Category Archives: Economics

AI, Economics, Finance, Fintech, Payments

Digital Payments are Going to REALLY Grow

Leave a comment

The payments market is going to massively expand over the next decade because:

1. ANYONE can now build anything digital — AI code creation means exponentially more digital SKUs that can be created and, of course, paid for. We are in the very early innings here. The gating item is just human creativity. It’s not just software. Can you whistle or come up with a tune? Then you can compose music (no need to learn to read music or know music theory). Can you think of an idea for a movie? You can just…create one. Etc.

Combined with:

2. Almost anything that was “payroll” (paying PEOPLE) can now be “payments” (paying for THINGS). For example: “Hiring an assistant” or “hiring a paralegal” (both payroll) -> paying for a SKU.

We don’t think of ADP or Paychex as payments companies because they aren’t; they are payroll companies. Paying people != paying things.

But more tasks/outputs that were once only available through “paying for people” now become available for purchase on a credit or debit card. This is already starting to happen and accelerate.

And of course, this is not zero sum! Much of this is “everything to the right” of the supply-demand equilibrium point, where there’s conceptually high quantity demanded at a very low price where there’s heretofore no (human) labor supplied. Lots of people will want to purchase a SKU who were unable to hire a person historically.

Bureaucracy, Economics, Fundraising, Sales, Startups, Strategy, Uncategorized

Cheat Code: Try to Pay More

Leave a comment

When I was running my little “shareware” business in college, I hired my first PR firm. Press really moved the needle for us (credibility and reach), and I wanted more. This PR firm had some very big name clients and lots of connectivity to the journalists and publications we cared about.

There was a monthly retainer, something like $10,000, and I fought hard to negotiate it down to something like $5,000. Almost immediately I was disappointed. I was getting almost nothing from them.

But of course I wasn’t. The firm only had so many favors they could call in. Should they use them on their biggest customer, or their smallest one? I was their smallest one.

I had an epiphany: Don’t negotiate down. Negotiate up. Try to be the highest paying customer.

I fired them, met with this Boston firm named fama PR, told them I wanted to be their highest paying client, and asked them point blank what that would take. I was a college kid and they probably thought this was funny, but we worked out a plan by which I’d pay them $40-$60K+/month (in 2003!) for certain performance.

If I remember correctly, we had different tiers: get us on The Today Show and that’s $10K, front page of USA Today/NYT/WSJ also $10K, lesser tier $5K, etc.

We launched this product called DidTheyReadIt in May 2004, and it was on the front page of USA Today, and then Carl Quintanilla came out to interview me for The Today Show. And many more. I still have the PR book they built of all of the appearances. It was insane.

Mission accomplished: biggest client.

The moral of the story is you get what you pay for. There are related learnings, too. The principal-agent problem is real. Shared services with no currency are hard. Let’s dive into those.

This played out many years later when hiring tech recruiters who typically take a percentage of first year salary (of the placed employee). They might take 15-30% depending on the market.

Remember what a tech recruiter does. They often find a really good candidate and peddle him/her to every company to maximize the chance of earning their fee. (In many cases, they’ll send cold emails about this — “I have 4 amazing candidates!”).

At TrialPay we once lost a REALLY good candidate and learned that our recruiter (who sent us the candidate!) was ACTIVELY selling him to reject our HIGHER offer and instead take an offer from another company! What the hell? My team was so pissed.

But of course this happened. We had smartly (and stupidly) negotiated the fee down. Let’s say we offered the engineer $150K, the other company offered the engineer $140K, and you’re the recruiter — would you rather get 30% of $140K, or 15% of $150K?

Was this unethical of the recruiter? Yes. Is this how the world works? Also yes.

You get what you pay for. The world is a competition and you are better off maximizing outputs versus minimizing inputs.

Economics, Finance, Fintech, Lending, Uncategorized

First Principles on Lending…

Leave a comment

Original Posted: https://x.com/arampell/status/1893883095646093315?s=20

From first principles: If you ask me to loan you $100, and I think there’s a 50% chance you don’t pay me back, I should only make the loan if I get $200 back. Otherwise, I shouldn’t make the loan! And you won’t get the loan.

The A in APR is Annual, so even if I think there’s only a 10% chance you don’t pay me back, and the loan is a week long, the APR will be enormous on a percentage basis, but only $11.11 on a dollar basis (.9 [probability] X Repayment = $100, so Repayment = $111.11)

That’s a nominal APR of 577% (or a compounded rate of 23,900%).Should that be “illegal”? If you want to restrict access to credit, then yes. I think most people would say that being able to loan their friend $100 to get back $111.11 the next week when their friend is only 90% reliable…should be perfectly fine…particularly when both parties opt in.

These headlines always miss the fact that most Americans don’t have good access to credit and more competition is the best way of lowering costs, not forcing banks to make money-losing loans (that doesn’t work!) or making it hard to start new companies to compete (the CFPB enjoyed doing that)

AI, Economics, Finance, Startups, Tweetstorms

New Essay: The Transmutation of Capital into Labor

Leave a comment

Originally posted as a Twitter thread on August 22, 2024

New Essay: The Transmutation of Capital into Labor

https://a16z.com/ai-turns-capital-to-labor/

The first era of software took analog files, digitized them, and made them accessible with a specialized interface. Think PeopleSoft for HR files, Quickbooks for ledgers, Epic/Cerner for health…

This has played out for 50+ years as more industries have moved to running on software, not files. Cloud lowered the adoption barrier. Adding financial services to cloud made more markets “big enough” for specialized companies (e.g., Toast, ServiceTitan) to exist.

But the same humans that acted on the analog files now act on the digital files! And sometimes it’s impossible to align hiring and training (of those humans) with business needs.

This is what’s exciting about AI. It’s not filling software budget. It’s filling “labor” budget.

Wages in the US alone are $10T+ per year. The worldwide software market is a few hundred billion dollars.

The original “digital filing cabinet” winners have a tremendous amount of potential to add AI, but also have a daunting task of shifting from “per seat” pricing to “per outcome” pricing. Zendesk monetizes per seat. What if a business needs 95% fewer seats because of AI?

Some of the biggest startup outcomes will likely be “net new” industries where a business runs on nothing but Excel…because the software budget was small, the human budget large, and the ability to hire humans was so hard…think compliance officers at a bank.

There’s a saying in economics: “the cure for high prices, is high prices.” As the price goes up, more widgets get manufactured, which increases supply, which lowers the price.
But when it comes to humans and wages, there’s too much latency because of training, licensing, etc.

AI will largely augment employment, and fix many of the “market failures” present with highly skilled yet episodic labor. Imagine: I need your skill for 3 days a year (peak demand), but you need to go to school for 3 years to earn it.

AI, Economics, Startups, Strategy, Tweetstorms

Outcome Based Pricing

Leave a comment

Originally posted as a Twitter thread on June 21, 2024

Can’t wait to see the first “incumbent” (in a large software field…like support, CRM, HR, etc) switch from “per-seat” pricing to **per-outcome** pricing.

I’m writing an essay on this now, but consider Zendesk at $115/seat per month…or ~$1.4M/year for 1000 agents:

Let’s say an agent is paid all-in $75,000/year and answers 2000 tickets per year.

This makes the human cost of a ticket $37.50, and the software cost $.69.

The human cost obviously massively outstrips the software cost…and unlike software licenses, it can take months to “install” (find, hire, train) a human to occupy that seat. And in many areas there is simply a dearth of qualified humans given licensing latency

In other words, you can’t simply lift wages and produce more workers…if it’s a role that requires licensing or sufficient training (think mortgage brokers, nurses, etc)

Not to mention the fact that it’s hard (and cruel!) to “flex” humans. Southwest Airlines can’t hire tons of humans when bad weather threatens to cancel flights and then fire tons of humans when weather is clear. But software is perfect for this

So: given the rate of improvement in AI for asynchronous support — what will it take for Zendesk to switch from (in the prior example) $115 per seat per month to, say, $10 per successful ticket answered BY Zendesk? Still much cheaper, more flexible, instant provisioning

It’s obviously going to happen, but how should they price this — it’s the ultimate example of value-based pricing? How to have this interact with existing “seats”? How to have teams not feel threatened by their new AI colleagues filling “seats”?

Whole industries will change, and new ones will be created now that software can produce the outcome vs simply be the tool.

Salesforce charges per-seat pricing for salespeople…why not charge per sale?
Maybe Workday can charge for HR “resolutions”
Etc

Banks, Economics, Finance, Regulation, Tweetstorms

Banks and Fear

Leave a comment

Originally posted as a Twitter thread on March 11, 2023

We no longer live in the “It’s a Wonderful Life” bank era. Fear can spread at the speed of WhatsApp and iMessage and Twitter, and electronic transfers can instantaneously render a bank insolvent.

Branches and branch-centric thinking are anachronisms.

At the same time, banks in 2023 do MUCH MORE than just lend and deposit money. They provide pipes and technology for *everything.* Payments are mostly electronic, not cash. Payroll goes to a payroll company which…has its own bank.

The Great Depression rendered a whole generation skeptical of banks. Money under mattresses was a thing. But that’s before commerce was entirely electronic. Most people can’t live life “cash under a mattress” even if they try. Lots of places won’t even accept cash!

Image

Image

And if you just say, ok, I’ll diversify banks at $250K max cap…what do you do if your business has a $1M payroll run to make and you use ADP/Paychex/etc. Which bank do THEY use? Or: How do you buy something like a >$250K house where the money “sits” somewhere in escrow?

Do we really want to concentrate all US deposits in 4 big banks? They can’t withstand a 50% instant withdrawal event, either. Or concentrate OUT of banks and into short-term t-bills?

2023 is not 1933

AI, Economics, Finance, Strategy, Tweetstorms

The “Finance” Opportunity of AI

Leave a comment

Originally posted as a Twitter thread on January 27, 2023

What is the “Finance” and “Financial Opportunity” of AI?

If “Bit Manipulation” is a key part of your COGS or SG&A, there’s a huge opportunity or huge disruption coming your way (or a PE firm that might just buy you).

Two sections follow: “Known Knowns” and “Known Unknowns”

Known Knowns: There are companies already doing X, and thus there are two opportunities:
-sell a tool to turn “bit-manipulation-by-people” costs -> GPU usage (AI base marginal cost)
-create a vertically integrated company that competes with a legacy player…by doing the above

Financial services (unlike, say, Campbell Soup or Boeing or Fedex) are primarily “bit manipulation” — little atom moving needed!

How do you apply for a mortgage? Insurance? Reinsurance?

A lot of the cost is…movement of bits. Move info from here to there, validate X, etc.

Companies, and people within companies, tend to be extraordinarily slow routers of information. Person X emails Y, who’s on vacation…who upon return asks for more info, and then passes it to Z, etc. Do it more quickly, save money and win share.

There’s a tremendous private equity opportunity here, which is the “finance” opp. Any company might see a *dramatic* difference in bottom line once more of these bit-manipulation functions are automated. It’s like going from seamstress -> loom -> textile factory…for bits.

Next: Known Unknowns. What I’m fascinated with are companies that cannot/do not exist today due to a market failure between what companies/consumers will pay and what people will work for…in the realm of bit manipulation.

For example: “Find all counterfeit listings of my product on Reddit/FB/Twitter/forums, for $100K/year” or “Reach out to unhappy customers and get more information, for $100K year”
There’s probably lots of demand at a given price but impossible to provide service at that price

So there are no “market comparables” or set of companies to look to. It’s just an old fashioned supply/demand curve where there’s no quantity demanded at the price where labor is willing to supply…

Working on an essay on this with some data from existing companies — more to come soon.

Economics, Fintech, Startups, Tweetstorms

Maturity Matters

Leave a comment

Originally posted as a Twitter thread on October 25, 2022

If you had bought the May 2020 30 Year T-Bill (1.25% Coupon) at auction, you’d currently be holding something worth LESS THAN $.50 on the dollar. The Aug ‘22 issue is trading at $.77!

If you are investing your cash, no matter how safe the instrument, MATURITY MATTERS.

I know Fintwit knows this. But if you are, say, an unprofitable startup investing your cash, *do not invest in long-maturity products* — it doesn’t matter how safe they are. Holding to maturity is not the benefit it seems. And the problems are magnified with illiquidity.

Customer Acquisition, Economics, Fintech, Startups, Strategy, Tweetstorms

Stochastic Gross Margins of Financial Services

Leave a comment

Originally posted as a Twitter thread on November 03, 2021

Many areas of financial services have “stochastic margins” per widget, but hopefully (obviously!) positive margins for the whole batch of widgets sold – unlike most manufacturers, with fixed/declining COGS at scale. This means many things when you build a “financial” business

You might make or lose money on the marginal loan, marginal insurance policy, marginal payment processed, marginal market-making trade. Apple makes the same margin on every iPhone 13 Pro it sells.

In no particular order, here are some things to think about:

Understanding adverse selection v positive selection is crucial. And the “default” (when you are providing pure MONEY) is being *overwhelmed* with adverse selection before the positive selection customers even show up. Bad loans, risky insurance, tainted properties, etc.

ONE FORM of adverse v positive selection is pull v push. In most businesses organic consumer adoption is a godsend. In risk, it is not! Compare people searching for “I need a loan” to people who get “pushed” a loan offer based on their highly desirable, prescreened credit

This is one of many reasons why postal mail works so well (surprisingly!) for lenders. It’s not just the saturation of the channel – it’s push v pull, picking your customers vs trying to pick through the sea of (possibly) adverse selection applicants

So tracking the **channel** against the cohort performance is *crucial* to understand which channels tend to have more of this adverse dynamic, even holding things like credit score or underwriting risk constant. Cohort customers by time AND channel (and other behaviors)

Be highly, highly tuned to “anomalously high” conversion rates. It might mean that you found a great channel, or it might mean you found a motherlode of desperate/bad/fake customers

A life insurance executive once told me that they found that post-midnight advertising on the History Channel was their most cost effective channel, but turned out to be netting very bad, depressed customers — which didn’t show up in medical underwriting but tracked to channel

Another form of adverse selection happens when you are buying/underwriting a subset of financial products without seeing the full set. Think lenders who sell SOME of their loans. The offered products are ipso facto riskier or worse than the retained ones.

The tail REALLY matters – cohorts need to season. If you are selling life insurance, you don’t know if you’re good or bad at underwriting until (sadly) people die. If you’re buying homes…until the VERY last homes sell.

A mistake I see many companies make is they record their early realized gains, and either assume that will continue or (equally bad) hold everything else in the tail at cost. The last trades are almost always the worst — that’s why it took so long to unload them

Promotions are their own form of adverse selection in “deal seekers.” PayPal once ran a giant promo (trying to launch their offline biz) with HomeDepot, and saw massive customer adoption – but all from SlickDeals deal seekers who saw opportunity for profit

so:
-let cohorts season before assuming anything
-understand channels
-always watch for adverse selection
-be vigilant watching for anomalously HIGH conversion rates
-push can outperform pull
-beware underwriting “subsets” of a customer’s business
-beware deal-seeking

Economics, Finance, Fintech, Real Estate, Strategy, Tweetstorms

iBuying and Marketplaces

Leave a comment

Originally posted as a Twitter thread on November 02, 2021

few thoughts on iBuying in light of ZG news:
Amazon started off stocking every book it sold, but the vast majority of revenue is now 3P marketplace/FBA (Fulfilled by Amazon). Once AMZN aggregated consumer demand, it started aggregating other sellers and charging commissions

So iBuying is not simply “let’s take lots of principal risk by playing market maker.” Opendoor is aggregating a lot of inventory, which in turn aggregates consumer demand (direct to OD), which then would allow OD to aggregate 3P supply since supply follows consumer demand

the only way to do this is to buy the homes since, as a principal, Opendoor can choose to withhold from MLS and simply list direct. An agent representing a homeowner could try this but…there’s no strategic value to owner in risking lower price for “strategic value to company”

next: cohort math. The real embarrassment to ZG is that their misfire on this business impugns the accuracy of their apparently not very accurate “Zestimate.” But the reason for the misfire, IMHO, is about how cohorts work and age.

let’s say I buy 1000 homes this month for $300M. Avg price $300K. Between commissions, fixes, cost of capital, etc I might be shooting for 50bps profit at the end. But the last 10 homes to sell will make or break me. Why?

by virtue of the fact that they are my LAST 10 to sell, something must be wrong with them. Termites, ghosts, etc. I might need to discount them by 50% to sell them. But that 50% principal impairment wipes out my WHOLE cohort profit/is not realized until the END of cohort!

so basically:
-there is a lot of strategic value to aggregating supply -> aggregating demand to build a marketplace in the biggest asset class in the world. It’s not dumb to try.
-it is very, very hard to get it working, particularly since it will look good until the very end