DEV Community: Adi

The Trillion-Dollar Contradiction: Why Finance Loves Open Source but Refuses to Trust It

Adi — Sun, 22 Mar 2026 21:20:17 +0000

Picture a boardroom at one of the world’s largest banks. The CTO is walking through the latest platform architecture. Kubernetes for orchestration. Kafka for event streaming. PostgreSQL on the data layer. TensorFlow running the ML models. Every critical piece of the stack is open source, and nobody in the room disputes that it has made things better, faster, cheaper. Then a hand goes up. “What are we doing to contribute back?”

Silence.

I keep coming back to that moment because it captures something true about where financial services stands with open source in 2025. The consumption story is settled. Nearly everyone agrees this stuff works. But the contribution story? That one is barely getting started, and the longer the gap persists, the more dangerous it becomes.

Consensus Without Conviction:
The latest industry data is unambiguous. Ninety-three percent of respondents say open source improves software quality. Eighty-seven percent agree it delivers real business value. Eighty-four percent believe it is critical to the future of financial services (FINOS & Linux Foundation Research, 2025). These are not niche opinions from a developer subculture. This is the mainstream view.

And still, the gap between appreciation and action is staggering. Nearly one in five organizations report saving more than a million dollars a year from open source. That kind of number typically gets a standing ovation in any cost review meeting. But when the conversation shifts from “how much are we saving?” to “how much should we invest back?”, the enthusiasm evaporates.

Why? Because financial services is, at its core, a risk management business. And open source, for all its proven utility, still sits in an uncomfortable gray zone on most risk registers. Over half of survey respondents flag security vulnerabilities as their top concern. Close behind, at 48%, is the lack of ongoing maintenance. Both worries are fair. But they also beg a question that most institutions would rather not answer: if no one funds the maintenance, who exactly is doing the securing?

The Free Rider Problem No One Wants to Name:
Let’s be direct about something. Open source is free in the same way a public park is free. Somebody built it. Somebody maintains it. Somebody pays for that, whether through corporate sponsorship, volunteer labor, or foundation grants. The fact that you can walk in without a ticket does not mean it costs nothing to exist.

A Harvard Business School study put some hard numbers behind this intuition. Researchers estimated that the demand-side value of widely used open source software sits at roughly $8.8 trillion, and that companies worldwide would need to spend 3.5 times more on software if open source simply vanished (Hoffmann et al., 2024). That is not a rounding error. It is a dependency of civilizational scale, maintained by a community that most of its beneficiaries barely acknowledge.

To be fair, financial institutions are making structural moves. Sixty-four percent of large firms have stood up an Open Source Program Office. Sixty-seven percent have affiliated with open source organizations. These are real steps. An OSPO is not window dressing; it means someone inside the organization is thinking about policy, governance, licensing, and community engagement in a coordinated way.
But standing up an OSPO and actually contributing at scale are different things entirely. When you ask firms why contribution lags, two answers come back with metronomic consistency: 48% say there is no clear ROI, and 48% point to legal and licensing headaches.

The legal concern, while often overstated, at least has a concrete shape. Lawyers can work through licensing questions; it just takes time and willingness. The ROI objection is more interesting because it reveals a deeper conceptual problem. Traditional ROI calculations are transactional. You spend money, you get a thing, you measure the thing’s value. Open source contribution does not work that way. Its returns are systemic: influencing the trajectory of projects your competitors also depend on, reducing the odds of the next catastrophic vulnerability, earning credibility in communities where architectural decisions get made. Those returns are real. They are also nearly impossible to capture on a single line in a quarterly report, which is why they need to be championed at the executive level, not delegated to engineering.

What Actually Drives the People Who Do Contribute:
Here is what I find encouraging. When you look at the firms and individuals who do contribute actively, their reasons are not the ones you would hear in a strategy deck. The top motivations are giving back to the community, influencing critical projects, and reducing technical debt. That last one is the sleeper.

Technical debt in financial services is enormous, and a surprising amount of it is self-inflicted through poor open source hygiene. Every internal fork that drifts from the upstream project. Every patch you apply locally instead of pushing back to the community. Every major version you skip because the upgrade path is too tangled. It all compounds. Three years later, you are maintaining a bespoke version of something that the rest of the world has moved on from, and your team is spending cycles on plumbing work instead of building things that matter.

Contributing upstream is not charity. It is an engineering discipline. It keeps your implementation aligned with the community, reduces your maintenance surface, and, not incidentally, gives your engineers visibility and credibility in the ecosystems they work in every day. The firms that treat open source contribution as a line item to be minimized are the same ones drowning in technical debt five years later and blaming the technology.

Open Source as AI Infrastructure:
Everything I have described so far was already true when open source was mainly about databases, container runtimes, and messaging systems. The arrival of generative AI has raised the stakes considerably.

When financial services professionals are asked which open source components have the greatest impact on AI development, the top three answers are standards (56%), models (54%), and frameworks (52%). Think about what that means. The industry is not just using open source tools to build AI. It is building AI on top of open source foundations. The models themselves, the frameworks for training and deploying them, and the standards governing their use are all rooted in the open ecosystem. Trying to go fully proprietary on AI at this point is like trying to build a house without using lumber.

Theoretically possible, but expensive to the point of absurdity.
And yet, 49% of respondents say they expect GenAI’s biggest impact to be on internal developer productivity, compared to just 23% who see it primarily improving client-facing services. The industry is, in effect, pointing AI inward. That is a reasonable starting position, but it carries a specific implication: the quality and reliability of the open source AI toolchain is not an abstract concern. It directly affects the people writing the code that runs your trading platforms, your risk engines, your compliance systems. Getting this wrong is not a theoretical risk.

Hilary Carter of the Linux Foundation put it well when she noted that financial services has moved from “cautious experimentation” to a “full-fledged strategic practice woven into the operating fabric” of the sector (FINOS, 2025). That shift is welcome. But a practice woven into your operating fabric is also one you cannot afford to neglect. If the open source AI ecosystem deteriorates because the biggest consumers are not contributing, the consequences will not be hypothetical. They will show up in production.

The ROI Timeline and What It Actually Tells Us:
Forty-four percent of organizations expect to see ROI from GenAI within two to five years. Another 18% say they are already there. Both numbers deserve scrutiny.

A two-to-five-year ROI window in AI is practically a geological epoch. The models available in 2027 will be dramatically different from the ones organizations are piloting today. Any institution that treats its current AI stack as a fixed investment rather than a rapidly evolving capability will find itself re-platforming sooner than it planned. Meanwhile, the 18% already seeing returns have a compounding advantage. They are learning faster, iterating faster, and building organizational knowledge that their slower peers will struggle to replicate.

The firms best positioned to ride this wave are the ones most embedded in the open source communities building the next generation of AI tooling. Not because open source is always the best option for every component, but because proximity to the ecosystem gives you early signal. You see what standards are forming. You know which frameworks are gaining traction. You have relationships with the people building the tools that will matter in 2028. You cannot buy that kind of insight after the fact.

Collaboration as Competitive Advantage:
One finding that keeps nagging at me. When asked where open source delivers the most value, over half of respondents point to open collaboration on industry standards. Not speed. Not cost. Standards.
In hindsight, this makes perfect sense. Financial services spends an extraordinary amount of money on regulatory compliance, and a significant chunk of that spending goes toward reconciling incompatible systems, formats, and protocols across institutions. Shared open source standards reduce that friction. They create a common language. And, crucially, they shift competitive dynamics away from “who built the better plumbing” and toward “who serves the customer better”, which is where competition should be anyway.

The AI governance angle makes this even more urgent. The Log4j crisis offers an instructive parallel. In 2022, the U.S. Cyber Safety Review Board classified the vulnerability as “endemic,” warning that unpatched instances would remain in systems for a decade or more, and called out the fact that the open source community was “under-equipped” to handle security at scale (Cyber Safety Review Board, 2022). The FTC followed up with an unusually direct warning: companies that failed to remediate known open source vulnerabilities could face legal action (Federal Trade Commission, 2022). That was not a gentle suggestion. It was a signal that the regulatory environment around open source maintenance is tightening, and institutions that ignore it are accumulating legal as well as technical risk.

As AI governance frameworks take shape around the world, financial services has a narrow window to lead. Institutions that contribute to open standards and governance models now will help write the rules. The rest will simply have to follow them.

The Strategic Imperative:
I want to be clear about what I am arguing here, because it is easy to mistake this for idealism. I am not saying financial institutions should contribute to open source because it is the right thing to do, although it is. I am saying they should contribute because the alternative is untenable.

The old playbook, where you consumed open source quietly, kept your head down, and managed risk through isolation, worked when open source was a nice-to-have. It does not work when open source is the substrate of your entire technology strategy, your AI ambitions, and your compliance infrastructure. At that level of dependency, refusing to invest in the commons is not prudent risk management. It is the risk.

The organizations that get this right will do more than save money on software. They will shape the standards that govern their industry. They will influence the AI frameworks their competitors use. They will have a say in how security, governance, and interoperability evolve across the sector. That is not a technology decision. That is a strategic one.

So the next time someone raises a hand in a boardroom and asks about contribution, maybe the answer should not be silence. Maybe it should be: “What are we risking by staying quiet?”

Right now, the honest answer is: quite a lot.

References:

Cyber Safety Review Board. (2022). Review of the December 2021 Log4j event. U.S. Department of Homeland Security. https://www.cisa.gov/sites/default/files/publications/CSRB-Report-on-Log4-July-11-2022_508.pdf
Federal Trade Commission. (2022, January 4). FTC warns companies to remediate Log4j security vulnerability. https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2022/01/ftc-warns-companies-remediate-log4j-security-vulnerability
FINOS & Linux Foundation Research. (2025). The 2025 state of open source in financial services. https://www.linuxfoundation.org/hubfs/Research%20Reports/05_FINOS_2025_Report.pdf
Hoffmann, M., Nagle, F., & Zhou, Y. (2024). The value of open source software (Harvard Business School Working Paper No. 24-038).
Cover Image Source: Shaker.AI

Books That Found Me When I Wasn’t Looking

Adi — Mon, 23 Jun 2025 18:37:01 +0000

I didn’t set out to make a reading list.

Honestly, most of the books I’ve found useful were ones I stumbled on. A friend mentioned it, or I saw it in a strange corner of a bookstore, or I picked it up, put it down, then picked it back up two years later when something in my life finally made me ready for it.

What’s strange is, a lot of them weren’t directly related to work. Not in the obvious way. They weren’t books about “career growth” or “leadership hacks.” But they shifted something in me—how I make sense of chaos, how I ask questions, how I interpret what’s not being said in a room.

So no, this isn’t a ranked list. I’m not trying to be helpful or definitive. I’m just trying to trace a line backward—through the ideas, arguments, phrases, and small turning points that stuck.

Maybe some of them will stick for you too. Maybe not. But either way, this is me writing it out.

The Psychology of Money – Morgan Housel

This one came into my life at a time when things were fine on the surface. I wasn’t panicking, but I was overthinking every financial decision. Not in a productive way—more like mentally spiraling around “what if I’m being reckless?” or “what if I’m being too cautious?” depending on the day.

Housel’s writing felt like a quiet interruption. He doesn’t tell you what to do with your money. He shows you why two smart people can look at the same scenario and make opposite decisions—and still both be right, in context.

What clicked for me was the idea that money is emotional history. It's shaped by what we saw growing up, what we fear, what we believe about risk. After that, I stopped expecting my choices to make perfect sense to other people. And I stopped judging theirs. That alone gave me a lot more peace than I expected from a finance book.

Orbiting the Giant Hairball – Gordon MacKenzie

There’s no other book like this one. It’s a weird little manifesto from a longtime creative at Hallmark—yes, greeting cards Hallmark. But it’s also one of the most honest things I’ve ever read about staying creative inside systems that slowly try to flatten everything into safe, predictable outputs.

The first time I read it, I had no idea what I was supposed to take from it. It’s sketchy and nonlinear and slightly unhinged. But I remember one line: “To be fully human is to be creative.” That one stuck.

Later, when I was in a role where everything felt like compromise—more process, less spark—I came back to this book. It helped me realize that preserving creativity isn’t about being loud or rebellious. Sometimes it’s about staying quietly weird inside a system that wants you to be efficient and beige.

Antifragile – Nassim Nicholas Taleb

I didn’t read this one in a single go. Actually, I hated it the first time I tried. Taleb’s writing style is—how do I say this—abrasive. It feels like he’s yelling at you while smirking. But the core idea wormed its way into my thinking and refused to leave.

The idea is this: fragile things break under stress. Resilient things survive stress. But antifragile things—those get stronger because of stress.

That completely rewired how I was looking at projects and systems. I had been aiming for resilience: can this withstand pressure? But Taleb pushed me to ask: can this actually benefit from unpredictability? What if the mess isn’t just survivable—it’s fuel?

I still don’t agree with everything he says. But I think about that idea almost every time something breaks and I’m tempted to just patch it back together and move on.

The Score Takes Care of Itself – Bill Walsh

This came at a time when I was feeling stuck—not dramatically, just… out of rhythm. I was managing a team, hitting goals, but something about the dynamic felt flat. People were going through the motions. Including me.

Walsh, a legendary football coach, writes with the intensity of someone who believes every detail matters. And not in a performative, micro-managey way—more like, if your standard is excellence, then you don’t get to switch it off. You brush your teeth with it. You show up to meetings with it. You prepare small things with care because that’s who you are, not because someone’s watching.

That was both confronting and freeing. I wasn’t bringing that energy. And suddenly it made sense why the culture felt loose. It wasn’t a vibe problem. It was a standard problem. Reading that book didn’t fix it—but it helped me name what needed to shift.

The Art of Learning – Josh Waitzkin

This one surprised me. I expected something practical or motivational. But what I got was strangely meditative. Waitzkin, who was the real-life chess prodigy from Searching for Bobby Fischer, talks less about chess or martial arts and more about the texture of mastery.

It’s about how you get better—not in a linear, input-output way, but in waves. Plateaus. Regressions. Breakthroughs. And more importantly, how you observe yourself learning.

That lens stuck. I started to notice how I learned under pressure. How I reacted to being wrong. How I rushed through things when I felt insecure. It’s not a “10 rules for success” kind of book. It’s slower, gentler. And sometimes that’s exactly what you need.

The Hard Thing About Hard Things – Ben Horowitz

This book has a misleading title. You think it’s going to be hard-nosed tactical wisdom. And sure, there’s some of that. But what it actually is—at least to me—is a long, conflicted letter about how lonely leadership can be when things go bad and there’s nobody left to blame.

Horowitz talks about firing friends. Making impossible decisions. Having no good options, only less-worse ones. And not in a heroic tone—he’s often unsure, regretful, emotionally raw.

I didn’t read this because I was leading a company. I read it because I was in a messy project with no clear path, and I needed to feel like the fog I was walking through wasn’t just me being bad at my job. That fog is real. It’s not a sign you’re failing. Sometimes it’s just what it looks like to keep going.

Bird by Bird – Anne Lamott

This book is technically about writing. But I’ve applied it to almost everything.

The title comes from a story she tells about her brother, overwhelmed by a school project on birds, and her father tells him: “Bird by bird, buddy. Just take it bird by bird.”

That’s it. That’s the magic. It’s a book about imperfect starts, clumsy middles, and honest work. Lamott is funny, flawed, and entirely without ego. She writes like someone who’s tripped over her own mind enough times to know what real progress feels like—and what it doesn’t.

Whenever I get frozen trying to make something good, this is the book I open. Not to get inspired. Just to remember: do the next small thing.

The Fifth Discipline – Peter Senge

This book isn’t an easy read. It’s slow, thick, full of diagrams and systems language. But there’s a moment when it clicks—and once it does, you can’t unsee it.

Senge’s core point is this: most organizational dysfunction is not about bad people. It’s about invisible systems. Feedback loops. Misaligned structures. Goals that contradict each other. And most of the time, we treat the symptoms instead of asking what the system is designed to produce.

That was a huge shift for me. It made me stop blaming individuals when things felt off. It gave me permission to look at the architecture. And slowly, that helped me influence change that actually stuck.

Man’s Search for Meaning – Viktor Frankl

This is not a business book. It’s not even really a “self-help” book. It’s a record of survival. And a theory of purpose, built in one of the darkest human environments imaginable.

I read it during a quiet period where I felt weirdly disconnected from everything I was working on. Like I was hitting all my goals, but none of it was moving me. Frankl doesn’t offer comfort. He doesn’t give you a process. He just says: if you can find meaning in your suffering, you can endure almost anything.

It didn’t make me leap out of bed with inspiration. But it helped me get honest with myself. That’s more powerful, in the long run.

The Mom Test – Rob Fitzpatrick

This is the only book on this list that I would hand to someone with zero context and say: just read it, now.

It’s short. It’s sharp. And it changed how I talk to people about ideas.

The premise is simple: most people are too polite to give you honest feedback—especially when you ask bad questions. You think you’re validating your idea. You’re actually just collecting approval that doesn’t mean anything.

This book helped me stop leading people into compliments. I started asking better questions. I started listening for what wasn’t being said. I’ve saved weeks of wasted effort just by remembering what I learned in 90 pages.

That’s all of them.

Conclusion:
I don’t know if these books will land for you the way they did for me. Maybe you’ll love one. Maybe another won’t click at all.

But if you’ve ever felt stuck, or foggy, or just unsure why something isn’t working the way it should—maybe one of these gives you a phrase, or a pause, or a shift.

That’s how they worked for me.

And that’s enough.

That’s my reading list—for now, at least. If any of these books hit home, or if you’ve got one that changed how you think or work, hit reply and let me know. I love hearing what’s stuck with people—it almost always leads me to my next favorite read. And if you enjoyed this and want more slow, thoughtful content like it, feel free to share it or subscribe. I don’t write often, but when I do, it’s with intention.

Until next time.... continue learning, unlearning and relearning folks!

Why I Stopped Believing in “High-Performing Teams”

Adi — Sat, 21 Jun 2025 03:11:27 +0000

There’s a story I don’t tell in retros. Not because I’m hiding it—just because it doesn’t fit into sticky notes and burndown charts.

It was a team I led during one of the most complex innovation cycles I’ve ever been part of—cross-functional, multi-time-zone, high visibility. We were working on a risk intelligence model powered by machine learning, and from the outside, we looked like a textbook Agile success story. Everyone loved referencing us. Our slide made it into a global town hall.

But there’s a truth behind the slide.

Yes, the team delivered. Yes, the roadmap held. But it was a specific, time-boxed stretch of momentum—maybe four months—when everything just aligned. People, purpose, bandwidth, backlog, timing. We had a good product owner, a smart designer who didn’t disappear behind Figma, and a lead engineer who didn’t let ego into architecture discussions.

There was no dysfunction to fix. It just... worked. I remember thinking, This is it. This is what we mean by high-performing teams.

And then, like sand slipping through fingers, it changed.

First, one of the senior engineers left. Then we got pulled into a wider platform rewrite that had nothing to do with our mission. The product owner rotated to another line of business. And the new one? Different rhythm. Different voice. Not bad, just unfamiliar. One sprint we were flying. The next, we were dragging unfinished stories across columns like we were moving furniture.

Nobody failed. But the music stopped. Quietly. And most people didn’t notice.

But I did.

I used to chase “high performance” like it was a trophy.

It’s not. It’s a moment. A phase. And like all phases—it passes.

I wish someone had told me that earlier.

Instead, I spent years looking at high-performing teams like they were something I was supposed to build, maintain, and export across the org. I thought if I just got the right blend of personalities, gave them autonomy, shielded them from distractions, and enforced Agile rituals with care—I could engineer greatness.

That belief is romantic. And false.

Because high performance isn’t something you build. It’s something that emerges. Temporarily. Under very particular conditions. And it doesn’t always announce when it’s arrived, or when it’s leaving.

There’s no retrospective slide for “team soul erosion.”

What I’ve realized, after leading enough squads, is that most of our Agile mythology relies on a dangerous assumption: that if a team is good, it’ll stay good.

That’s not how teams work.

People change. Energy shifts. Trust, even when well-earned, thins in silence. Greatness is real—but it’s not permanent.

We don’t like saying that. Because it means we can’t take credit for it when it’s working. And we can’t fix it with tooling when it’s not.

The closest I’ve seen anyone capture this truth is Richard Hackman. He led a body of research at Harvard for decades, quietly studying what made teams effective—not in theory, but in real-world contexts like cockpit crews and intelligence task forces. His conclusion was blunt: teams don’t succeed because they’re high-functioning or friendly. They succeed when five conditions line up—clear direction, solid structure, stable membership, supportive context, and expert coaching.

And here’s the kicker: most organizations can’t offer all five consistently. The business doesn’t stand still long enough for that alignment to last. And that’s not a failure. That’s the job.

Amy Edmondson’s work on psychological safety only deepens that insight. She showed—long before it became a buzzword—that learning and performance are byproducts of interpersonal risk-taking without punishment. Meaning: the minute people don’t feel safe saying “I don’t know” or “I disagree,” your velocity numbers stop telling the real story.

In theory, high-performing teams are safe, supported, structured, and clear.
In practice, they’re lucky. And when that luck hits, a good leader’s job is to host it—not hoard it. And when the moment ends? To know when to let go.

I stopped trying to immortalize great teams. I stopped naming them in slides. I stopped calling them “high-performing” like it was a medal.

Now I say something else.

I say: this team is in a moment.
That moment could be flow. Or friction. Or fog. All of those are valid. All of those are human.

When a team is thriving, I don’t freeze them. I ask them what they’re afraid of losing. When a team is stuck, I don’t fix them. I listen for what changed that no one acknowledged.

I no longer use the phrase “high-performing team” the way I used to. Not because I think excellence is a myth. But because permanence is.

We should stop holding teams to a bar that assumes stasis.
The bar should be: can they notice when things change? Can they talk about it? Can they reorient? Can they grieve the version of themselves that no longer fits, and choose to become something new?

If a team can do that? That’s what I call maturity.

That’s what I build for now.

I look forward to your thoughts, comments and feedback. If this was helpful, engaging and informative do like, share and subscribe. You never know who may need it, or could benefit from it.

Until then, keep learning unlearning and relearning folks.

References:

Tuckman, B. W. & Jensen, M. A. C. (1977). Stages of small-group development revisited. Group & Organization Studies.
Hackman, J. R. (2002). Leading Teams: Setting the Stage for Great Performances. Harvard Business School Press.
Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly.

Numbers Don’t Lie. But They Do Leave Without Saying Goodbye.

Adi — Thu, 19 Jun 2025 19:36:51 +0000

If you’re building something worth pitching—something more than a glorified hobby with a login screen—you need to know your numbers. Not "I’ll get back to you" know them. I mean know them like you know your co-founder’s coffee order.

I’ve worked inside innovation teams at one of the world’s largest financial institutions. And I’ve watched too many founders—smart, legit, ambitious—get ghosted by investors simply because they couldn’t walk through their unit economics.

It’s not personal. It’s math.

So here it is:
10 numbers that’ll either carry your pitch or quietly kill it—explained like a real person would, with examples, and zero BS.

Burn Rate

How fast are you lighting your cash on fire???

If you’re spending $80K a month to keep the lights on (payroll, AWS, your workspace snacks), and you’ve got $400K in the bank… that’s 5 months of oxygen. Not 6. Not “depends.” Five. And that’s if you don’t hire your cousin as Head of Product Strategy.

Real talk: Investors want to know when the plane runs out of fuel—before they board.

CAC (Customer Acquisition Cost)

How much does it cost to convince someone to pay you???

You spend $10K on ads, content, and cold outreach. That brings you 100 paying users. Your CAC = $100. Great!!!

Now ask:
Are you selling a product for $20 one time? Or $100 per month? Because if your CAC is higher than your LTV, you’re not building a business— you’re sponsoring users. Plain and simple.

LTV (Customer Lifetime Value)

How much one customer is worth over time???

Let’s say you charge $25/month. The average customer sticks around 12 months.
LTV = $300.

If your CAC is $80? You’re in the green.
If it’s $350? You’re basically paying people to hang out.

Rule of thumb:
You want your LTV to be at least 3x your CAC. Otherwise, the math stops working at scale. And based off my experience you are much more appealing if you are closer to 5x if you ask me.

Gross Margin

What you actually keep after delivering your service or product???

If you sell a subscription for $50/month and it costs you $10/month to host, maintain, and support it, your gross margin is 80%.

Good: SaaS companies often hit 70–90%.
Bad: If you're below 30%, your "scalable" business will collapse under weight.

Runway

How long before you run out of cash???

Same math as burn rate. You’ve got $250K in the bank. Spending $50K/month.
You’ve got 5 months. That’s your runway.

Investors ask: It answers the simple question “If we don’t fund you, how long do you survive?” If you don’t know that answer, you're not fundraising— you’re freelancing with hope.

MRR / ARR

Monthly / Annual Recurring Revenue = predictable income.

If you’re pulling $20K/month in subscriptions, that’s $240K ARR. Simple.

What investors care about:

Is that number growing?
Is it churn-resistant?
And are you dependent on one or two big contracts that could walk?

Churn Rate

How fast are your users leaving— and should we be worried???

You had 500 users at the start of the month. Lost 50.
That’s 10% churn.

That’s high.
Annualize that and… ouch. You're not growing. You're replacing.

Fix it before you fundraise. Or at least explain why churn’s high and what you’re doing to plug the holes.

Payback Period

How long before you recover your CAC???

Let’s say your CAC is $250. And your customer pays $50/month.
It takes 5 months to break even.

Healthy range: 3–6 months.
Longer than that? You need serious retention—or a bank account that can handle the wait.

EBITDA

Earnings Before Interest, Taxes, Depreciation, and Amortization.

It’s not flashy. It’s not fun. But it tells grown-up investors: “Here’s what we really make once the accounting fog clears.”

If it’s negative, that’s fine—early-stage often is. Just don’t act surprised when someone brings it up. You should’ve done that math before the pitch.

Valuation (Trust me: Not Just a Feeling, it logic and facts)

What’s your company worth—and what supports that number?

If you’re pre-revenue and saying $30M because a friend raised at that, please stop.

Valuation = traction + market comps + revenue + momentum + team.

Inflated numbers make investors run.
They don’t correct you—they just ghost you.

Real Talk Before You Close That Tab:

I met this founder once—early days, raw product, but you could tell he actually cared. He wasn’t trying to look impressive. No buzzwords. No “disruption” talk. Just a person trying to fix something annoying and important.

He walked into the room with a spark. Not swagger—just that quiet intensity.

We were leaning in.

Then, middle of the pitch, someone asks,
“So what’s your monthly burn?”

And I kid you not, he said,
“Umm… I think my co-founder has that. I haven’t looked recently.”

That was it.

No meltdown.
No awkward silence.
Just… a click. Like a window closing in the background.

The product? Still smart.
But the moment? Gone.

Nobody was angry. No one laughed. We even thanked him.
But no one followed up.

Why? Because it didn’t feel like a business.
It felt like a maybe.

I’ve seen so many versions of that same scene play out.
It’s never about charisma. It’s not even about the idea, half the time.

It’s about whether the person asking for money actually knows what they’re building. Not the dream—the mechanics. The guts. The ugly Excel math nobody brags about on Twitter.

And you don’t have to be perfect.
You just have to be in it. Eyes open. Numbers in your head.
Because if you’re asking people to believe in what you’re building, you’d better believe in the scaffolding holding it up.

So yeah, know your CAC. Your LTV. Your margins. Your churn.
Not to check some box on an investor’s sheet.
To prove to yourself that the thing you’re spending your life on… has legs.

That it can stand.
And run.
And maybe, someday, outlast you.

No pitch deck will do that part for you.
No co-founder can answer those questions on your behalf forever.

If it’s your vision— own the math.
If it’s your company— learn the cost of keeping it alive.

That’s the stuff fundable companies are made of.

The rest? The logos, the taglines, the “go-to-market” plans?.... All of that’s just packaging.

Closing Remarks:

It is my sincere hope that this was informative and helpful for all you Founders trying to build the next big thing. While there a many more ratios and concepts, this these are the crux of them.

I hope this gives you a perspective of being on the other side evaluating your hard work and passion, and sets you up for success in your next Investor Review.

I look forward to your thoughts, comments and feedback. If this was helpful, engaging and informative do like, share and subscribe. You never know who may need it, or could benefit from it.

Until then, keep learning unlearning and relearning folks.

AI’s Invisible Hand: The Emerging Intelligence Gap in Financial Services

Adi — Thu, 19 Jun 2025 03:05:34 +0000

In boardrooms across Wall Street and beyond, AI is now a fixture—discussed in every strategic offsite, featured in every quarterly roadmap. Yet, beneath the noise and novelty lies a far less visible, far more insidious challenge: the Intelligence Gap.

This isn’t about machines replacing humans. It’s about some institutions accelerating faster than their industry’s cognitive center of gravity. We are witnessing the birth of a new kind of systemic risk—one not caused by capital imbalances, but by knowledge asymmetries. In this silent divergence, the firms who understand how to wield AI at scale will not just outcompete others—they’ll reshape the rules of the game before the rest realize the game has changed.

The question is no longer "What can AI do for us?" but rather "What happens when only a few can afford to think at AI’s speed?"

The Unseen Divide: Intelligence as Capital
In the past, financial power was hoarded through three levers: balance sheets, relationships, and regulatory mastery. Today, a fourth force is emerging—intelligence capital: the capacity to synthesize, simulate, and act on data faster than the market, the regulator, or even the client can perceive.

Firms investing heavily in foundation models, proprietary data pipelines, and real-time decision infrastructure aren’t just innovating. They’re compounding knowledge. They’re building cognitive compounding loops—feedback systems that learn faster, get smarter, and deepen defensibility with every transaction.

This advantage isn’t just technical. It’s temporal. When one bank simulates 10,000 credit scenarios in a day and another in a quarter, they’re not just operating at different speeds—they’re inhabiting different futures.

This is the real competitive moat in financial services—and almost no one is talking about it.

From Efficiency to Epistemology
Most AI conversations still revolve around optimization: faster onboarding, lower fraud rates, smarter collections. But the next frontier isn’t operational—it’s epistemological. It’s about how we know what we know.

Imagine an AI that not only detects anomalies in your trade flow but also infers why they occur, simulates what if scenarios, and advises what next. These are not workflows—they are meta-workflows. They’re not just changing the outputs of financial institutions. They’re changing how financial institutions perceive risk, opportunity, and reality itself.

This creates a dilemma. Because as some firms shift into AI-native cognition, the interpretive gap between the human and the machine—and between AI-mature and AI-immature firms—begins to widen. Communication frays. Coordination lags. Mispricing occurs. And over time, the market starts to fracture cognitively.

We are no longer just building models. We are building epistemic engines that shape the very fabric of financial truth.

The Quiet Fragility of AI Concentration
There’s another, deeper risk at play. As AI becomes more expensive to train, more reliant on proprietary data, and more integrated into real-time decision flows, it becomes concentrated—in the hands of a few global banks, tech-forward asset managers, and cloud-native fintechs.

This creates a structural fragility: If too few players own the cognitive infrastructure of finance, systemic blind spots grow. Think of it like the 2008 financial crisis—not triggered by individual bad actors, but by widespread over-reliance on misprized models and assumptions.

Now, imagine a future where half the global credit market is underwritten using the same few AI platforms, trained on overlapping datasets, and optimized for the same risk signals. That’s not diversification. That’s monoculture. And monocultures fail catastrophically.

We are sleepwalking into cognitive concentration risk—and the industry has yet to design a framework to measure, audit, or govern it.

Rethinking Regulation: From Compliance to Cognition
Our regulatory frameworks were designed to govern transactions, not intelligence. Model risk guidelines focus on inputs, outputs, and documentation—but not on learning loops, synthetic data generation, or autonomous model updates.

As AI grows more self-referential—models fine-tuning themselves, agents making recursive decisions—the old paradigm of “check the model annually” becomes dangerously outdated. Supervision must shift from static validation to continuous oversight. Regulators must evolve from examiners into AI-aware risk engineers—capable of understanding how models reason, where they fail, and how to design systems for transparent cognition.

If we fail to bridge this gap, we won’t just see AI failures—we’ll see governance failures that trigger reputational, legal, and systemic consequences.

The Human Imperative in an AI World
Ironically, in a world dominated by machines, human judgment becomes more—not less—valuable. But the kind of judgment we need is different.

We don’t need more manual reviews of output. We need people who can ask better questions of the machine. Who understand that explainability is not a tradeoff with performance—it’s the foundation of trust. Who see that AI doesn’t eliminate ambiguity; it reframes it. Who can sit at the intersection of ethics, policy, and code and say: Here’s what we can do. Here’s what we should do. And here’s what we must never do.

The most strategic role in financial services over the next decade won’t be the trader or the compliance officer—it will be the AI integrator: the leader who can translate strategy into models and models into decisions.

The Next Race Isn’t for Talent or Tools. It’s for Time.
Every institution today has access to AI tools. Most have talent. What separates leaders from laggards is cycle time—the time it takes to test, learn, validate, and deploy intelligence at scale.

This is where legacy firms are most vulnerable—not because they lack smart people, but because their operating models are allergic to experimentation. Governance is designed to prevent failure, not learn from it. Architecture is rigid. Data is siloed. Culture is cautious.

Meanwhile, the frontrunners are shortening cognitive cycles. They’re making three AI-informed decisions for every one their competitors make. Over time, that compounds. It’s not just about getting smarter. It’s about getting faster at getting smarter.

That’s the race. And it’s already underway.

Final Thoughts: The Future Is Unevenly Distributed—Intellectually
AI will not democratize finance. At least not at first. It will amplify the capabilities of those already positioned to use it well—and expose the gaps of those who are not.

The coming decade will be defined not by who has the most data or the most dollars, but by who has the ability to turn intelligence into action responsibly, repeatedly, and at scale.

The real transformation is not technical. It is institutional. And it begins with a question too few are asking:

In a world of infinite intelligence, what will your firm choose to understand better than anyone else?

The firms that answer this boldly—and build accordingly—will define the next era of financial services. The rest? They’ll spend the next decade trying to catch up to decisions that have already been made.

AI Isn’t the Story. How We Work Together Is.

Adi — Wed, 18 Jun 2025 21:35:56 +0000

That's the thing about real change: it doesn't always arrive with a bang. Sometimes it just rolls on in. Quietly. No parade. No keynote. Just this stubbornly little drift you only notice if you're looking.

This is where we are now with financial services. There is no banner headline shouting "BREAKING: INDUSTRY TRANSFORMED." But beneath the jargon and panels, something is real in terms of how we conceptualize innovation. And yes, it's kind of a big deal.

I’ve had a front-row seat to this slow-motion evolution. Between leading innovation efforts, trading notes with startups and regulators, and listening to enterprise leaders grapple with AI and agility over coffee (sometimes multiple coffees), I’ve realized: the biggest breakthroughs aren’t always technical.

They’re philosophical. Cultural. Sometimes even emotional. (Yes, emotional. Innovation is messy. Ask anyone who's tried to modernize a legacy system without weeping.)

We talk about AI like it's the headliner—and rightly so, because it's everywhere now. From onboarding processes to risk models, AI is performing more thankless tasks than ever before. And here's the shocker: the magic isn't inside the model. It's inside the environment.

Who built it? Who governed it? Who developed it right?

That's where the term "ecosystems" comes in. And don't tune out just yet—yeah, I know, cliche.But bear with me. When it's done well, an ecosystem is not a buzzword. It's actually kind of a smart way of saying, "Hey, we probably can't do this alone."

And that's pleasant. Let's try again.

The best examples? They're below the radar. JPMorgan's IndexGPT wasn't assembled by some solo genius - it was co-creation. UBS's synthetic analyst bots? Built by cross-fertilizing old-school financial know-how with OpenAI expertise. MAS in Singapore? They've perfected co-creation with industry and academia as a national sport (and we should be taking a page from them).

It's all backed up by this: openness. These projects aren't about empires-building. They're about collaborating on blueprints. Abandoning perfection. Getting the unlikely partners in the same room—and maybe even allowing them to hold the whiteboard marker.

In this new universe, the winners aren't the glitziest-teched, deepest-pocketed. They're those who can orchestrate. Less "command and control," more "conduct the symphony."

And not disorder, no. It signifies another style of leadership. One that is comfortable with complexity. One that wagers on platforms rather than fortresses. One that worries less about short-term ego scores and more about long-term resilience.

So no, this is not some puff blog post drifting around in hysteria or a warning story. It's just an observation.A reminder. That maybe the future of finance will not be so much about moonshot-ing, and so much about well-crafted collaborations that actually function.

And seriously? That does sound like progress.

Let me know if you agree? Would love to know what you think and as Innovators, Developers and Techies what appealed to you most?