A finance bro and a math guy walk into a bar. Well, sorta. The two of us are an unlikely duo to run a marketing agency: Hamza was a math author and Andrew was deep in the weeds of private markets. So, naturally, the question comes up all the time - how and why are you guys doing what you’re doing? Off rip, math and finance are the furthest things away from marketing. But peel away at the layers, the Venn diagram has more surface-area than what meets the eye.

Why? Let us explain (in a very finance analogy). In the old days of Wall Street, relationships were key: alpha was earned through phone calls, negotiation prowess, and Le Bernardin dinners. This changed slowly - then all at once.

Starting in the early 2000s, financial networks stopped being run by people. Computers started replacing more and more work. By late 2007, Dan Spivey, backed by Barksdale (Netscape) and a $300mm war chest, started laying the groundwork of what would ultimately become Spread Networks. Reductively, the company was nothing more than a fiber optic cable from the Chicago Mercantile Exchange to the NASDAQ servers in Carteret, New Jersey. Companies had been laying subterranean cables since the late 19th century. But, this cable was far from ordinary. Aside from the cost, the 827-mile route was to be covered in a near dead straight line and full secrecy.

From the surface, this was a really dumb idea and a great way to light money on fire. But Spread Networks did one thing really well; they undercut the speed of existing networks that traders used to front-run pricing delta between CME futures and the underlying equites at the NASDAQ. Michael Lewis goes deep on Spread Networks and the murky world of electronic/quant/high frequency trading in Flash Boys. If you’re too lazy to read, you can watch Jesse Eisenberg explain all of the above in The Hummingbird Project. And, if you’re too lazy to watch, the major takeaway in Lewis’s exposé is the following:

“People no longer are responsible for what happens in the market, because computers make all the decisions. And in the beginning God created the heaven and the earth.” ― Michael Lewis, Flash Boys

Fast forward to today. As the era of active management sunsets and technology + liquidity prices in anomalies, your best bet is to go with the market. I’m sorry to say that 9 times of out of 10, you’re not going to beat the Carnegie Melon obsessive quant armed with Adderall, Asperger’s, and a much longer stick up their ass than yours. Just take Buffett’s advice: buy SPY and call it a day.

Ironically, Wall Street is a leading indicator to how shit gets built in Silicon Valley. Likely because the street is openly greedy, not the closeted greed of the peninsula as AGM points out:

Wall Street was merely the first inkling. The next place where this shift would be seen at whopping scale in terms of both money and technology (though I didn’t realize at the time) was in Internet advertising... As I would eventually see, Wall Street and Silicon Valley possess surprising parallels” - Antonio Garcia Martinez, Chaos Monkeys

Now back to 2007, the year Spread Networks started being built. At the same time, down to the month, another network was being laid, this time across the coast in Palo Alto. See, when Facebook first allowed ads on the platform in November ’07, it was adding to a seismic shift in how companies pay for a scintilla of attention in someone’s mind, which, after a few more clicks and electrons shuffling about, turns back into money. Just like The Street, the way ad-inventory was traded slowly transitioned to the whims of black boxes locked behind supercomputers.

Every time you go to Facebook or ESPN.com or wherever, you’re unleashing a mad scramble of money, data, and pixels that involves undersea fiber-optic cables, the world’s best database technologies, and everything that is known about you by greedy strangers. Every. Single. Time. - AGM, CM

These little boxes of computation, colloquially referred to as “The Algorithm,” remain a mystery (watch Amy Klobuchar try to figure it out). The reality is, it’s a black box and nobody has it figured out -- not even the engineers that build it. It’s a fool’s errand because it’s not real. The algorithm doesn’t exist.

No, really. We always get the question: “Have you cracked the algorithm?” What algorithm?

It’s basically all machine learning: matrices multiplied by other matrices to maximize some event. A math optimization problem at play. And each problem (i.e. campaign, day, cost for XYZ, etc.) is a different problem. Of course, this is very reductive, but what we’re getting at is there’s no formula or series of reproducible steps, so calling it an “algorithm” is misguided at best. If someone tells you they cracked the algorithm, run before you get scammed. As McConaughey put it, “It's all a fugazzi.”

For our friends at Facebook it wasn’t all sunshine and roses. Looking back at Facebook’s history, their Ad-algo had a rocky start: Facebook ads underperformed by as much as twenty times the worst targeted ads around the inception - another parallel to the early days of electronic trading on Wall Street (read Flash Crash).

The problem was handed off to Joaquin Quiñonero. Inspired by the Arab Spring, he joined Facebook and left his old role at Microsoft. Quiñonero began working on the ads and built up machine learning systems that improved their guesses over time. These algorithms would spot correlations between what users clicked on and their attributes (gender, age, location, content interests, etc), then use that information to guess which future ads they should be served. This algorithm began with near random guesses, but from its hits and misses, it kept refining.

This “algorithm” was, and still is, hardly great. Recommendations are still inexplicable at times. But given how cheap advertising was (and still relatively is), the hit rate doesn't need to be that high. A 2% click through rate is amazing for most advertisers. Quiñonero and his team would end up shipping updates at an unparalleled speed. Even a slight improvement would print money for Facebook. Facebook went from having a terrible algorithm to now having something that impresses even the most critical marketers. It can do a lot with very little and everyone wanted a piece of the action. So FB Learner was created. Basically a Wordpress for ML recommender algos. Instead of deliberately designing systems, engineers threw stuff at the wall and ML would figure it out. TL;DR, the “Algorithm(s)” we speak of.

So how do these algorithms work? There are some basic things that we can establish. They're at core—a market where ad inventory is the asset being traded. That’s why ad costs rise in certain times (spend is higher). You’re competing with other advertisers to reach the same eyeballs. Now the algo’s job is (very roughly speaking) to match eyeballs with ads to make the most money. If FB can make their advertisers money (or whatever their objective is), then they spend more and FB makes more. There is an optimization problem at play here. With limited eyeballs, how can you make the most money? That's what the algorithm solves for.

So, is there a way to “game” this algorithm? Hack it? The answer is as boring as Warren Buffet's portfolio strategy. You need to target broadly. 95% of the time, that's going to work better than trying to outsmart the algorithm. Just like Wall Street, there’s no longer alpha in complex targeting and strategies. You're competing for an ever-thinner edge. As Buffet tells you—simply buy the S&P and hold it. This is akin to letting the algorithm target broadly.

Why does this work? As mentioned, ad networks are like markets. That means everything is priced in given market efficiency. Your audience’s quality (let’s say their propensity to pay or convert in some capacity) is already priced in. So, trying to price it in yourself (targeting narrowly) won’t work and the algo will make sure to punish you. We oversee close to 100mm of spend annually. The bigger the client, the more they know simple=better.

Not only are you going to guess your target audience wrong by manually targeting (you're not capturing all the nuances), but you'll be punished for doing so by the algorithm through high CPMs. The algo has an opportunity cost function built in: if you make shitty content that gets users to scroll and exit the app, the paid algo will spike your CPM. It's an unforgiving machine aimed at maximizing every ad dollar.

When you target narrowly, you’re obviously not doing a better job than the algo. Just as the markets have an information advantage, so does the algo -- shuffling through millions of data points on each of their users. By targeting narrowly, you’re getting less than ideal users but the algorithm is forced to serve them. And the more aggressively narrow the targeting, the more CPMs go through the roof. The algorithm knows users will churn if shown the same ad with high frequency. Again, the algorithm is predictive and the opportunity cost is clear. Why show an ad to someone that'll cause them to leave the platform, meaning less overall ad dollars?

A lot of people ask us how these algorithms are so good. If you optimize for an install vs a purchase, you notice a 2x, maybe even 3x lift in purchases with 100 events in a day. How can it target so well with so little data? Algorithms don’t work on a user level anymore—they rely heavily on “collaborative filtering.” This is (partially) why your algorithm is so good. As unique as we think we all are—other users heavily inform your own algo/ad targeting. These inter-user relations are not captured in things like hashtag or topic interests because they are incredibly nuanced/unintelligible to a human. They are just matrices with numbers. This makes the algorithm even more inexplicable. No one can answer exactly why one ad appeared in your feed instead of another—it’s kind of the culmination of every other user and ad interaction. Obviously not, but re: butterfly effect for more.

You’re probably wondering, if what we’re saying is true (it is), how do you win? The key to winning is really simple:

● great data
● great creative
● great product(duh)

Simply focus on feeding the algorithm all the right signals at scale and none of the bad ones. No, like really avoid sending the bad ones back. For example, we had a fintech client who was feeding bank-add events back to TikTok. The only issue is—they were feeding back unsuccessful bank-add events as well. Cases where the login failed, cases where the user literally had a negative balance, etc. The algorithm (thinking it's maximizing the event) kept sending these low-quality users. Of course, they didn't get much out of their marketing.

The key is finding high signal, high frequency events so the algorithm can do what it does best: optimize. If you feed it shit, it gives you shit back. If you feed it gold... well, you get our point. Make sure your event infrastructure is built well.

The second focus is great creative. No algorithm can make your shitty creative work, sorry. There's no way around it. Your creative needs to be unforgivingly targeted to your main audience. Don't try trends. They don't work. Just make a good, engaging ad that your core audience can identify with. We do this at scale at NewForm. But you can try it too if you want. Just make good creative, pls.

PS: you gotta have a good product. Something people want. Duh. But a lot of people gloss over this step.

If you made it this far, we should chat. Email us at andrew@newform.ai / hamza@newform.ai or feel free to grab some time from our calendar here.

In 1970, Chicago economist, Eugene Fama, argued that information flows in public markets make it impossible to build a portfolio that consistently outperforms - information is “priced in.” The book Efficient Capital Markets went on to win Fama a Nobel prize and built the foundation of the Efficient Market Hypothesis (EMH). Most finance undergrads learn the legend of Fama on day one of Asset Pricing 101. However, on day one of Behavioral Finance 101, students are met with a direct criticism of Fama as Shiller spares no expense in tearing away at the EMH. Many (if they’re anything like myself) are left scratching their head questioning everything - Are markets priced in? Do investors actually act rationally? This debate over the EMH is best reserved for the sidewalks surrounding Harper Center and Wall Street, not Sand Hill Road and not the focus of this essay.

This being said, the criticisms that Fama saw in his work in the fields of finance and economics should also be applied to the nascent decalogue in start-up land. For most founders, Blitzscaling to capture and dominate the market in a globalized world is the bedrock that ideas are formed on, capital is raised on, and runway is burned on.

VCs have followed a framework that mirrored the thesis Fama attached to the markets: freer flows of information, transparency in pricing, and globalization don’t allow for easy alpha. Markets are efficient and rational, so you better start burning those dollars to dominate your TAM and make the world a better place. Following this framework for the better part of the last decade, venture backed bets have been the only noble pursuits in hallowed grounds of SoHo House happy hours and SXSW coffee-chats. And, from the buyside, the rapid scale playbook computes well. The asymmetry of venture portfolios supports power law incentives. From an investor’s perspective, it’s beyond necessary to have each PortCo go for the 10x bet. If 1 in 10 account for all the returns, you can’t afford a knock on IRR if your only winners are medium X’ers. This incentive structure means a natural push for founders to grow, grow, grow, at all costs. And, for the most part, founders have subscribed to the SV model of raise (dilute), spend, grow, repeat. There are a select few companies where this is the only way to capitalize: those that need immense network effects (Snap, Meta, Uber, etc) or technical intensity (SpaceX, Waymo) to back into immense asymmetry from a risk reward matrix.

But what about those medium X’ers? What about the the N’th SaaS or consumer utility app? Historically, the technical intensity of these products justified (to a degree) frothy rounds. But with rapidly decreasing CAPEX costs and abundance of cheap tech leverage, it’s clear more can be done with less - so why isn’t that reflected in how startups are capitalized?

In the current environment, the consensus is a lot of companies are walking dead. Many will say these companies failed to execute, find product market fit, or their founder was distracted shitposting on Twitter. But I’d argue that many of these failures are failures of balance sheet and cap table incentives. As Alex Danco put it, “The greatest trick VCs ever pulled was convincing founders, ‘you’re just like us.’” For the past decade of free money and good times rolling - the cycle supported deeply capitalized bets both financially through constant up-rounds and culturally through founder friendly tweetstorms. But what happens when rate driven austerity measures dry up capital and support for founders reaching for the stars? My mentor and dear friend, Alec Andronikov, said it best in his post My Biggest F-up of 2017: Chasing Shiny Innovation:

You have to remember: the vast majority of VCs are NOT in the game of innovation. They care about one thing and one thing only: maximizing their IRR in the shortest time possible by getting the highest valuation possible… They do NOT care about your personal struggles of making really crappy income while you are building value for them.

For the wide-eyed quixotic founder off a 2022 Series A extension, the take is crass and jaded. But, in a world where discount rates are nonzero, the Overton window shifts into making money and away from effective altruist shibboleths.

Maybe it’s the perspective from watching hundreds of companies spin tires and close rounds from less-than-ideal partners at sub-par terms that triggers my fight or flight. But does this mean that founders should pack up, discouraged and running with tails between their legs? No. It just means there’s a new game to play (and one that’s arguably more exciting). One that I’m playing: the Silicon Valley Small Business (SVSB). SVSBs pull the best of both worlds from SMBs and classic startups. From the SV side, retaining frameworks of infinite leverage and growth hacky creativity and from the SBs, day one profitability and rational spending habits (no, we don’t have Kombucha on tap).

But for those of you that are venture backed, the lessons learned from SMBs can still be applied without sacrificing growth (and no, this doesn’t mean you have to start using Teams and move your Chelsea office to Ding Dong Texas).

What I learned from working with small-town HVAC companies and widget manufacturers, is the intense bottom up evaluation of each cost-center. The constant conversation of “why are we paying Joe the PVC pipe guy that?” “let’s tell Xfinity we’re moving to CenturyLink” etc. However, the SV of the SB here is SMBs will sacrifice growth for profitability, failing to put dollars on testing and innovation. Why? Not only because growth is physically localized, but because those are balance sheet dollars. Meaning, if you’re operating at 20% EBITDA, you pad the balance sheet to prep for the next inevitable Silverado and technician.

But, by the miracle of $0 marginal cost with software, net profit margins climb to comfortable ranges where you can then predictably set aside exploratory dollars on channel testing → substituting CAPEX for CAC, setting B/E hurdles, and timelines for those exploratory dollars to become profitable or call it quits.

This structure is the inevitable future of tech. Small teams (sometimes individuals such as @Levelsio) who are aggressively throwing projects to users, iterating quickly, pulling the plug on losers, and running it back.

Will there be a place for venture dollars? Sure. But it won’t (and shouldn’t) be like the free ZIRP dollars of the last decade. This might be obvious for some, but for most there’s a thing or two to learn from the mom & pop SMB in the Deep South or Midwest.

My shameless plug here is this unit economic focused building is central what we’re helping companies solve here for at NewForm; we’re rapidly opening up our clients to growth frontiers with profitability on an agency level. Where, instead of thinking of the next VC coffee chat, founders will be able to think about recycling profitable dollars back into growth - preserving dilution, control, and (hopefully) sanity over the lifecycle of their business.

And no, we’re not doing this because we’re nice generous people. It’s because when shit hits the fan and our clients realize we’re making them real money, we’ll be just fine, and they’ll keep helping us help them make the world a better place.

Want to chat? Feel free to grab some time here.

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