I own multiple CD and/or digital copies of a lot of albums. The original CD, the remaster, sometimes a deluxe edition or an anniversary box on top of that. They all sit in the same library in Roon, and every time I hit play I’m implicitly choosing one of them (Roon sets one of the copies as “primary”). This bugged me because I didn’t know which one would objectively sound better (I clearly have problems). So I decided to stop guessing and measure it to help me: which version of each album is the most dynamic?

The short answer is that the original pressing usually wins, and the exceptions are super interesting, so I thought I’d write it up. (This post is about CD/digital remasters—I know vinyl is a whole different ballgame.)

How I measured

My CD collection is ripped losslessly (mostly FLAC files), a little over 2,100 albums across 603 artists. For every track I calculated crest-factor dynamic range, the same DR measurement the Dynamic Range Database uses. Take the difference between the loudest peaks and the average level of a track, in decibels. A DR13 album has quiet verses and loud choruses. A DR6 album is loud all the way through.

All of the measurements live on The Shelf, a small site I built to browse the results: every album and every pressing I own, with the measured numbers side by side. That’s also where the comparisons in this post come from, so you can check my work.

One caveat before the numbers. DR measures dynamics, and dynamics are one ingredient of how something sounds. It can’t tell you about tonal balance or tape sources, and two masters can share a DR value and still sound different. What it captures well is compression—and compression is the mastering decision you can actually hear as fatigue. It’s also the defining sound of the loudness war: from the mid-90s onward, CDs were mastered progressively louder so they’d stand out on radio and in shuffle mixes, with the peaks flattened into the average. By the 2000s this was an open fight, with Metallica’s Death Magnetic as the famous casualty. Many of the remasters in my collection come from exactly that era.

What the numbers say

Of my 2,100 albums, 127 exist in two or more versions. In 79 of those the gap between the best and second-best version is at least one full DR point, which is my threshold for caring.

Remaster-type releases win 16 of the 79. That sounds like a respectable showing until you look at who those winners are.

Seven of the 16 are Genesis albums, and they’re my favorite finding in the whole dataset. The winners are the 1994 Definitive Edition Remasters, and the versions they beat are the 2007 and 2009 remixes, rebuilt from the original multitrack tapes, by 3 to 5 DR points each. And Then There Were Three is the widest gap: DR13 for the 1994 remaster against DR8 for the 2007 remix. The older product wins because the newer one was made at the height of the loudness war. That’s the whole war in one discography. David Bowie’s Ziggy Stardust makes it eight, with the 1990 remaster beating the 2012 one.

Five more winners are audiophile pressings from Mobile Fidelity Sound Lab, including Alanis Morissette’s Jagged Little Pill, Michael Jackson’s Dangerous, and Muddy Waters’ Folk Singer. No surprise there. Gentle mastering is the entire reason those pressings exist, and it shows up in the measurements exactly as advertised.

That leaves three cases, out of 79, where an ordinary label remaster beats the original CD. Porcupine Tree’s In Absentia is the standout. The 2017 remaster measures DR11 against DR7 for the notoriously loud 2002 CD, a rare example of a remaster undoing loudness-war damage. The other two are Metallica’s Master of Puppets (the 2017 remaster) and the 2023 remaster of Dire Straits’ On The Night.

There’s one hopeful pattern below the threshold too. The recent wave of anniversary editions (Dookie at 30, Dark Side of the Moon at 50, the Kid A reissue) mostly ties the originals. Reissues seem to have stopped making things worse, which after two decades of the opposite counts as progress.

What I do with this

My buying rule used to be a vague preference for original pressings. Now it’s a measured one: the earliest CD is the default choice, an audiophile pressing is worth it when the gap is real, and a remaster needs evidence before I go near it. I will now check the DR Database before I buy—it’s crowdsourced and covers most pressings of most things.

I really wanted “remastered” to mean “better”, but for years I suspected that wasn’t really the case. Now I know for sure. The good news is that the originals are, for the most part, much cheaper second-hand than the remasters, so at least there’s that.

Next week I’m running a session with our product and engineering leadership on how AI is changing the product management role. To prepare, I read the people who’ve shaped how product managers think about the job (Marty Cagan, Melissa Perri, Teresa Torres, Rich Mironov, John Cutler, the Reforge team), alongside product leaders doing the job right now at Anthropic, OpenAI, Netflix, Instagram, and Whatnot. Then I went through the 2025-2026 survey and study data I could find.

Most of what I found applies well beyond our team, so I’m sharing the condensed version here, along with the questions we’ll be discussing in case they’re useful for yours.

Where the sources agree

1. Building got cheap. Deciding what to build got expensive. Mike Krieger, Anthropic’s chief product officer, says 90-95% of the Claude Code product’s own code is now written by Claude Code, and that their bottleneck moved to deciding what to build. (Self-reported, but the rest of the sources point the same direction.) Writing documents by hand is losing its value, because AI now produces a serviceable draft of any of them on demand: product requirements documents (PRDs), backlogs, research summaries, status updates. So the hard parts of being a PM are becoming more important: choosing which problems to work on, understanding customers, and defining what “good” means.

2. Speed alone is not converting to outcomes. In an April-May 2026 survey of 309 senior product leaders, nearly nine in ten had adopted AI coding assistants. But just over a third said AI strengthened how their org operates. DORA, Google’s long-running research program on software delivery, found in 2025 that teams using AI ship faster but their releases get less stable. GitClear, which analyzes code quality across millions of code changes, found duplicated code up 81% since 2023. Refactoring, the cleanup work that keeps a codebase maintainable, dropped sharply over the same period. Marty Cagan calls this the AI productivity paradox: shipping faster without better discovery (the work of figuring out what’s worth building) gets you to the wrong place sooner.

3. Teams are getting smaller and more senior, with wider scope per person. Netflix, Instagram, and Whatnot all describe versions of this. Instagram is moving toward pods of 4-6 engineers plus one “product staff” generalist covering product management, design, data, and research. Nobody agrees on what the smaller team should look like, though. Across the sources I counted multiple different proposed structures, and none of them match. The one that maps best to enterprise infrastructure, where I spend my days, is Drew Breunig’s. He splits the role into application PMs, who move fast and sit with customers, and foundation PMs, who own the platform, compliance, and quality underneath the application teams.

Where they disagree

The biggest disagreement is over validation: do you still need to test whether an idea works before shipping it, or is shipping the test? Cat Wu at Anthropic is on the “shipping is the test” side. Her argument is that models improve so fast that a plan made at the start of a project can be wrong by the end of it, so her team ships quickly and revisits what they’ve built at every model release. In her version of the job, the PM names the few non-negotiables and lets go of the rest.

Leah Tharin argues the opposite. The slow part of building something valuable is finding out whether people want it, and that depends on how many users you can learn from, no matter how quickly the code gets written. Even at Smallpdf, the document-tools company where she used to work, experiments across 50 million users took weeks to produce a real answer.

Marty Cagan’s build-to-learn versus build-to-earn distinction is the middle position, and roughly where I currently land. Prototype and learn as fast as you like, but a prototype exists to answer questions, and a product has to work at scale for people who pay for it, which hasn’t gotten any cheaper.

They also split on the PM-to-engineer ratio. Oji and Ezinne Udezue, both longtime product leaders, argue PMs are the constraint now, so the ratio should go up. The strongest counterpoint is Microsoft’s 2025 cuts: of 1,985 roles cut in Washington state, 817 were engineers and 373 were product managers, roughly one PM for every two engineers, a far bigger share of PMs than any org I’ve worked in. The sources do agree on one thing here: scope per PM keeps growing.

Three questions for your team

1. As engineering gets much faster over the next year, what breaks first in your org? The candidates across the sources are discovery, decision speed, validation, go-to-market (getting the thing sold and adopted), and your capacity to review and absorb everything that now gets built. Rejecting the premise is a legitimate answer. John Cutler calls “the bottleneck moved to product” a lazy metaphor, and points instead at the pace of knowledge turns, meaning how quickly the whole system learns.

2. When a prototype can act as the spec, what is the PRD still doing for you? And who owns the evals that define “good” for your AI features? Uber’s experience suggests the PRD survives as the record of why, with prototypes carrying the what. Their line that “two hours of prototyping unblocked four weeks of discussion” matches what I’ve seen. As for evals: they’re the repeatable tests that grade an AI feature’s output, which you need because AI answers aren’t simply right or wrong. OpenAI’s Kevin Weil calls writing them a core skill for product managers. In most organizations I’d wager that job currently belongs to nobody.

3. Where do the seniors come from, and what is their scope? Everyone wants smaller and more senior teams, but the junior work people used to build judgment on is the first thing AI absorbs. Stanford’s payroll data shows employment for early-career workers (ages 22-25) in the jobs most exposed to AI down 16% relative to other workers, while experienced people in the same jobs have held steady. Teresa Torres has the rule I find most convincing: an expert using AI beats AI on its own, but juniors who let AI do all the analysis never build the skills to become that expert. LinkedIn replaced its associate product manager program, the traditional way into the role, with a “Product Builder” program that trains generalists across product, design, and engineering. That’s one answer to the scope question; the sources’ proposed structures all differ, so it’s yours to decide.

The reading list

If you only read three of these:

• Code Isn’t Product by Rich Mironov, for the case that the constraint moved to discovery and go-to-market.
• State of AI in Product 2026 by Melissa Perri and Product Circle, for the best current data on the gap between adopting AI and being changed by it.
• Cutler’s bottleneck piece, for the counterargument to the tidy version of this whole story.

Melissa Perri has the best take I’ve read on what matters most for PMs right now:

Measure your productivity by how often you changed a decision that mattered, how often you saw around a corner, how often a senior leader walked out of a room thinking differently because of something you said. How often your shipped features translate into real customer outcomes is what matters.

AI can speed up some of what’s on her list. The judgment behind it still comes from years of practice, and there are no shortcuts to that.