👋 Welcome to this week’s edition of 5-Bit Fridays. Your weekly roundup of 5 snackable—and actionable—insights from the best operators, bringing you concrete advice on how to build and grow a product.

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Happy Friday, friends 🍻

In case you missed it this week:

• Guilty is charged. Justice is served. SBF, the weasel behind FTX, was found guilty on all seven counts. His last act before going to prison for a very long time apparently was just a scathing closing argument where he just repeatedly lied under oath.

• OpenAI, and their engineers who just btw make a median $900K/year, are also working on their own phone—the “iPhone for AI'“, as they call it. That’s big news, and definitely confirms their ambitions to create their own platform. It might seem odd to see them leaning into both consumer and enterprise, but it’s not that uncommon of a strategy at a certain scale when looking under the hood of companies like Microsoft, Google, Facebook, and Apple. I’m very curious to see this rollout.

• Leaked messages show Apple paused dedicated internal Slack channels for Muslim and Jewish employees. The pause comes after Apple deleted messages from employees related to the Israel-Hamas war.

• Google just dropped the ability to purchase a new type of domain extension. You can now grab your new .ing URL extension. Pretty cool. If you have a cool $130K laying around, you can snatch up X.ing before Papa Elon does.

  • Should you? 🤷 Who’s to say, and don’t take this as investment advice please, but with Elon’s obsessions over the letter X and his fever dream to make it a Super App…stealing one of the richest men in the worlds lunch (one who has a big ego), may not be the worst idea ever. If you do, just don’t forget me later 🤭

• Microsoft 365 AI Copilot has launched for enterprise. It’s coming in at $30/m with a 300 users minimum. Forrester thinks there will be 6.9M US workers using Copilot by 2024. We’re seeing their OpenAI investment come to fruition.

• Oh, and a tad bit of housekeeping, if you missed this week’s deep dive, be sure to check out our analysis into Cash App…perhaps the most undervalued FinTech product around.

Ok cool, onto this weeks roundup.

Here’s what we’ve got this week:

• Overchoice and how to avoid it

• The Difficulty Ratio

• What your focus should be during your first 90 days as Head of Product (including generalized advice)

• Non-obvious, but important, insights on product management

• The agony and ecstasy of building with data

p.s If you’re reading this in your email, as usual, the post gets cut short. Click here to read the full thing.

(#1) Overchoice and how to avoid it

American journalist, Hunter Stockton Thompson, says it well:

And when it comes to the minute-by-minute plays of of our lives—whether at work or at home—there’s just so much to always choose from.

A few seconds ago, you had the choice of which newsletter to read. Thankfully, you chose this one. And when writing this weeks 5-bit, I had so many great articles to pick from…I chose a great one by about five heuristics to help us make decisions.

As he points out, research shows we spend about 3 hours per day making trivial and mundane decisions. Yup, all those “What should we do for dinner tonight?” and “Wait, what should we watch after dinner?” decisions add up. If you do the math, which is sobering, that amounts to a full 40 days each year of just dithering.

And yes, that’s just the petty and easy decision. It doesn’t count actual consequential ones, like your job, choice of spouse, etc.

This brings us to the world of decision theory, where physicist Edward Fredkin proposes this:

That’s Fredkin’s paradox, and it means we waste the most time on the least important decisions.

It’s best illustrated by what’s know as Buridan’s ass:

Basically, a hungry donkey is placed precisely in the middle between two identical stacks of hay. Since the paradox assumes the donkey will always go to whichever is closer, it dies of hunger since it cannot make any rational decision between which one to eat. It wavers until starvation.

Put you hand up if you’ve been an ass…scrolling Uber Eats while starving, trying to find just what you’re looking for…🙋‍♂️

Simply, the paradox drives home the important point we often forget: the choices we make don’t need to be the best—they just have to be worth more than the time spent making them.

In Gurwinder’s post, he shares what he calls 5 “philosophical razors” that can help us made decisions in a world of endless choice.

Here they are:

Uphill Decisions:

“If you can't decide between two equally difficult choices, take the path that's more difficult/painful in the short term.”

Why?

Because we like to overvalue short-term pain/reward and undervalue long-term pain/reward. So if a decision is painful in the short term, you’re likely overestimating that pain, and should choose it over the longer term pain that only seems small because it’s far away.

The Two-Minute Rule:

“If a task will take less than two minutes, it should be done at the moment it’s defined.”

Why?

Short tasks can pile up if you just keeping kicking them down the can an occupying space on your physical and mental to-do-list. Just get it done.

Solomon's Paradox:

“If you can’t decide, pretend you’re deciding for a friend.”

Why?

Being detached from something brings objectivity and clarity. It’s the same reason we’re better and giving advice than taking our own. We’re often just better at solving other peoples problems, and Solomon’s paradox is a forcing function to change our POV.

The Hesitation Heuristic:

“If you can’t decide, the answer is no.”

We live in an age of abundance, where new options are constantly becoming available. But every option has an opportunity cost, so if you keep taking opportunities you’re not eager for, you’ll miss out on ones to which you’d unequivocally answer “Yes!”.

Regret Minimization:

“The opinion you should care about most is your future self’s.”

We all have a future self—someone who will look back and have memories around the decision we made today. This relates to the first razor (Uphill Decisions), where we prioritize the gain/reward in the long-run.

Tactically, think of the 10:10:10 strategy, where you consider how a decision will affect you in 10 minutes, 10 months, and 10 years.

…

And with these 5 razors…go into the world my friend, armed with all the philosophical tools you need to simplify the decisions in your life.

For example, I present you with the choice of whether to upgrade your HTG subscription and become a founding member. 😅 It’s less than $7 a month, you get exclusive posts (like the upcoming essay on velocity killers), access to community Chat or email where you can ask me for advice anytime, and over 80 hours of my research/writing each month. If you apply even just one lesson per year successfully, I am certain this will pay for itself many times over.

So, which razor will you use you make a decision…30 seconds. 👀

(#2) The Difficulty Ratio

There are a lot of factors that determine which SaaS products will win.

Let’s just talk about 1 today—the ratio of how much a deal is worth vs how long it takes to close the deal. AKA: Reward / Effort.

has a more memorable term for it—The Difficulty Ratio.

Logically it makes sense, and almost not worth spelling out: Larger deals can take longer to close; smaller deals must close quickly.

Yet, it is worth putting to paper because as we just covered in with “philosophical razors”, we’re often not rational.

Simply, if your product isn’t expensive and you’re closing deals with a sales motion, you must close them fast. And if you’re selling a big-tag software, it’s okay to for things to take time.

You might be wondering what a low ACV is. Luckily David has some example ratios here for us to consider as rules of thumb.

In a great post, David and answer the important question: How do we get out of the losing quadrant—the low ACV and slow to close time?

Here’s what they suggest:

Soundbite: You just can’t have a GTM where it takes you a long time to sell low-cost goods. Either you get better at sales so you can sell faster, or you hike your prices. Ideally, you do both. If you can’t do either, you need to pivot somewhere, such as distribution model/product/market/customer.

(#3) What your focus should be during your first 90 days as Head of Product

I’m a huge fan. Gibs was the VP of Product at Netflix and has a long track history of Head of Product roles at different startups.

He has a ton of experience, and I’m very glad he writes about it over at his “Ask Gibs” newsletter.

A few years ago he wrote about what senior product leadership should do in their first 3 months.

While his advice is focused towards heads of product (CPO/VPs/Directors), here’s what anyone can abstract for how yo approach those key first 90 days:

• Ensure you’re heading in the right direction and that the strategy, metrics, and projects against these strategies are clear.

• Spend lots of time with the organization's people, helping you learn the process, culture, and where decisions are made

• Focus on developing high-cadence output, and where you can, eliminate any weak links like lack of tools, systems, or resources.

Okay, let’s zoom into his more specific advice for product leaders:

Gibs has a great analogy, and he uses this line of thinking to shape a 3-point plan when he joins a team: “Think of the product organization as a cannon. Is it pointed in the right direction? Does it have the right people to load and fire it? Are there enough cannonballs and gunpowder for rapid-fire blasts?”

With that, here’s his playbook for the first 3 months:

1. Outline the product strategy

This is the perfect place to start, because remember, for any product manager strategy is the source of our leverage power.

2. Build the product team

To learn how everything works, Gibs makes sure to spend a lot of time speaking to people:

• With the CEO, he develops an understanding of the big picture: vision, thesis, strategy, goals, big moves, etc.

• With peers on the product team, he figures out how the product team fits into the overall company, and looks for where there’s misalignment (i.e marketing <> product, product <> sales, etc)

• With direct reports, he ramps up on each person’s skills and motivations. He starts building trust, and learns where his key players and weaker links are.

• In all his conversations, the goal is to understand the culture.

And after all this, he’s in a position where he knows who should be doubled down on, who needs investment to get to the right level, and who on the team just isn’t cutting it. Once he knows the latter, he drives home a hard lesson for leaders: don’t waste time firing people and building the team you need:

3. Enable high-cadence product development

In the early days, Gibs work to identify the gaiting resources, and who is on the hook to address them. Teams are only as strong as their weakest link. One tactic in particular he uses stands out to me—a great case of how as a leader to set other people up to lead by example:

That kind of method is the mark of true, hard-won, leadership experience. Love it.

(#4) Non-obvious, but important, insights on product management

Dan Hill, former Head of Growth at Brex and Director of Product at Airbnb, has spent many years building products. In his essay, “Observations on Product Management”, he took the time to consolidate some of his biggest lessons. He dubs them “mostly things that are either obvious but no-one ever says them, or non-obvious but important.”

Had me hooked with that line.

So, here are Dan’s 12 observations:

Excellent observations from a long career in the trenches. And that last one on data is a nice and convenient segue to the final bit this week. 👇

(#5) The agony and ecstasy of building with data

I saw this headline by and it literally screamed out at me. Just recently my team and I have been going through some issues around shipping features with A/B tests and making data-driven decisions.

I could not second this point Julie makes more: Data and A/B testing are amazing tools, and that they have “the power to wrap you in the warm comfort of understanding, soothe philosophical disagreements, and make the hard decisions easier.” But…but…they are not cheat codes. And it’s easy to over use them, where we call enthusiastically and proudly for “Just test it!” or “Well, look at this metric!”. While we might think that means we’re being analytical and data-driven (yes, essential PM traits), it often is just a crutch for lack of conviction and understanding.

Can’t decide how you truly feel about the future? Just have data and experimentation take the decision out of your hands. 🤷‍♂️

As Julie says:

So, as good and useful as they are are tools, there are a few common pitfalls to watch out for. Julie wrote about all of them in this post, but given some of the pain I’ve been working through on the testing front, here’s a PSA on A/B testing.

Pitfall #1: Spending too long perfecting tests 🚩

In general, the goal of an A/B test is to narrow down a long list of ideas into a few key levers to get signal on which directions are exciting to pursue.

Tests should be directional, and show help you validate hypotheses and learn quickly. If you’re spending forever figuring out your test and getting it ready, it’s not worth it. Don’t kill your velocity by over polishing tests.

Pitfall #2: Shipping successful tests right away 🚩

If you’re doing the above correctly and testing quickly, be mindful that you shouldn’t just ship something fully that has an immediately positive result.

A good test outcome doesn’t mean it’s ready to go.

Pitfall #3: Running too many tests on details that don’t matter 🚩

Yes, it actually is. It’s bang-your-head-against-the-wall annoying.

If you’re testing stuff where literal fractional % point increases are a win, you’re probably doing soul-sucking work. Unless you’re in the FAANG world where those bumps do matter, you could easily do that for years and just be treading water around local maximums.

Instead, spend time figuring out which ideas are going to give you the step function improvements you’re hoping for. Build some level of conviction around them (assumption testing), and then go A/B test it.

You might be thinking, but what’s the harm? At minimum, we learn something. True, but is it worth it?

Amen, amen, amen. That’s all just spot on.

Pitfall #4: Relying on A/B tests to do anything innovative or large or multi-faceted 🚩

…

Soundbite: You need data and A/B testing in your arsenal, and you should be testing things regularly and bringing data into your decisions, but they are still not replacements for clear-headed and strong decision making that brings intuition and good-ol’ product sense to the table. Don’t be lazy and rely on pure numbers, and make sure you’re able to explain your ideas without needing stats as a crutch.

🌱 And now, byte on this 🧠

AI is weird. No one actually knows the full range of capabilities of the most advanced Large Language Models, like GPT-4. No one really knows the best ways to use them, or the conditions under which they fail. There is no instruction manual. On some tasks AI is immensely powerful, and on others it fails completely or subtly. And, unless you use AI a lot, you won’t know which is which.

The result is what we call the “Jagged Frontier” of AI. Imagine a fortress wall, with some towers and battlements jutting out into the countryside, while others fold back towards the center of the castle. That wall is the capability of AI, and the further from the center, the harder the task. Everything inside the wall can be done by the AI, everything outside is hard for the AI to do. The problem is ….

Read: Centaurs and Cyborgs on the Jagged Frontier

by

And that’s everything for this week, folks.

If you learned anything new, the best way to support me and this newsletter is to give this post a like or share. Or, if you really want to go the extra mile, I’d be incredibly grateful if you considered upgrading. The first 500 folks who become founding subscribers get 30% off this newsletter ($7/m, or $70/year), as well as any other products I sell, for life.

Thanks so much for reading. I hope you have an awesome weekend.

Until next time.— Jaryd✌️

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