IanJoshua.com

The /Goal

Sun, 28 Jun 2026 00:00:00 GMT

While Jensen Huang is talking about token factories and Steve Yegge is building Gas City to let anyone build their own dark factory with a focus on maximizing token output, more often I am going back to a foundational text for understanding how factories work: The Goal, by Eliyahu Goldratt. The important system when modernizing any existing workflow is not just the token factory - it is the end-to-end workflows for creating value and driving business impact. For everything from manufacturing a widget to building new product features or creating new content, opportunities enabled by AI do not remove the need to think from first principles.

The Goal is structured around the Theory of Constraints. A key learning from this theory is that a system’s output is governed by one bottleneck at a time. On the factory floor a bottleneck might be obvious - waiting on a key input, one machine running at full capacity while others sit idle, or completed work piling up waiting to be delivered to customers. The same can be true in digital product development, if you know where to look. Jira tickets piling up for a specific team or experiment results waiting to be actioned on are common examples - but the calendar of a key approver, or the training cycle for sales and customer support teams might be the real indicator of the bottleneck. Just as often the bottleneck is not being measured - a deep product backlog without high confidence prioritization might be a sign UX research and talking to customers is the real bottleneck, or lack of adoption of a new feature might be a result of bottlenecks in go-to-market activities. Goldratt’s line is that people behave the way they’re measured, so the constraint often hides somewhere not being tracked.

The Theory of Constraints tells us that if we optimize any aspect of our factory that is not the bottleneck, we are not actually driving any business impact. So step one when identifying AI acceleration opportunities for any organization or system has to be identifying where the bottleneck is - or where it will be. Goldratt’s lesson is that relieving your current constraint relocates it. The trap is worse than wasted effort. Flood the org with code when it isn’t your bottleneck, and you’ve just created more unnecessary work-in-progress. Backlogs will grow elsewhere, like review, integration, and go-to-market. You haven’t sped up value creation, you’ve just piled up inventory that isn’t getting to customers any faster.

I too often see AI-pilled, motivated, and talented professionals skip this key step. They are used to living in a world where engineers - and code output - are the limited resource. Fighting for engineering headcount has made them assume this is where the constraint will always be. However, if you believe in the benefits of AI for software development (and I do - my GitHub account has seen more activity in the past 8 months than the prior 8 years), then the last thing you need is more people pushing code. If you blindly start pushing everyone to start vibe coding their own solutions, or if your product managers are spending time pushing code for the first time in years, realize that you are not going to see any benefit if you are accelerating any activity that isn’t your bottleneck.

If you want to leverage AI to move faster, first find the constraint. Accelerate that, and only that, until it moves somewhere else. Then repeat - that’s the whole job. The factory metaphor is fun, but your customers don’t care about you maximizing token output.

The Goal earned its spot on my reading lists – you’ll find it (with company) on New to PM and the CIS 9000 pre-reads.

From Tokens to Tasks

Sat, 09 May 2026 00:00:00 GMT

I gave a lecture yesterday for Baruch’s MBA Club called From Tokens to Tasks. About 40 students were there, in person and remote. The lecture was part technical overview and part history lesson, covering, at a high level, tokens, transformers, RLHF, reasoning, tool use, and the agentic loop. I ended with a live demo that started with just a resume I’d made for Bernie the Bearcat, Baruch’s mascot, and ended with a deployed personal website about 20 minutes later. The website came out better than I expected, and Claude taking three tries to successfully deploy it was a good example of how these tools can brute force their way through almost anything for a project as simple as a personal website.

I designed the whole presentation around one sentence I wanted students to be able to say at the end: “I can do this, and I know enough about how it works to be confident using Claude Code.” Every slide and every part of the demo was designed to land that.

The bet was that the hard part of getting started with these tools isn’t technical. It’s the initial hurdle – not knowing what they actually are, what they can and can’t do, where they fit, whether you have the right background to use them. Once you’re past that, the rest is reps. The goal of a 50 minute lecture and a live demo isn’t expertise, but I’m expecting a handful of students to have new, live personal websites this weekend.

The framing I leaned on most for the opportunity created by agentic development tools was Boris Cherny’s printing press analogy. In 1450, about 1 in 50 people could read. The printing press didn’t invent literacy, it scaled it. If something similar happens with code – if writing software stops being a specialist activity and becomes something every professional can do – then the person best suited to write accounting software is an accountant, not a software engineer. The accountant has the domain context; the code is downstream of that. The same logic runs through any line of work an MBA might end up in.

That’s a different pitch than “AI is going to take your job,” and it’s one MBAs can actually act on. You’re the domain expert in your own work, and the thing you’ve been waiting on engineering for is suddenly something you can prototype yourself. We discussed some other examples of projects that would be good for students to work on. For the live demo, since every MBA student is always practicing their self promotion, working on a personal website was an obvious choice.

The questions are always interesting. While Claude was thinking, there were some questions from students that were actually very similar to conversations or debates I’ve had with software engineer friends and coworkers. Things like trying to better understand the jagged frontier (where’s the line between when to use it and when not to), how to be frugal with tokens (my answer: don’t, be frugal with your time but if you’re solving an important problem you want to maximize test-time compute), and comparing Claude to other models like Codex or debating when to use Opus vs. Sonnet. The pattern across the questions was that students were already past the “is this real” stage and into “how do I actually use this in my work,” and the similarity in topics between questions from students who are new to agentic development and debates I have had with software engineers really showed how early we are.

Overall, I think the presentation was a success. I’ll be incorporating aspects of it into my refreshed curriculum for CIS 9000 (IT Strategy) in the fall – the reading list behind the course is up on my bookshelf. One of the joys of teaching right now though – who knows what additional developments I’ll need to figure out how to address by the time the fall semester starts.

If you’ve used ChatGPT and haven’t opened Claude Code yet, an hour with it this week is probably worth more than another 50 minute lecture about it. Check out the slides and the site we built below.

Slides · Bernie’s site

Optimistic Notes Heading into 2023

Mon, 02 Jan 2023 00:00:00 GMT

Update, May 2026. Re-reading this three years on: the gold rush framing was right, most of my specific bets were not. Lensa basically died. ChatGPT got bigger than I expected but the consumer-AI-app pattern in general didn’t win the way I thought it would. What actually changed my day-to-day was agentic coding — I use Kiro every day at work and Claude Code for most everything else, and the framing below (“sell pickaxes or learn to swing them”) doesn’t quite hold up. The pickaxes are swinging themselves.

Contrary to what I wrote in 2020, I’m much more optimistic about the future these days. Reviewing what I wrote 2 years ago, however, my optimism then was mostly misplaced. If anything, we saw an overemphasis on financial engineering and FinTech since 2021, and the acceleration we saw during COVID seems to be fleeting as most metrics are returning to their pre-COVID trend lines. However, after the economic turmoil of this year, which will continue in 2023, there is one standout reason to be optimistic for the future: Artificial intelligence.

Artificial intelligence, particularly generative AI, has seen tremendous strides in 2022, building on top of the gold rush of AI and machine learning research in the last 5 years. Lensa is a great example – a mobile app to generate profile pictures, built on top of Stable Diffusion, an open source image generation model released earlier in 2022. ChatGPT is even more interesting, as it was a new product built on primarily 2 year old technology. ChatGPT was eye opening for many people, as it was an incredibly user friendly interface to experience modern large language models (LLMs). Having your model respond in first person seemed to break many people’s brains, but the people who were doomsaying about the singularity or the threat of AI due to ChatGPT are probably closer to the truth than those who ignore it or minimize the impact similar technologies will have over the next decade. Still, Lensa, a consumer app available in mobile app stores, is probably the first significant consumer product leveraging generative AI for a key feature, and will be the first experience with generative AI for many.

ChatGPT being built on a fine tuned version of technology from almost two years ago is actually evidence of a critical aspect of the current state of AI – research is leaping well ahead of the current state of products. Current best in class AI products are toys – Midjourney is incredible to see in action, but still seems mostly to be used due to the novelty, not for productive purposes. Text editors like Sudowrite or Lex are starting to get at productive use cases, but still very early days and in search of a critical customer. The fact that the killer app of AI in 2022 was a demo from an AI research group, in my mind, is evidence of a failure of innovation from product developers and designers.

Research in machine learning is moving incredibly fast. Attention is All You Need was only written 5 years ago, and there are still incredible gains found just by experimenting with taking a technique that worked in one domain and trying it in another (like taking techniques from BERT and applying to TTS instead of NLP). Relatively simple training techniques are bringing huge gains (ChatGPT was built on GPT3.5, essentially just GPT3 with reinforcement learning from human feedback [RLHF]). This is the metaphorical gold resting on the ground in California in the 1840s.

With any gold rush, there are two ways to capture some of the gains society will be presented with – you can sell pickaxes, or learn to swing them.

With research moving fast, there are incredibly opportunities to build great products and experiences. If the pace of progress in the last few years continues, there will be a new Moore’s Law to rely on. Products can be designed without needing to be concerned about today’s constraints, as by the time the product is built and used at scale the models will have surpassed the performance of what was possible early in the product development cycle. There will be lots of work on the research side as well, but R&D efforts will need to transition from being purely research focused – ensuring models play nice with each other and updated models can be used by existing products will be critical. We’ll also need to move away from published papers and citations as a measure of success. Groups that have researchers working hand in hand with product developers, balancing the tradeoffs of exciting research opportunities with delivering value to users, will be best positioned to succeed in the next few years. This will require leaders with a balance of technical acumen and ability to understand their customers. There will be many who try, but few who will succeed at scale.

But for those without the interest or skill to work on building products, there is still the opportunity to become the best creator or user of these new AI products. AI products present new loops and workflows that will take some getting used to. Becoming an expert in the early days of these products will be a surefire way to accelerate one’s growth – whether the use of AI products is known to others or simply an input that increases productivity. In a decade or less, everyone will need to leverage AI products to keep up, so there is only a short period of time where simply being comfortable using AI products will be an advantage. However, there will always be those who can extract more value and productivity gains from the same tool, and building the skillset early will provide lasting value. Working with generative AI products is different than the normal mental model we’ve all developed of working with computers – generative AI is not guaranteed to be correct or deterministic. Learning how to leverage this is a skill in and of itself – like learning how to get value out of a teammate who is incredibly responsive but just makes something up the majority of the time.

I encourage my students at Baruch to use generative AI products. I’m able to do this while the school doesn’t have an official policy yet, but I’m doing my part to help academia evolve to support generative AI products faster than the calculator was adopted in the 20th century. I think everyone needs to start learning to use these products, and, besides encouraging the next generation to use them, in 2023 I’ll be doing my best to help build user focused products that turn these exciting opportunities into real productivity gains and value for users.

Upgrading Your Tech Skills

Sun, 14 Aug 2022 00:00:00 GMT

Through work and my involvement with Cornell Tech, I talk with many junior / aspiring product managers and MBAs who want to ‘get more technical.’ I’ve always been relatively technical, but for those who weren’t making their own websites in elementary school (or didn’t stick with it), here are my suggestions:

Learn to Program

The foundational piece of any technical skills, learning to program is a must. I highly recommend Automate the Boring Stuff with Python (the book or course) to start.

I recommend using introductory programming books or courses as a way to get a quick overview of any new language or interesting technology – I usually find a reason to do this every other year.

Understand the Cloud and Infrastructure

If you’re comfortable with basic programming, the next step is understanding basics of the cloud and managing infrastructure. There are two paths here I recommend – either have a project and host it yourself with something like Digital Ocean or go through a beginner certification of a major cloud provider, like AWS’s Certified Cloud Practitioner.

Data Structures and Algorithms

So you’re familiar with writing your own scripts and spinning up a quick website for a new project. What’s next? This is where you get to work that might not impact your day to day in most Product or MBA jobs, but will still be very useful mental models.

For data structures and algorithms, I recommend The Algorithm Design Manual.

System Design

Arguably more important than DSA, system design is an incredibly deep topic. I don’t have a singular entry level resource to point folks to, but the System Design Primer has a ton of resources worth going through. There is also this System Design Interview course that has been recommended to me, but I haven’t gone through it myself.

Staying Technical

The most important part to staying technical is to always be pushing yourself. If you aren’t being forced to flex your technical skills at work, you need to find time to stretch yourself elsewhere. I try to devote at least 5-10 hours per week to leveling up a skill outside of my day to day responsibilities. If you want to get and stay more technical, the above resources are a great place to start.

Thirty Three

Wed, 13 Apr 2022 00:00:00 GMT

Unlike Drake when he was ten years younger, I’m wondering if I’m really too old to be feeling this young. I’ve never felt more confident about, and excited for, the future work I’ll be doing.

I spent my birthday back in the office, planning with my team as we spin up a new organization at Audible dedicated to building products that support creators. Our team went out to happy hour after, and it was great to run into colleagues I hadn’t seen in person in years, or ever. I never plan to go back to the office full time, but there is something to the serendipitous interactions that you miss out on from home.

After getting home from happy hour and eating a fabulous dinner cooked by my fiancée, I continued prepping for the last few classes of the semester at Baruch and edited some onboarding documents for an angel investing syndicate I’m getting started.

My goal for the next year of my life is to continue surrounding myself with new opportunities and energetic, talented people. Building new organizations at Audible, teaching undergraduates in the final semester before graduation, and helping new companies get off the ground are a few of the ways I’ll be doing this.

The phrase “old and jaded” is making less and less sense to me, as every year I get older I only get more excited and optimistic about the future. We have some incredibly difficult challenges facing the world that we can’t ignore, but just as constraints breed creativity, the challenges we’re facing will bring out the best in us. I’m thirty three, and feeling younger than I ever have.

Global Media & Entertainment at Amazon

Sun, 01 Aug 2021 00:00:00 GMT

For a lecture in the Business Strategy undergraduate capstone course at Baruch in July 2021, I wrote the below 1 page case study. In the Amazon fashion, I had all of the students read the case during our class, we walked through it answering questions from the students, and then we continued to the discussion questions. I was very impressed with the students, and sharing in case it helps create more great discussions.

Amazon, Inc.

Amazon launched in 1995 as an online book seller. It quickly expanded into additional categories to become the everything store, growing on razor thin margins and reaching its first profitable year in 2003. In 2002, the first web service from Amazon was launched. When Amazon first broke out earnings from Amazon Web Services (AWS), it had $265 million in profit on net sales of $1.57 billion in Q1 2015, growing at 69% YoY. That same quarter, Amazon had a $57 million loss on total net sales of $22.72 billion.

In Q1 2021, Amazon had $8.86 billion in profit on $108.51 billion in net sales. In their earnings report, this net sales was broken down into three segments – $64.36 billion from North America, $30.65 billion from International, and $13.5 billion from AWS, with $3.45 billion, $1.25 billion, and $4.16 billion in profit respectively.

Media Reorg and Acquisitions

On May 13, 2021, Andy Jassy announced in an internal memo that Jeff Blackburn would be returning to Amazon to lead a new Global Media & Entertainment (GME) organization. GME would include Prime Video, Amazon Studios, Amazon Music, Wondery, and Audible, all previously part of the Consumer Domain Organization (CDO), as well as Amazon Games and Twitch, formerly part of AWS. Jeff Blackburn would report directly to Jeff Bezos, and then to Jassy once the CEO transition takes place in Q3 2021.

Less than two weeks after this announcement, Amazon announced the acquisition of MGM, the nearly 100 year old film and television studio. This $8.45 billion acquisition would be Amazon’s second largest, only behind Whole Foods, purchased for $13.7 billion in 2017. 6 months before the MGM announcement, Amazon announced that Wondery, the popular podcast publisher, would join Amazon Music. On June 24, Amazon announced that Art19, the podcast hosting and monetization platform, would be joining Amazon Music as well.

Culture

Amazon is known for its peculiar culture. Made famous by its leadership principles and emphasis on narrative documents over powerpoint presentations, Amazon leadership is also known for providing autonomy for entrepreneurs and leaders of business units at all levels. The service oriented architecture of Amazon’s technical organization is mirrored in its business units, with single threaded owners and 2 pizza teams able to execute with minimal executive intervention.

For citations and discussion questions, download the case study PDF.

My Personal Productivity System — Getting Sh*t Done in 2021

Tue, 01 Jun 2021 00:00:00 GMT

I was stuck inside for a rainy Memorial Day Weekend in NJ, thinking a lot about how I could make the most of my time. This inevitably comes down to the tools and systems I use to prioritize my work and get shit done. I wanted to document how I’m working right now, as it is always a work in progress. The best productivity system is simply what works for you – none of this should be proscriptive, but this is what works for me right now.

I’ve spent a lot of time thinking about how to manage myself and my work, since reading Merlin Mann’s 43 Folders and thinking too much about GTD in undergrad. I’ve used everything from a Hipster PDA to Omnifocus to text files. Still, updating my tools and systems is typically on my “not a priority” list these days. Despite the popularity of thoughtbois, working on your personal productivity system has extreme diminishing returns.

I spend more time focusing on my personal learning system – how I read and take notes, my zettelkasten, etc. That’s another post though. For my productivity system, we’ll focus on:

Inbox Zero
GTD & OKRs
Automations

Inbox Zero

Personal email, work email, Slack messages – whatever it is, I practice Inbox Zero. If I’m reading a message I can action and close out in under 5 minutes, I do it. This includes making quick decisions on if a message actually requires action (hint: less inbound messages require an action than you think).

Anything that requires thought or will take more than a few minutes to resolve, gets put on a to do list. I’m not strict about managing this list, but most off them go into…

GTD & OKRs

For Getting Things Done and managing projects, I use Things. I have Things divided into three Areas: Life, Growth, Work. Most smaller projects get a few to-dos. This blog post, for example, was 3 items – Outline, Draft, and then Publish. Larger projects will get their own Project in things. I stay flexible with due dates, typically scheduling each item to a day within the next week, or leaving it unscheduled. Unscheduled items are reviewed at the beginning of the week, as I finalize my calendar.

I use the same system for personal OKRs. I’ve experimented with more structure and using more full-featured products like Notion, but for managing my own OKRs keeping it simple has been what’s worked best. About once per quarter I will update a page in Notion for my OKRs and goals. I keep a running list of items that I acknowledge are low priority here as well. This quarterly update is then put into Things for any action items.

Automations

Besides practicing Inbox Zero and using Things for most of my productivity system, there are a few tools I use that significantly impact my day to day productivity. I have not gotten deep into automations, as the nature of Product work I don’t find myself often doing repeatable tasks.

Still, a very important tool for my system is Alfred. I have the Alfred Powerpack on my personal and work computers. I use it every day – definitely not to its maximum potential, but for exactly what I need it to do:

Shortcuts and quickly searching for or opening documents and programs
Auto completion and text expansion
Writing templates (everything from journalling to PRDs)
Clipboard History
Workflows

I use a few other smaller tools that have been very helpful, but shouldn’t need much of an explanation. What is key, though, is that for almost all of these there is a fully featured iOS or mobile web version.

1Password
Dropbox
Roam
Anki

Roam and Anki are a bit of a preview to my learning system, which I’ll save for another day.

This overview of my productivity system, I’ve found, to be the exact right level of detail you should stick to. You should keep your system flexible, but have a few models for thinking about work that any new project or task can fit into. Having this system where I can offload all of the maintenance – remembering dates and deadlines especially – has proven to be hugely impactful for both my day to day stress and productivity.

Newsletters — May, 2021

Sun, 09 May 2021 00:00:00 GMT

About a year ago, I shared the newsletters I subscribe to. I still think newsletters, in addition to a properly curated Twitter feed, are the best way to both stay up to date on relevant news. They are also a great mechanism to support a continuous growth mindset.

There have never been more high quality newsletters as there are right now. Still, as part of how I put Inbox Zero into practice, I avoid subscribing to any newsletter I won’t be reading thoroughly. I am quick to unsubscribe if I find a newsletter is not providing the right ROI for the time invested to read it. With that bar, the newsletters I’m currently reading are all highly recommended if they line up with your interests or career.

Stratechery by Ben Thompson (paid version)
The Diff by Byrne Hobart (paid version)
Lenny’s Newsletter by Lenny Rachitsky (paid version)
Not Boring by Packy McCormick
Money Stuff by Matt Levine
Alex Danco’s Newsletter

New to the list are Not Boring and Lenny’s Newsletter (the latter I was trying out in June 2020). Anything I should be reading not on the list? Let me know.

Optimistic Notes Heading Into 2021

Sun, 03 Jan 2021 00:00:00 GMT

Generally, I am not optimistic about the short term future of the US or the world. I’m not writing songs about it, but there are large problems I don’t see a clear path to resolving. Still, as we head into 2021, there are a few areas where I am cautiously optimistic.

Finance

Unlike most FinTech companies that are creating a better UX for the same services of old, companies like Stripe, Affirm, and Pipe are building creative, new solutions. These companies will enable new opportunities for entrepreneurs and consumers. When everything is online, everything is data. Internet-native financial engineers are just getting started making use of this data, and I’m excited for what it could mean. Pipe looks like it could be capital-as-a-service at scale, helping companies grow and founders maintain equity, and Affirm is making it easier for both sides of large transactions. Stripe truly seems to just be getting started building the financial rails of the internet. As similar product offerings mature to become more accessible, I’m optimistic the global impact, especially from supporting or enabling small businesses, will be significant. Stripe’s continued global focus alone is responsible for much of this optimism.

Passion Economy

Substack, TikTok, YouTube, Twitch, Teachable, and others are continuing to prove how individual or small collectives of creators can be incredibly successful. ACX is seeing incredible growth. Some of these ideas have been around for a while, but I continue to be impressed by the unique ideas of the up and coming creative class. I expect more thought leaders, of all shapes and sizes, to monetize their passion in new ways. Supported by advances in financial engineering and new distributed resources (like MrBeast leveraging cloud kitchens to sell burgers nationally), I’m optimistic for how these creators will be growing and opening up new opportunities in the 2020s.

2020 Acceleration

2020’s acceleration of ecommerce and work from home trends have been written about ad nauseam. The flight from cities will have negative environmental impacts that shouldn’t be ignored. However, I’m hopeful for the positive social impact that a wider distribution of tech earnings could have, and increased optionality in both where one works and who one works for. There will be a realignment period that will be tough for cities and companies that are slow to adapt, but I’m confident the long term impact, globally, will be worth the short term local disruptions.

Health and Genetics

Many more informed than I have written about the advances AlphaFold2 is responsible for and what the successful use of mRNA vaccine development could mean. These advances feel like the step change in BioTech that we have been expecting, and both can potentially unlock new, unprecedented treatments. I am optimistic both will lead to further advances, and the coming years could also be the time CRISPR starts having real impact.

Elon Musk

Elon Musk alone is a reason to be optimistic. He has taken incredibly hard engineering challenges and made generations worth of impact in the last decade. $TSLA has been ridiculous to watch, but the excitement, among the market and retail investors, for clean energy technology is undeniable. Kara Swisher’s statement that the first trillionaire will be a CleanTech entrepreneur seems almost inevitable at this point. One of many things that need to go right to minimize climate catastrophes, but the insanity of $TSLA’s rise the last few years does have a ring of optimism to it.

Beyond Tesla, it is hard not to be optimistic seeing what SpaceX has accomplished. When I was teaching elementary school, I would make a big deal out of SpaceX launches and would watch them live with students. Now, they happen so often that they are getting boring. Additionally, starlink might be able to generate the cashflow to get SpaceX to Mars, and if it goes well it will also have a huge impact here on Earth. Combined with working-from-home trends, Starlink could leapfrog the rural broadband infrastructure much of the world, including the rural US, is missing. Friends I grew up with in Big Sur could immediately benefit, and it would make my father’s house hunting in rural Northern California much simpler.

All Usage is Not Created Equal

Sun, 15 Nov 2020 00:00:00 GMT

Every product manager needs a detailed understanding of how their product is used. In a desire to be data-driven, most PMs jump to Google Analytics or its equivalent (I have a problem with how “data-driven” is used when it comes to product management, but that’s another post). To really understand how your product is being used, you can’t just rely on the raw data. You’ll need a framework to understand the data, the right metrics, and anecdotes to paint a fuller picture.

The best framework to interpret your data is something you should already be very familiar with: your user personas. Any data won’t be worthwhile if you aren’t able to tell the story of what it means, and personas are key to that.

When working on one of my first SaaS B2B products, this became very clear. When looking at the administration portal for an EdTech product I owned, we had personas who used the portal every day (librarians, professors), every week or month (IT), and then a group of users who only accessed the portal every quarter or even every year. This last group was usually the key decision maker, but might only log in to the portal a few times a year. Looking at their use cases and usage metrics alongside DAUs wouldn’t make sense. Their use cases occurred in less than 1% of logins, but were incredibly important — that single annual login might be the deciding factor in a subscription renewal.

Alongside your personas, you’ll still need the actual data and the right metrics. A very important thing to get right, you’ll have to look at your product and identify the right high level metrics to track (could be as simple as DAUs) as well as how you’ll measure specific features. As always, you’ll need some metric to measure anything that matters.

In addition to tracking all the right data and a framework to interpret it, a key, and often missed, aspect of understanding a product’s usage is anecdotes. Usage anecdotes are important to surface edge cases that might not be visible in the data, to validate interpretations of the data, or paint a more coherent story you can share about your product’s usage. The key part about anecdotes is you will need to prompt users to provide them — this can be everything from app store reviews to an open ended question in surveys. The critical part is having a path for users to provide them and a process for your product team to interpret them.

With these three things — a source of anecdotes, sufficient tracking of metrics, and a persona-based framework to interpret them — you’ll have the key ingredients to really understand your product’s usage.

Building Frames: Mental Models for Product Management

Sun, 04 Oct 2020 00:00:00 GMT

Adapted from a presentation originally shared at Yeshiva University’s Katz School of Science and Health.

When starting as a Product Manager, knowing the responsibilities and goals is only the first step. The responsibilities of a PM can vary drastically from team to team or project to project. It is important to have different ways of thinking about the role in order to properly orient yourself. This is a sample of my toolkit that serves as a baseline for how I think about the Product Manager role and any problem, feature request, or new initiative I encounter.

CEO of the Product

One of the common mental models for thinking about product management. Being the CEO of the Product sounds enticing, but it is important to consider what this does not mean.

This model does not mean you will be:

making unilateral decisions on the direction of the product
making hiring and firing decisions
managing cross functional direct reports

It does mean you will be:

coordinating across the company – regularly talking with legal, marketing, customer service, PR, finance, operations, UX, engineering, etc.
balancing projects based on current and future needs
working to understand the market and competitive landscape
discussing priorities and projects with company leadership

CEO of the Product is a useful model to remind a product manager they have a uniquely comprehensive view of the product. Don’t take this to mean that the product manager is ‘the boss’ though.

Sh*t Umbrella

In the sh*t umbrella model, the Product Manager is acting as protector – protecting the engineers and designers building the product, or even protecting the product’s users. A good sh*t umbrella is a blocker or filter – preventing well intentioned, but harmful, forces from negatively impacting the product. This often comes in the form of incorrect metrics or goals, an incorrect balance of short vs. long term thinking, or inappropriate executive intervention.

For the product team, the umbrella ensures that the changing whims of an executive don’t change the daily priorities, or that engineers aren’t being distracted by daily requests from partners. The PM should be ensuring the team is working on the right projects, that requirements are defined before development starts, and that any change in focus is warranted. Often an engineering team is a company’s most valuable resource – preventing throwaway or busy work is a high leverage task for the PM.

Similar can be true for protecting customers. A PM should be making sure fixing one problem doesn’t cause another and that the product keeps all of its target user groups in mind when maturing.

Bring the Donuts

Made famous by Ken Norton, Product Managers both literally and figuratively bring the donuts.

PMs can have a huge impact on the morale of a team, and the right cheerleader can create or kill a team’s momentum. This is definitely true for the product team – the engineers, designers, and PMs building the product – but, from my experience, it is equally true with business and operations teams. A product manager might be the only one focused on keeping customer service teams motivated to help solve a tricky problem or building excitement among sales teams for an upcoming enhancement.

Bringing the donuts isn’t just acting as a cheerleader though – it is facilitating conversations between the right stakeholders and doing the dirty jobs that won’t get done otherwise.

PM Project Lifecycle

Especially at a large organization or with a mature product, it is important to define what stage a specific project is in. All stages definitely overlap, but thinking through this lens will help inform what the key areas of focus should be for at that time. Many products or features will not survive every transition from one stage to the next – it is important for a PM not to get too far into solving tomorrow’s problems.

Ideation

A PM should always be spending some time with projects in this stage. Understanding customer pain points, defining a problem space, and the beginning of solutioning.

Socialization

For an idea to move forward, you’ll need to properly socialize it. Everything from hallway conversations, formal meetings, or sharing documents falls in here. At Amazon, this is often centered around a PRFAQ.

MVP

Once everyone is aligned on the problem space and solution, organizational concerns like putting the team together and defining ownership are resolved (to some degree of certainty) then you’ll have reached the minimum viable product stage. At Amazon the concept of “minimum lovable product” is also used – the smallest, quickest set of functionality that can be delivered for a customer to fall in love with.

Scale

Once a feature or product is in the hands of customers and we have some data showing it is addressing the problem we are targeting, it is time to transition into scaling and maturing. The team will have to balance paying down tech debt while expanding to support new use cases, new users, and so on. A product lucky enough to survive to this stage may be a going concern for a long time, and a good product managers will already be ideating and socializing what the next steps will be.

Using Mental Models

I find these mental models useful for making sure I’m focused on the right tasks for any given project or team. They are a useful way to train myself to quickly understand a product, and what my focus should be for a specific project. I also use these often when talking with early career PMs to understand their perspective. Product Management is an ambiguous role, and these frameworks help build a shared language.

Reading 6 Hours Per Day

Fri, 26 Jun 2020 00:00:00 GMT

Warren Buffet spends five to six hours reading every day. I’m always trying to read more, but this quote made me think more specifically about how much and what I am reading. This is my ideal breakdown for reading in a day:

Current events, business news, and related research	3 hours
Fun, relaxing read (fiction, well written business books or case studies)	1 hour
Social media	1 hour
Growth reading (academic papers, books outside my core focus)	1 hour

Newsletters — June, 2020

Tue, 02 Jun 2020 00:00:00 GMT

Email newsletters (in addition to a well curated Twitter feed) are the best way to stay aware of current events, learn from subject matter experts, and broaden one’s understanding of key issues. Newsletters are growing in popularity for a reason!

This is my list of newsletters I currently subscribe to:

Stratechery by Ben Thompson

Sinocism by Bill Bishop

Money Stuff by Matt Levine

Two Truths and a Take by Alex Danco

The Diff by Byrne Hobart

Benedict’s Newsletter by Benedict Evans

I also try to sample a new newsletter every month or so. The Diff was a recent sample that I’m definitely sticking with. Currently, I’m also subscribed to Lenny Rachitsky’s newsletter.

I don’t recommend all of these to everyone, but, if they match with your interests, all of the above are worth checking out.