + (0×3×1) + (0×1×1) — (1×9) = -9

T(1; after 1 year)

L(1) = (1×1×4) + (1×3×1) + (1×1×1) — (1×9) = -1

T(2)

L(2) = (2×1×4) + (2×3×1) + (2×1×1) — (1×9) = 7

T(3)

L(3) = (3×1×4) + (3×3×1) + (3×1×1) — (1×9) = 15

Silicon Valley (US) Time-Based

T(0)

L(0) = (0×2×4) + (0×1×2) + (0×2×4) — (1×5) = -5

T(1; after one year)

L(1) = (1×2×4) + (1×1×2) + (1×2×4) — (1×5) = 13

T(2)

L(2) = (2×2×4) + (2×1×2) + (2×2×4) — (1×5) = 31

T(3)

L(3) = (3×2×4) + (3×1×2) + (3×2×4) — (1×5) = 49

FOOTNOTES

Analysis:

I weighted O=3 there because opportunities seem to be the make-or-break factor for the founder in Portugal. Without a crack at the right network resources barely budge. And Silicon Valley’s R-heavy because the founder leverages its opportunities and network to access resources-capital,energy; that pile up fast, so I set R’s weight at 2. Time compounds it; riding it out shrinks those gaps in Portugal but rockets SV ahead. These are scenarios, scale it 10x, and SV’s edge explodes.
Looking at the numbers, the founder in Portugal crawls from -1 in year one to 15 by year three. In Silicon Valley, they’re at 13 in year one, 49 by year three. Perseverance pays, but location is the multiplier.

Contextual Bias:

My upbringing was shaped by a deeply multicultural environment ; moving across countries thrice, multiple cities before turning 17 and left home without my parents by 11. My mother, a microbiologist and biochemist, and my father, who couldn’t afford medical school despite excelling academically (but later earned a PhD), all instilled in me that hard work is non-negotiable. While the exact ages may vary slightly due to memory, the impact of those experiences remains foundational to who I am.

Note: I understand “Lift” like success is non-linear, and many uncontrollable factors (luck, etc ) come into play. I built this formula primarily as a diagnostic tool to help myself reason through the variables I can control. If I could measure them, I believed I could strategically increase them. It’s not deterministic, founders should feel free to adjust the coefficients based on their industry, background, and specific leverage points

If you have any opposing viewpoints, I’d like to hear them. And do think of how the framework can be better I would love to hear from you too.