When I was 19, I embarked upon my first solo trip.

It was my first time catching a plane on my own, and as my taxi rolled up to the airport, I found myself running late.

So I made like Flock Of Seagulls’ classic hit, and I ran.

I ran through the terminal, working up a sweat as I dodged and weaved through the crowd like I was Muhammad Ali dancing around George Foreman.

And it paid off. I made it to the gate in time.

The hostess greeted me with a warm smile, and a wave of relief came over me.

But a moment later, she informed me that I was in the wrong terminal.

Most work is a waste of time

You might think, “cool story Steve, but what on Earth does this have to do with how AI is showing up in our organisation?”

A lot, actually.

Back in 2020, at the height of lockdowns, I released my last book Time Rich (Wiley), which was an ode to better ways of working.

I made the case that most of what most people do at large organisations is a waste of time.

Price’s Law backs this up. It posits that the square root of an organisation’s headcount creates about half the value. That means that in a 1,000-person company, 32 people create half the value.

This is further echoed by the Pareto Principle, or the 80/20 Principle, which finds that 4% of causes create about 64% of outcomes.

Essentially, there is a very long tail of people doing work that doesn’t amount to all that much.

A typical workday

Think about your typical workday inside a large organisation.

• Consensus seeking.
• Hyper-responsiveness.
• Always on.
• Constant chatter (and GIFs) in Microsoft Teams or Slack.
• Process paralysis.
• Meetings that you don’t need to attend.
• Making reports nobody reads.
• Polishing work well beyond the point of diminishing returns.
• Tenure and loyalty being rewarded over contribution.
• Constant task switching.
• Poor prioritisation of workload.
• Completing tasks because we’ve always done them, but nobody seems to know why.
• A long tail of busywork that adds up to very little.

Faster at wasting time

And then we add AI to the mix.

So now, we’re faster, better, stronger… at wasting time.

It’s akin to me running for my life through the wrong airport terminal.

Which is why 95% of companies are yet to see a measurable ROI on their AI investment.

Redesign the work first

To get business outcomes from AI, organisations need to revisit their culture, their processes, and how work gets done.

If leaders are serious about measurable business outcomes beyond simple declarations from employees like “I feel more productive”, then the work should be re-designed before bolting AI on top of it.

We’re going through a radical transformation of what “knowledge work” looks like.

This requires leaders to re-think everything they know about how work gets done and how value gets created.

It requires mapping tasks and workflows across the organisation, and re-designing them with AI in mind.

The hard decisions

It requires addressing cultural shortcomings and making some very hard decisions.

Recently, Canva announced that it’s laid off 22% of its staff, and Microsoft offered a quarter of its staff redundancies.

This is to be expected. How you choose to handle that is another question, and beyond the scope of this article. But there are ethical and respectful ways of parting with talent in an increasingly uncertain environment.

Otherwise, you can keep measuring vanity metrics like “AI adoption”, and “AI certification badges earned”, just don’t expect it to translate into meaningful ROI.

Back to the airport

And in case you’re still wondering. I missed that flight and learned a very valuable lesson early into my travels around the globe. I’m pleased to say I haven’t missed a flight since because I was in the wrong terminal.

Steve Glaveski is the founder of Collective Campus, which builds culture and talent for the AI world. He’s also a 2x Wiley author, regular HBR contributor, and hosts the Future Squared podcast. Learn more at collectivecampus.io

Why Your AI Program Isn’t Delivering Business Outcomes was originally published in Venture on Medium, where people are continuing the conversation by highlighting and responding to this story.

As an elder millennial born in 1983, I was buying CDs regularly until my early 20s, when the iPod and iTunes, arriving in the wake of Napster, fundamentally changed music distribution.

As an unashamed metalhead, I remember going into record stores, finding the heavy metal section (which might’ve boasted a couple of hundred CDs), and often buying albums on the strength of the front and back cover alone. Hello Cannibal Corpse!
And if it had a Parental Advisory sticker on it, even better!

Because it wasn’t just music that was in limited supply, it was content about music.

There was no social media, Reddit, YouTube, or even Spotify to help us discover artists or listen to something before we bought it.

Yeah, we might’ve learned about bands from one of several music mags or locally made fanzines; maybe we traded tapes with friends or with strangers across the globe (whose details we got in the classified section of said music mags).

Maybe we heard a track or two on the radio at 2 minutes to midnight (as did I, listening to graveyard shift heavy metal radio shows like Andrew Haug’s 3 Hours of Power on Australia’s Triple J).

But beyond that, buying an album was like buying a box of chocolates. You often didn’t quite know what you were going to get.

So just by virtue of your music being in a record store, you had a much better chance of people discovering and buying your album than you do today [6].

Contrast this with Spotify, where about 81% of the roughly 9.75 million artists that Chartmetric tracked had fewer than 1,000 monthly listeners in 2023 [1]. It is an excruciatingly long tail of artists screaming into the void.

What it took in 1995

By my estimate, somewhere around 25,000 albums were released in the US in 1995, by major labels and independents combined [2].

Recording costs were prohibitive, and the average major label put out roughly 1,500 albums per year, or just 30 a week [3].

If you wanted to go it alone and be indie, you’d better have had a well-paying day job (or rich parents!).

Buying studio time, hiring a producer, covering manufacturing costs and so on could set you back tens of thousands of dollars if you wanted to do it well [4]. And even then, you still needed a hard-to-come-by distribution deal to get your album into stores.

When there’s only so much shelf space to go around, distributors and record stores are only going to carry you if they think you’ll actually sell. Not much different from getting a D2C product into a big box retailer today.

Most musicians simply couldn’t afford the DIY option, nor was it an option that set you up for success, so they chased major label record deals.

And if you were one of the lucky ones, your album was one of a few thousand to a few tens of thousands of titles in your local record store, depending on the store [5].

Suffice to say, the barriers to entry were high. But once you cleared them, you were one of perhaps 8,000 to 12,000 major-label releases that year [3], a far smaller pool than today’s.

That gave you a real shot at being heard and building a cult following that could put you on the road and earn you a living, though plenty of major-label albums still flopped [6]. And once you had an album in major record stores, being discovered by listeners was, in my experience, far easier than it is today [6].

What it takes now

On Spotify, roughly 36 million new tracks are released each year, based on the figure of over 100,000 new songs a day [7].

By comparison, 25,000 albums at about 10 tracks each comes to roughly 250,000 tracks in 1995, so Spotify sees on the order of 140 times as many new tracks per year [8]. That’s because anybody with a laptop can record music today, and anybody with an internet connection can get it distributed to the world via Spotify and other platforms.

Today you can record an album at home with a DAW like Pro Tools for roughly $1,000 or less, if you already own a laptop and buy entry-level gear.

Pro Tools Artist costs $99 a year, and an audio interface, a microphone, headphones and cables add a few hundred dollars more [9]. Getting the album onto Spotify through a distributor typically costs somewhere around $20 to $30 a year [10].

So while the internet has made it cheaper and easier than ever to make your music accessible to a Siberian lumberjack in the cold dead of winter, it has also made it fundamentally harder to stand out. This is the essence of the distribution paradox.

And guess what? AI music generators like Suno are increasingly making the supply side even more crowded.

You don’t need to know how to play an instrument, write music, sing, or operate a DAW anymore. Just prompt Suno to do the whole God damn thing for you, wait a few seconds, and voilà, here’s your track!

For the week of November 3, 2025, researchers found 202,046 AI-generated tracks released onto Spotify against 238,646 human-made ones, putting AI above 40% of new releases [11].

So far, though, those tracks have won very little attention: 93% had fewer than 1,000 plays, and Spotify says well under 1% of listening goes to fully AI-generated music [11]. The flood is real, but for now its main effect is making the noise louder, not displacing human listening [12].

Of course, if you’re a savvy digital marketer, you have a leg up in today’s environment. Back in the 80s, marketing almost exclusively amounted to plastering flyers of your band’s next gig all over the Sunset Strip in West Hollywood, and spamming magazines and radio stations with copies of your album. Now it means TikTok videos, appearing on podcasts and YouTube channels, collaborating with other artists, getting onto top lists, and all manner of other tactics.

But still, with roughly 36 million new tracks a year on Spotify and rising (up from about 60,000 a day in 2021 to over 100,000 now [7]), and with people’s music taste more splintered and fickle than ever, and their attention spans more bombarded and shorter than ever, getting and keeping attention long enough to amount to anything people really care about is f*cking hard.

Not only that, but back in the 90s, when you committed to forking your hard-earned money out for an album, you actually listened. I bought two to three albums a month with my then-limited disposable income, so you better believe I listened to each track, read the liner notes, and paid close attention to the cover art.

But today, if you manage to capture someone’s attention on Spotify, chances are they ‘listened’ while doing seven other things on their phone, and quickly moved on to another artist because there was no real investment made to listen to you. They didn’t pay a cent.

Which brings us to the real paradox.

In 1995, the scarce thing was access: to a studio, a deal, a spot on the shelf.

Today, access is nearly free, and the scarce thing is attention.

That little metal section at my local record store worked because someone had already narrowed a couple of hundred albums down for me, and a cover and a Parental Advisory sticker did the rest.

Maybe the artists who break through now will be the ones who find a way to recreate that intimacy online.

This isn’t true of just musicians, but video-game designers, authors, movie directors, and almost any other creative you can think of.

Today, the shelf space might be infinite.

But the attention has never been more scarce.

Sources and assumptions

Links in the text go to the sources. Anything without a link is my own estimate, and the numbers below explain how I got it.

[1] Chartmetric’s 2023 report, via Music Business Worldwide: 19.16% of about 9.75 million tracked artists had more than 1,000 monthly listeners. Monthly listeners are unique people over 28 days. Newer datasets differ at the low end, so call 81% a floor.

[2] My estimate. I couldn’t find a published count of 1995 releases, so I assumed US new releases grew from about 14,000 in 1990 to about 36,000 in 1999. That averages out near 25,000, with a range of 18,000 to 32,000. SoundScan misses small indie releases and counts reissues and compilations as new.

[3] My estimate. I assumed majors made up a third to a half of releases, so roughly 8,000 to 12,000 across Warner, Sony, BMG, EMI, PolyGram and Universal, or 1,300 to 2,000 each. That includes reissues and compilations.

[4] My estimate, from memory: $5,000 to $50,000 to record an indie album, and $150,000 to $500,000 or more for a major.

[5] My estimate, from memory: 3,000 to 6,000 titles at discount stores, 10,000 to 25,000 at mall chains, 5,000 to 30,000 at independents, and up to 150,000 at megastores.

[6] Opinion. I have no data on discovery odds then versus now. This is based on my own experience.

[7] Coverage of Spotify’s 2025 payout report puts new uploads above 100,000 songs a day, or about 36.5 million a year. Music Business Worldwide reported about 60,000 a day in 2021. The 100,000 figure is secondhand and counts tracks, not albums.

[8] My math. 25,000 albums at about 10 tracks each is roughly 250,000 tracks, and 36.5 million divided by that is about 145. The 10 tracks per album is an assumption. I compared tracks to tracks because many Spotify uploads are singles. If only a quarter to half of new tracks sit on 10-track albums, the gap in albums is closer to 40 to 80 times.

[9] Pro Tools pricing is from Production Expert. The hardware figures are my estimates: interface $100 to $500, mic $100 to $500, headphones or monitors $100 to $1,500, cables $50 to $200. “Under $1,000” assumes you already own a laptop and buy entry-level gear.

[10] My estimate. Distributors like DistroKid charge roughly $20 to $30 a year. I haven’t checked current prices.

[11] A University of Chicago study, as summarized by Xenospectrum, classified about 40.75 million tracks. For the week of November 3, 2025 it counted 202,046 AI tracks, 238,646 human-made and 60,022 unclassified. AI made up 5.1% of the catalog it analyzed. The under-1% listening figure is a Spotify spokesperson’s comment to ABC News in July 2026, as reported by Chartlex, not a published number. AI detection isn’t perfect.

[12] Opinion. The data shows a flood of AI uploads, not yet a flood of AI listening, so I’m calling it more noise rather than displaced listening.

I was recently the victim of a cyber-attack from Vietnam that saw my Facebook and Instagram accounts compromised and subsequently disabled.

Try as I might to speak with an actual human being at Meta, my efforts have increasingly seemed futile, and I now find myself rebuilding my social media from scratch.

Not necessarily a bad thing, because I needed a kick up the bum.

To help me get some momentum going, I’m giving away copies of my books Employee to Entrepreneur and Time Rich.

Just give me a follow at instagram.com/thenewsteveglaveski and DM me “books” to go into the running to win.

Ancient Buddhist wisdom teaches us that desire is the root of suffering.

Modern-day psychologists like Barry Schwartz tell us that being content with ‘good enough’ leads to more inner peace and happiness than pursuing optimal outcomes.

I too have urged readers to define what their version of “enough”is. Doing so can help us avoid the trap of chasing forever-shifting goalposts and never stopping to enjoy the temporary gift that is life.

And while all of this might be true, and while I still stand by all of it, there are times when good enough can be the enemy.

There are times when it can stand between you and Great — a more developed and fulfilled version of yourself, and it’s something worth being wary of as we make our way through our lives.

I write this as someone in his early 40s, with the dust having settled on a lot of life decisions, and with a lot more time and data to reflect on what worked and what didn’t

So in this piece, I’ll share how ‘good enough’ has shown up in my life, how it’s prevented me from being my best, and how it can show up in yours.

Good Enough Place

For the past decade, I’ve lived in the inner Melbourne suburb of Yarraville. It’s a 10-minute drive to the beach and the Melbourne CBD, and has a cute town centre dotted with cafes and an art deco cinema.

But outside of two or three busier blocks, it’s essentially a sleepy, leafy suburb.

For the past ten years, I have longed to live by the beach, in a more happening area, with a younger and more active demographic, and more going on.

But because Yarraville is ‘good enough’, and close enough to the action — not terrible by any stretch, the pain has simply not been great enough to leave, despite my knowing that I’d be much happier and higher-energy elsewhere.

When Bad is Better Than Good

It’s worth pausing here to elaborate on this “pain not being great enough” point.

If a relationship, place of abode, or job is simply unbearable, it compells us to make wholesale changes — to throw the baby out with the bathwater so to speak, and start anew, which can be both liberating and empowering.

But if something is “good enough”, then it’s harder to justify making said changes and facing the uncertainty and disruption that comes with it, even if we know we’re living a 6/10 existence.

Sometimes the pain has to be so great that nothing is better.

Sometimes the relationship has to be so bad that you’d rather be alone.

Sometimes the job and your boss are just so soul-devouring that you’d rather go without a paycheck for a few months and live off your savings, or heck — even move back home if you’re young enough, while you find something better to do with your life.

Good Enough Partner

You might find someone who ticks some arbitrary boxes — good looking, decent job, not an asshole, not a dumbass, makes an effort to help out around the house, and so on.

On the surface of it, this might be good enough for a lot of people.

But do they light you up?!

Can you get lost in conversation for hours at a time?

Is the sex actually amazing and almost transcendental, or is it just transactional?

Do you laugh at silly things together, and lift each other up to new heights?

Are your values truly aligned?

That ‘good enough’ person could be getting in the way of that amazing person who would lift you up to a whole new level of consciousness.

I’ve been there many times, and being afraid to leave just kept me locked into a less than happy existence marred by the constant drum of background anxiety.

Good Enough Profession

Years ago, I had a ‘good enough’ job.

I worked at EY in my late 20s, making six-figures, flying around Australia, staying in nice hotels, and consulting to large listed companies.

I got to enjoy corporate junkets, and the ‘status’ that came with working for a big consulting firm.

It was definitely good enough.

But it was also constricting. As I wrote in my 2018 book Employee to Entrepreneur, it left me miserably comfortable.

It didn’t allow me to tap into my diverse skill-sets and creative drive, and it definitely didn’t tap into my entrepreneurialism.

Only when I took the risk and stepped away from it did it open me up to a whole new world — building businesses and teams, public speaking, writing books, podcasting, appearing in mainstream media outlets, and so much more.

I did a lot more learning, growing and developing as an individual building businesses than I did in the comfortable and secure confines of an established management consulting firm.

Good Enough Outcomes

One of my biggest life regrets has been not moving to the United States while I was in my early-to-mid thirties.

I missed out on tapping into what author Mark Manson calls ‘Earth luck’ — putting yourself in a place and surrounding yourself with people that can give you outsized returns.

For example, if I’m a tech founder, I will naturally have more luck in San Francisco than Lincoln, Nebraska.

If I’m a musician, I’ll have more luck in Hollywood than Arkansas.

If I’m in a comedian, I’ll have more luck in Austin, Texas than Albuquerque

So why didn’t I move?

Well, back in 2017 (when I was 33), I was running a seven-figure startup accelerator, I was writing for HBR, I had just secured my first big book deal with Wiley, my podcast was doing quite well, and I was interviewing the world’s biggest entrepreneurs and authors on the regular.

I was on the local speaking circuit and even got flown out to do a few international talks.

This was definitely “good enough”.

But by not putting myself at the epicentre of the action in the US, I tempered what could have been extraordinary outcomes in favour of modest ones.

I didn’t surround myself with the kinds of people and opportunities that could have not just 10X’d my career outcomes and growth as a person, but 100X’d them.

And upon reflection, I have never truly elevated to another level in the time since. Things are still good enough.

All of this being said, it’s worth reflecting on two simple questions.

1. Where is “good enough” showing up in your life?

and

2. Is this something you’re truly content and happy with, or is it getting between you and a vastly more fulfilled and developed version of yourself?

I’m not a developer.

Let’s get that out of the way first.

Twelve months ago, if I’d had this idea, it would’ve turned into a six-month side quest involving agencies, estimates, and polite emails explaining why it would “cost more than expected.”

At least, that’s been my experience to date.

Instead, using Claude Code, I built a working product in under four hours for about twenty bucks.

Here’s what it does - and why that’s a bigger deal than it sounds.

The problem was boring. That’s the point.

I’ve spent a lot of time around local soccer clubs in Melbourne. Like most grassroots sports organisations, they survive on a fragile mix of memberships, gate receipts, food and drink sales… and sponsorships.

Sponsorships are the lifeblood. And they’re also a pain.

Clubs tend to chase the same businesses over and over, mostly because they don’t know who else is sponsoring nearby clubs.

The reality is simple: companies that sponsor one local club are far more likely to sponsor another.

So the idea was obvious:
What if you could see every sponsor of every sports club near you?

Not a spreadsheet.

Not a guess.

Actual data.

So I built the tool.

You sign in with Google.

You enter your suburb.

You choose a radius — 5, 10, 20 kilometres.

And the tool returns every sports club in that area, across different sports, along with their sponsors.

In my local search, that surfaced 131 clubs. One of them — Preston Lions Football Club — has 291 sponsors. That’s not luck. That’s a dataset.

Click into a sponsor, and the tool can:

• Find a contact
• Pull a name and email
• Draft a personalised outreach email
• Send it from your email address

This would have been insane to build a year ago.

If I’d tried to do this twelve months back:

• It would’ve taken me months
• Or cost tens of thousands of dollars
• And still required multiple iterations to get it usable and not ugly

This time?

A handful of prompts.
Claude Code.
GitHub.
Vercel.
A couple of other free tools.

Total cost: about $20 in usage and ~$30/month in tooling.
Total build time: roughly four focused hours.

That’s the part people still haven’t fully internalised.

This isn’t about “AI building apps for you”

This isn’t a “look how smart the AI is” story.

It’s a leverage story.

The real shift is that:

• Non-technical people can now test real ideas fast
• The cost of being wrong has collapsed
• Speed matters more than polish
• Thinking beats execution horsepower

This completely changes how experimentation works.

You don’t need permission.
You don’t need funding.
You don’t need a roadmap.

You need a real problem, a rough idea, and the willingness to try.

The real unlock: rapid testing

The most important part isn’t the tool itself.

It’s the fact that I could:

• Identify a niche problem
• Build a working solution
• See if anyone actually wants it

All before a traditional product team would’ve finished their first planning meeting.

That’s the game now.

And if you’re still waiting for perfect requirements, technical certainty, or “the right resources” - you’re already behind.

I’m not technical.
I didn’t write clever algorithms.
I didn’t raise money.

I just built something useful by prompting Claude Code.

That door is now open to everyone with a basic command of English and $20 in their pocket.

“If the ladder is not leaning against the right wall, every step we take just gets us to the wrong place faster.” — Stephen Covey

Which Industry or Company Do You Target, First?

In B2B go-to-market, most teams obsess over the climb.

They argue about messaging, debate which channels to test, fine-tune outbound cadences, and optimise landing pages. All useful. But very few stop to ask a more basic question:

“Are we even climbing the right mountain?”

Because in reality, not all prospects are created equal.

One company can be the perfect storm: big deal size, painful problem, short sales cycle, a hungry champion, and a logo that opens five more doors.

Another looks similar on paper, yet turns into an 18-month slog that never closes, or worse, closes small and drains your team (and bank account) with ongoing support requests.

If you are a founder, marketer, or GTM leader, this is your real constraint: It’s not how many emails you can send, but how precisely you choose who to send them to and in what order.

So here is the uncomfortable question you need to ask yourself:

“Are we chasing the companies that look good on a pitch deck, or the ones that actually compound our learning, revenue, and reputation?”

This post is about solving that problem.

It is about building a simple, spreadsheet-friendly scoring model that helps you decide:

• Which industry to focus on first
• Which companies inside that industry to target first, second, and third

Before you invest another cycle in “more pipeline,” make sure you are climbing the right mountain range.

Why Sequencing Matters More Than You Think

Sequencing Is the Hidden Variable

Sequencing sounds boring. It feels like something you worry about later, once you have “more leads” and “a bigger pipeline.” But sequencing is often the hidden variable that explains why one team gets lift off while another with the same product and talent stalls out.

Think about two startups selling roughly the same thing.

Startup A lands three early customers who are perfect fits.

They have painful problems, budget, short sales cycles, and are happy to appear in case studies. Within six months, Startup A has real proof, real stories, and real revenue. Every future deal becomes easier because they can point to those early wins.

Startup B chases whatever looks shiny.

They burn months trying to close a giant logo that has no urgency and brutal procurement. They discount heavily to get a mid-fit customer over the line. They take on a small, needy account that drains support. On paper, they “have customers”, but none of them really move the needle or set the company up for sustained growth and success.

The Compounding Effect of Good (and Bad) Sequencing

Same product. Different sequence. Completely different trajectory.

Sequencing matters because the value of each customer is not just the revenue they pay this year or next.

It is:

• The learning they give you about your ICP
• The proof they give you to win others
• The distribution they unlock through referrals or co-marketing

Good sequencing compounds.

Early wins create better stories, faster cycles, stronger confidence.

Bad sequencing compounds too. Long, stuck deals and weak fits create doubt, churn, and wasted quarters.

The Tempting but Wrong Ways to Prioritise

The Myths That Quietly Wreck Your Pipeline

Most teams do have a prioritisation method. It just happens to be terrible.

One common trap is the Big TAM fallacy.

You see a slide that says “100 billion dollar market” and convince yourself that you simply have to be in there.

Big markets look impressive in decks, but they tell you almost nothing about how painful the problem is, how fast deals move, or whether you have any real edge. You end up spreading yourself across a huge space where you are average everywhere and excellent nowhere.

Then there is logo lust.

You copy a list of famous brands from LinkedIn and decide these are the “strategic accounts.” The team becomes obsessed with chasing them, even if they have weak pain, glacial procurement, or almost zero chance of becoming referenceable. The logo would look amazing on your homepage, but the journey to get it can quietly kill you.

Spray And Pray, Then Pray Some More

The third trap is spray and pray.

Instead of committing to one mountain range, you scatter outbound across five industries and fifty ICPs across each, hoping patterns will emerge.

On the surface it feels like progress. Lots of activity, lots of conversations.

Underneath, every insight is noisy because it is coming from a different context. Product feedback is confused. Positioning never quite lands. Your team burns cycles learning the same lessons in parallel across markets that were never truly worth it.

All three approaches have the same root problem: they start from what looks big or shiny, not from what actually compounds learning, proof and revenue.

Two Layers of Sequencing: Industry First, Company Second

Most teams jump straight into building account lists.

They fire up LinkedIn Sales Navigator, apply a few filters, export a CSV, and call it a target list. What is usually missing is the layer above that: a clear decision on which industry they are actually committing to first.

If you skip that step, everything downstream becomes muddled.

You are effectively climbing half of one mountain, then abseiling into another, then wandering across a valley because someone on the board mentioned “healthcare has a big TAM.”

A better way to think about go-to-market is as two layers of sequencing:

1. Pick the mountain range
2. Then pick the peaks

Layer 1: Industry Level - Which Mountain Range?

At the industry level, you are asking:

In which market do we have the best mix of need, advantage, and economics?

This is where you score and compare industries on things like:

• Market size and growth. Not just “is it big” but “is it big enough and growing fast enough that our bet can compound.”
• ICP density. How many of your ideal customers live here, and how similar are they to each other.
• Your unfair advantage. Prior work, case studies, network, domain understanding.
• Problem intensity. Is the pain your product solves a big problem or just a minor irritant?
• Sales and adoption patterns. Typical sales cycles, innovation appetite, average deal sizes.

You can treat this as a portfolio choice.

You might have three to five industries you could serve. The goal is not to eliminate the others forever. It is to decide where to place a concentrated bet first so you can become genuinely sharp in at least one place, and use your no doubt limited resources effectively to best set you up for growth.

When you choose an industry, you are not only choosing customers. You are also choosing:

• The conferences you go to
• The podcasts you appear on
• The stories you tell
• The product features you prioritise
• The marketing channels you use
• The language you use
• The case studies you celebrate
• The integrations you support
• The content you create

Your learning compounds much faster when those things all point at the same mountain range.

Layer 2: Company Level - Which Peaks?

Once you have picked the range, you then decide which specific peaks to climb first.

Inside a chosen industry, companies are not equal.

Some will have:

• Strong pain and urgency
• Reasonable sales cycles
• Champions with real power
• Logos and networks that unlock the rest of the market

Others will be slow, political, underfunded, or simply not that bothered about the problem you solve.

This is where the company scoring model comes in. You are no longer comparing banking versus media. You are comparing Bank A versus Bank B, or Network A versus Network B, using a consistent lens:

• ICP fit
• Pain intensity
• Deal size and expansion potential
• Champion quality and access
• Sales cycle complexity and time to value
• Branding, reference and co-promotion potential

If the industry decision is a macro asset allocation, the company decision is stock picking within that sector.

Why You Need Both

If you only optimise at the company level, you risk becoming a generalist who learns too slowly. If you only optimise at the industry level, you risk picking the right range then climbing the wrong peaks.

The magic happens when you are deliberate about both. Commit to a specific mountain range. Then sequence the peaks that give you the fastest, cleanest path to proof, revenue, and reputation.

The Industry Scoring Model: Choosing the Right Mountain Range

Before you argue about which company to chase, you should answer a prior question:

Which industry gives us the best odds of compounding revenue, learning, and reputation?

You probably have a few plausible options. For example, with the same product you might be able to sell into media, fintech, SaaS, and professional services. All four might look attractive on a slide. Your job is not to say “we could sell to all of them.” Your job is to decide which one to go hard on first.

A simple way to do that is to build an industry scoring model. One spreadsheet. Each row is an industry. Each column is a factor. You give each factor a weight, score each industry from 1 to 5, and let the totals guide your focus.

Here are the core dimensions that matter.

Market Size, Growth, and Economics

Start with the basics, then go deeper.

• TAM size. Is this industry big enough that a focused play can produce a meaningful business.
• Market growth rate. A smaller market that is growing fast can be better than a giant but flat one.
• Average deal size and budgets. Are there healthy budgets for your type of solution. Are buyers used to paying serious money or do they haggle over every dollar.

You want a market that is big enough, growing enough, and where your category is not an afterthought.

Fit, Advantage, and Problem Intensity

Next, look at how right this industry is for you.

• ICP density. How many tight fit accounts live here. Are they similar enough that learning from one transfers to the others.
• Your unfair advantage. Do you already have experience, content, case studies, or a network in this space. Have you walked the halls and spoken the language.
• Problem intensity and urgency. Is the problem you solve a top three board level issue, or something that can keep getting pushed into next year.

The best industries feel like slightly rigged games. You already know the terrain. You already speak the language. The pain is already hot.

Sales Dynamics and Adoption Patterns

Then look at how selling and adoption actually work in that space.

• Typical sales cycle length. Are deals measured in weeks, months, or years.
• Buying complexity. How many stakeholders, how much procurement, how political is the average deal.
• Digital and innovation maturity. Are buyers experimenting with new tools or still stuck in email and spreadsheets.

You want at least one early industry where cycles are short enough that you can learn and adjust quickly.

Competitive Landscape and Moat Potential

Not all competition is bad. What matters is whether there is room for you to stand out.

• Vendor saturation. Are there already ten lookalike vendors crowding the space.
• Differentiation opportunity. Can you own a clear niche or angle.
• Moat potential. By going deep here, can you build defensibility through data, integrations, trust, or standards.

An industry with no competitors can mean there is no real budget. An industry with a few, weak competitors can be perfect.

Brand, Leverage, and Incentives

Finally, think about leverage and timing.

• Logo leverage. Will logos from this industry impress other industries you might expand into later.
• Co-marketing and distribution potential. Do customers here run events, publish content, or have channels that could amplify you.
• Grants, incentives, and ecosystem funding. Are there government or ecosystem programs that effectively subsidise your sales.

Once you score each industry on these dimensions, you do not need perfection. You just need a clear ordering. Tier A industries to focus on now. Tier B to explore lightly. Tier C to ignore, unless something drastic changes.

The Company Scoring Model: Picking the Right Peaks

Once you have chosen your mountain range, the next question is obvious:

Within this industry, which specific companies should we climb first?

This is where most teams retreat to gut feel. Someone knows someone at Company X. Another person is excited about Company Y because it is all over the news. A board member suggests Company Z. Before you know it, your “strategy” is just a collage of opinions.

A company scoring model gives you a way out of that. It forces you to look at each account through the same lens and ask, “If we are going to spend cycles on this company, what are we getting back in return?”

At a high level, the model does three things:

1. Filters out obvious bad fits up front
2. Separates core power factors from nice to have modifiers
3. Turns everything into a simple numeric score so you can rank accounts

Step 1: Gates Before Scores

Some factors are not just “low” or “high.” They are yes or no.

Before you worry about weights and scoring, define a few non negotiable gates:

• Basic ICP fit (industry, size, use case)
• Location and ability to serve them
• Minimum financial capacity
• At least some evidence of pain or relevance

If an account fails any gate, you mark it Disqualified and do not waste time scoring it. This alone protects you from chasing logos that were never really in play.

Step 2: Core vs Modifier Factors

Once you have a set of qualified accounts, you score them on two groups of factors.

Core factors are the things that really bend the curve:

• ICP fit or “perfect customer” score
• Pain intensity and cost of not acting
• Deal size and expansion potential
• Champion quality and warm access
• Sales cycle length and complexity
• Implementation complexity and time to value

If these are weak, nothing else matters. No amount of logo value or co marketing potential can compensate for a tiny deal with no urgency and a two year sales cycle.

Modifier factors are the multipliers and tie breakers:

• Financial capacity and price sensitivity
• Logo value and referenceability
• Co-promotion and distribution leverage
• Ecosystem and referral potential
• Competitive saturation and your differentiation strength
• Regulatory friction versus moat
• Customer success load and operational ease
• Legal or IP constraints

These do not drive the engine, but they can make a great account even better, or a borderline account less attractive.

In your spreadsheet, you give each factor a weight and a score from 1 to 5. Core factors get much heavier weights. Modifier factors get lighter weights. The sheet multiplies weight by score and sums the result.

Step 3: From Numbers to Focus

The goal is not to pretend the model is perfect. The goal is to avoid pretending every opinion carries the same weight.

When you have scored a set of accounts, rank them by their total score and bucket them:

• A accounts: founder or senior AE focus, full ABM treatment, outbound priority
• B accounts: nurture sequences, opportunistic outbound, partner motions
• C accounts: keep on a watch list, revisit when signals change

Now when someone says “we should really go after Company X,” you can say “Great, let’s score them” instead of “Sure, why not.”

Core “Power Factors”: What Really Moves the Needle

Not every factor in your model is equal. Some are nice to have. Some are mildly useful. And a handful are so powerful that they can make or break your entire go to market. These are your power factors. They deserve harsher scrutiny and much heavier weights in your scoring model.

Think of them as the engine. Everything else is just aerodynamics and paint.

1. ICP Fit: The Perfect Customer Score

ICP fit is the starting point. Not “could they use us” but “were we basically built for them”.

You are looking at:

• Industry and use case
• Company size, stage, and tech maturity
• How central the problem is to their world

High ICP fit means every conversation teaches you something useful. Product feedback is relevant. Wins turn into strong case studies. Implementation paths repeat. Low ICP fit often means you are distorting your roadmap, pricing, and messaging just to make a single customer happy.

In the scoring model, ICP fit deserves one of the highest weights.

2. Pain Intensity and the Cost of Not Acting

Next comes the pain. You can have perfect fit on paper, but if the problem is mild, the deal will drift forever.

Ask:

• How painful is the status quo
• What happens if they do nothing for 12 months
• Is this a top three priority for the sponsor

If the cost of not acting is small, budget gets pulled into louder fires. When the cost of not acting is reputational damage, regulatory risk, or big revenue leaks, deals move. This factor should sit at the same weight class as ICP fit.

3. Deal Size and Expansion Potential

You are not just selling a contract. You are building an asset.

Deal size is not only first year ACV. It is also:

• How many teams or regions could adopt this
• How many adjacent products or services you could introduce
• How much of the account you could reasonably capture

A smaller logo with high expansion potential can be more valuable than a big logo that is permanently constrained to one tiny use case.

4. Champion Quality and Warm Access

Most deals live or die on whether someone inside the company truly cares.

You want to know:

• Is there a real human who will look bad if this does not happen
• Do they have political capital and access to budget
• Are they responsive, candid, and willing to push

A strong champion with a warm intro can compress months of cold outreach into weeks. A weak champion means your deal is always one reorg away from death.

5. Sales Cycle Length and Complexity

Time is not neutral. A long, complex cycle consumes your most valuable resources: founder attention, senior sales time, and emotional energy.

You are looking at:

• Typical time from first intro to signed contract
• Number of approvals and stakeholders
• How rigid procurement and legal tend to be

A repeatable 60 day cycle with clear stages is gold. An unpredictable 12 to 18 month gauntlet can sink an early team, even if the eventual ACV looks impressive.

6. Implementation Complexity and Time to Value

Finally, consider what happens after the ink dries.

• How painful are integrations and data migration
• How much change management is required
• How quickly the customer will see outcomes they actually care about

Early in your journey, you want logos where you can ship value in 30 to 60 days. Fast time to value strengthens your references, lowers churn, and gives your team momentum.

These six power factors should dominate your scoring model. If they are strong, almost any other weakness can be managed. If they are weak, no amount of logo glow or co marketing will really save the deal.

8. Modifier Factors: The Tie-Breakers and Multipliers

Once your power factors are in good shape, the rest of the model is about tilting the playing field. These are the factors that rarely turn a bad prospect into a good one, but often decide which of two good prospects you should pursue first. Think of them as tie-breakers and multipliers.

Economic Refiners

Start with the money details.

Two accounts might have similar deal size potential, but very different financial behaviour:

• Financial capacity. Do they actually have the cash and budget flexibility to move quickly, or do they run on razor thin margins.
• Price sensitivity and margin. Are they used to paying for quality solutions, or is procurement’s main KPI “negotiate everything to death.”

All else being equal, you want accounts where your price is a rounding error on a bigger outcome, not a painful line item that gets haggled every renewal.

Brand, Reference, and Distribution

Then there is everything around signal and amplification.

• Logo value. Will this logo change how other prospects perceive you.
• Referenceability. Will they actually let you use their name, quote them, or speak on a webinar.
• Co-promotion and distribution. Can they put you in front of more of your ICP through email, events, marketplaces, or partner channels.

When two accounts have similar core scores, pick the one that makes the next ten deals easier, not just this one.

Signals, Timing, and Competition

A few more levers:

• Signals and timing. Funding announcements, leadership changes, new hires, and budget cycles tell you when a window is open.
• Competitive saturation and differentiation. Are you the fifth lookalike vendor they have seen this quarter, or the one with a clear, sharp edge.
• Regulatory friction versus moat. Some segments are painful to enter but create a barrier once you are through.

Customer Support, Ops, and Legal Friction

Finally, the “operational tax” of working with them:

• Support load. Will they consume your entire success team?
• Operational ease. Are they decent to work with on contracts, security reviews, and day-to-day interactions?
• Legal / IP constraints. Will their terms limit your ability to reuse learnings or work with their competitors?

Modifier factors do not drive your strategy, but they sharpen it. They help you allocate your best effort to the accounts where the upside is high and the hidden friction is low.

Gates: The Non-Negotiables

Before you start scoring, you need something more basic: stop signs.

Not every factor should be treated as a “1 out of 5 but still counts.” Some things, if missing, should simply disqualify an account from serious effort. If you do not define these gates, you will eventually convince yourself to chase anything that moves, because it scores “okay” on a few nice looking factors.

Gates are your non negotiables. They sit upstream of the scoring model.

What Makes a Good Gate

A good gate has two properties:

1. If it is missing, the deal is almost certainly not worth doing right now
2. No amount of strength in other factors can realistically compensate for it

Typical examples:

• Basic ICP fit. Wrong industry, wrong size, wrong use case. They might be able to buy, but they will not teach you much, and you will not be able to reuse what you learn.
• Location and serviceability. If time zones, language, or regulatory barriers mean you cannot support them properly, it is a distraction, not an opportunity.
• Minimum financial capacity. If they simply do not have the money, the entire exercise becomes an exercise in discounting.
• Zero real pain or relevance. If the problem you solve is not on their list at all, your deal is dead on arrival, even if they “like the idea.”

You can represent this in your spreadsheet with a simple column: Qualified? (Y/N). Only accounts with Y ever get a score.

Gates Protect Focus

Gates look harsh, but they are a gift to your team. They prevent you from:

• Chasing logos that were never real
• Bending your roadmap around edge cases
• Burning months on “opportunities” that were always fantasy

First ask: Should we even climb this thing at all?
Only then worry about whether it scores a 3 or a 4 on logo value.

Turning the Model into a Spreadsheet (And Actually Using It)

At this point it is easy to nod along and still keep operating on vibes. The way you force this model into reality is simple: one spreadsheet, kept up to date, that everyone can see.

Start with the structure. Make one sheet per layer:

• An Industry sheet, where each row is an industry and each column is a factor
• A Company sheet, where each row is an account and each column is a factor

For the company sheet, add columns like:

• Qualified? (Y/N)
• Each power factor (ICP fit, pain, deal size, champion quality, sales cycle, implementation) with weights and 1–5 scores
• Each modifier factor with weights and 1–5 scores
• A Core Score column that sums power factors
• A Modifier Score column that sums the rest
• A Total Score column that combines them (for example, 70 percent core, 30 percent modifier)

The mechanics are boring but important:

1. Define weights once as a starting point
2. For a batch of accounts, mark gates and set Qualified = Y only where it makes sense
3. Score quickly, using a mix of data and informed gut feel
4. Sort by Total Score and bucket into A, B, C tiers

The power of the spreadsheet is not mathematical precision. It is shared reality. When someone says “let us go after this logo,” you can drop it into the model, see how it stacks up, and have an adult conversation about trade offs.

Importantly, your scoring is not fixed. Every quarter, as you collect win loss data and see where deals actually close, you update:

• Weights for each factor
• How strict your gates are
• Which signals truly predict good customers

Over time, the sheet becomes less of an opinion tracker and more of a compass. It turns sequencing from a debate into a repeatable, transparent process.

Example: Two Accounts, Same Product, Very Different Futures

Let us make this concrete.

You are selling into the same industry. You have two targets on your list: Account A and Account B.

On the surface, Account B looks sexier. It is a global brand everyone recognises. If you put their logo on your homepage, investors and prospects would nod approvingly. Account A is mid market, reasonably well known in the industry, but not a household name.

If you are prioritising by vibe, you pick B. If you run both through the scoring model, a very different picture emerges.

Account A scores high on the power factors:

• ICP fit is almost perfect. They match your ideal customer profile on size, tech stack, and use case.
• Pain intensity is high. They are actively talking about the problem you solve in earnings calls and internal memos.
• Deal size and expansion look solid. You can start with one team and easily grow into adjacent teams.
• You have a strong champion with a warm intro from a current customer.
• Sales cycles in this segment are typically 60 to 90 days.
• Implementation is straightforward. You can ship value in the first month or two.

On the modifier side, their logo has some weight in the industry, they are open to co marketing, and CS load looks manageable.

Account B, on the other hand:

• Has weaker ICP fit. They are larger, more complex, and use a very different internal stack.
• Pain is present but diluted among many other priorities.
• Deal size is bigger on paper, but expansion is gated by multiple regional fiefdoms.
• No clear champion. You are starting from cold outbound.
• Sales cycles are 12 to 18 months, with brutal procurement and legal.
• Implementation would be a multi quarter project.

Logo value and potential distribution look incredible, but everything else screams drag.

In a world without a model, you chase B first and hope. With a model, it is obvious that A should be your first climb, and B might belong on a long term, low touch nurture list. The product has not changed. The clarity of your sequencing has.

Conclusion: Stop Chasing Shiny Peaks, Start Climbing the Right Mountain

If there is one idea to take from all of this, it is simple:

In B2B, who you go after and in what order is a bigger lever than any single tactic.

You can have a solid product, decent messaging, and competent sellers, and still stall out if you are climbing the wrong mountains in the wrong sequence. You can also have plenty of rough edges and still win if you pick an industry and a handful of accounts where the odds are stacked in your favour.

The model here is not magic. It is just a way to force discipline around questions most teams already ask quietly:

• Does this industry really suit us
• Is this company actually a great fit, or just a famous logo
• Are we betting our limited cycles on accounts that will compound learning, proof, and revenue

By splitting the problem into industry first, company second, and then separating power factors from modifier factors, you get three benefits:

1. You stop arguing from anecdotes and start deciding from a shared lens
2. You protect your team from wasting months on attractive but low leverage deals
3. You give everyone a clear list of “these are our mountains, these are our peaks, this is why”

This does not need to be perfect on day one. Start with a scrappy spreadsheet. Score a handful of industries. Score twenty accounts in your chosen space. See what the model tells you, run the plays, and then come back with real data:

• Which factors actually predicted good customers
• Which ones were noise
• Where you need to tighten gates or adjust weights

Treat the scoring model as a living artifact, not a one off exercise. The companies that win are rarely the ones who shout the loudest. They are the ones who pick their mountains carefully, climb the right peaks first, and let compounding do the rest.

The year was 1997 and I was just 13.

I caught a train to the Melbourne CBD and wandered on down to HMV on Bourke St.

This was before Napster, and before I even used the internet.

Aside from the radio and trading cassette tapes, record stores were the only place you could sample music.

The massive two-storey establishment made me feel like a kid in a candy store.

And it was full of headphones wired to albums you could listen to.

At this stage of my coming of age story, I had already begun forming an appreciation for the darker side of music — heavy metal — and found my way to the metal and hard rock aisle.

There, I cast my eyes on a black album with nothing but a gold cross on it emblazoned with just four letters… O Z Z Y.

It was Ozzy Osbourne’s then brand-new greatest hits album, celebrating the first 28 years of his career.

Fortunately, it was was wired to a pair of headphones.

I picked them up and put them on, and within seconds of hearing the opening riffs of Black Sabbath iconic and eerie self-titled track from 1970, I was transported to another world.

I was hooked.

I later fell in love with the melodies and hooks on not just Sabbath albums, but Ozzy’s classic solo albums like Blizzard of Ozz, Diary of a Madman, and No More Tears.

And I’ve been a fan of Ozzy’s music, and of the ecosystem that has emerged around him — guitarists like Zakk Wylde and the late Randy Rhoads, for example, ever since.

And now, another 28 years later, Ozzy has taken his final bow, not only on stage in Birmingham less than three weeks ago, but on the stage of life.

A sombre reminder that no matter what we achieve in this life, one day, we all have to say goodbye — so while you work and chase your dreams — don’t forget to channel your inner Ozzy Osbourne and enjoy the ride.

Thanks for the music, and for the wild stories.

RIP Ozzy.

Over the past few days, my LinkedIn feed has been flooded with thought leaders and authenticity warriors absolutely losing their minds over a deepfake influencer photo ‘taken’ at Wimbledon, subsequently racking up tons of engagement.

The general sentiment is something like: This is inauthentic! AI is ruining humanity! How dare they! Authenticity matters more than ever!

It’s all very dramatic.

And it would be cute… if it weren’t so hilariously misguided.

Because here’s the thing: you’re not as authentic as you think you are.

In fact, most people are about as authentic as a $10 Rolex sold in Bali.

Newsflash: Inauthenticity existed before generative-AI came along.

Let’s talk about audience capture, for instance.
It’s when you start saying and doing things - not because they’re true to you - but because they appease your audience, because they get you likes, because they stroke your ego with engagement.
You know exactly what I’m talking about.

That polished LinkedIn post about how “failure is the greatest teacher,” that you wrote 15 minutes after crying into your Uber Eats-delivered Pad Thai because your startup just imploded.

Or let’s talk about how you copy what everyone else is doing.
The TikTok dances.
The YouTube thumbnails with your mouth hanging open like you’re watching the Hindenburg explode.
The Instagram reels with the same recycled Hormozi captions and the same formula: “Here’s three things I wish I knew when I was 20.”

Sounds authentic, right?

But this isn’t new.

It’s not even a modern social media problem - it’s human nature.
In music, bands have been taking inspiration from and essentially copying each other for decades.

Oasis took more than just inspiration from The Beatles, for example.
Heck, half of Hollywood is just the same predictable story and happy ending, only with different actors and better CGI.

Authenticity has always been more of a marketing slogan than a reality.

Most people engage in preference falsification, saying one thing in public to fit in with the acceptable narrative within that group, and then quietly thinking or saying something very different in private.

It turns out integrity - the ability to speak and act in alignment with what you actually believe - is about as rare as a good hotel Wi-Fi connection.

And now AI comes along, and suddenly everyone’s worried that their unique, authentic selves are under threat.

I hate to break it to you, but in most cases, AI can probably do your job about 90% as well as you can - if not better - for a fraction of the cost, and it doesn’t need coffee breaks, or mental health days, or a yearly offsite in Bali to stay motivated.

You can rationalize all you want about why you’re irreplaceable, why your work is sacred and authentic and full of soul, but unless your name is Tim Henson or something like that, you’re probably just deluding yourself.

So if you choose to accept this, you’ve got two choices:

1. Bury your head in the sand and keep screaming about authenticity while you sleepwalk into redundancy.
2. Adapt. Find where you can still add value. Learn to work with the machines, instead of pretending you’re something special they can’t replicate.

Chances are, most of us will end up walking down a third road — living off UBI or something while we figure out how to fill our days, derive some sense of meaning, and distract ourselves long enough to quell the demons that inevitably pop up whenever we have too much free time on our hands.

I know this isn’t what most people want to hear on a “let’s all validate each other’s sentiments” platform like LinkedIn, but it’s probably what you need to hear.

So stop clutching your pearls over a deepfake influencer at Wimbledon.

If you’re worried about authenticity, start by looking in the mirror.

And maybe start thinking less about how to look authentic, and more about how to actually be someone worth replicating.

What happens when all of your heroes die?

It’s one of the cruel features of getting older.

All those larger-than-life characters you idolized — athletes, actors, musicians — start dropping like flies, reminding you they were, after all, just mortals.

The NBA stars I worshipped as a kid? Charles Barkley and Michael Jordan are now both 62.

Arnie and Stallone, once the very definition of indestructible action heroes, are 77 and 79, essentially living on borrowed time according to the average American lifespan.

And for me, as a lifelong headbanger, Black Sabbath’s recent farewell concert was a bittersweet punch in the gut.

Ozzy Osbourne, now 77 and battling Parkinson’s, took his final bow in Birmingham — the birthplace of heavy metal — in what can only be described as the Live Aid of Metal.
The lineup was absurd: Anthrax, Slayer, Pantera, Metallica, Guns N’ Roses, and even the ‘younger’ generation — Lamb of God, Gojira, Halestorm.

Two supergroups formed on stage, packed with icons: Nuno Bettencourt (Extreme), Tom Morello (RATM), KK Downing (Judas Priest), Chad Smith (RHCP), Steven Tyler (Aerosmith), and countless others.

It raised $190 million for Parkinson’s and children’s charities — a triumphant, deafening send-off.

But even as these legends shredded, screamed, and thrashed like men decades younger (see Anthrax’s set), I couldn’t shake the sense of sadness — not just for the moment Ozzy got people’s tears flowing during Mama I’m Coming Home, but for metal itself.

Because here’s the uncomfortable truth: most of the truly iconic artists on this stage were in their late-50s to late-70s.

And while many newer metal bands are excellent and often technically and musically better, they simply don’t — and likely won’t — command the same kind of influence or devotion as their forebears.

Lamb of God? Brilliant. But they’ll never be a household name like Metallica.
Gojira? Fantastic. But their name fades into obscurity the moment you step outside metal circles.

And the numbers say it all:

• Lamb of God — 2.1M monthly Spotify listeners
• Gojira — 2M
• Halestorm — 4M

Now compare:

• Guns N’ Roses — 34M
• Metallica — 30M
• Black Sabbath — 18M
• Ozzy — 12M
• Iron Maiden — 8.5M

Same story at concerts. Sure, older bands benefit from multi-generational fans — AC/DC played to a total of 26,000 at Melbourne’s Sidney Myer Music Bowl in 1981 and will this year play two shows at the MCG with a 180,000 capacity across both shows .

Do we expect Lamb of God to quadruple their audience in 20 years? Hard to imagine.

There’s more going on than the multigenerational effect.

1. Fragmentation of media

In the ’80s, you typically had four TV channels, a few magazines, and a handful of big radio stations. You couldn’t not hear about certain bands.

Now? We’re drowning in YouTube, TikTok, Spotify, Netflix, and whatever the hell else, and audiences are splintered into a thousand little niches.

Back then you’d buy a $30 album and play it on repeat for a month because that was all your budget allowed. Now? We scroll, we skip, we forget.

2. Barriers to entry are lower than ever

In the ’90s, if you didn’t have a record deal, good luck.

Today? Anyone with a laptop, ProTools, and Wi-Fi can record and release a song to the world.

Eleven million artists on Spotify, just a click away.

Distribution isn’t hard anymore — but getting noticed amongst a sea of 11 million artists on Spotify is almost impossible.

3. We don’t value music like we used to

If you wanted to discover underground bands in the 80s and 90s, you’d trade cassette tapes with strangers you found in the back pages of metal magazines.

I still remember swapping a mix of local bands on scratchy cassette tapes with a kid in São Paulo who introduced me to a little band called Sepultura. That discovery felt like mine.

When you had to work to find music, you cherished it.

Today’s music on tap makes it all seem a little disposable.

4. Nothing is truly new

Back in the 70s and 80s, music scared people.

When Tony Iommi unleashed the devil’s tritone on Sabbath’s debut, it was unsettling enough to have priests denouncing it from pulpits.

Heck, popular recorded music and rock and roll were only about 20 years old by the time Sabbath came along — about as old as Taylor Swift’s debut album.

When punk exploded, it was a middle finger to the establishment.

When grunge hit, it killed the hair metal party overnight.

Now? Sure, bands like Polyphia, Sleep Token, and Ghost are innovative — but nothing feels revolutionary anymore. We’re spoiled kids who don’t know what they want for Christmas. “Eh. Surprise me.”

Final Thoughts

So here we are. Watching our heroes fade while knowing their replacements will never feel quite as monumental.

But maybe that’s okay.

Maybe the point of heroes isn’t to live forever.

Maybe it’s to remind us what’s possible when lightning strikes — and to leave us stories, riffs, and memories we can pass down.

Because someday, all of them will be gone.

And maybe with them gone, the music too will slowly fade into obscurity and then oblivion.

But maybe not.

Maybe, like Jesus, Allah, and Buddha, these bands started something that will leave armies of disciples carrying the torch long after they’re gone. There might not be another metal messiah, but that doesn’t mean there won’t be a proverbial church to mosh at on Sundays.

Maybe we were just the lucky ones who got to see the proverbial Gods of metal who started it all in the flesh.

I know this much to be true. As a lifelong metal fan, few things in this life have reliably brought me as much joy, comfort, and strength as putting on a heavy metal record and cranking it to eleven — and I hope subsequent generations truly get to experience what I and millions of other horn-raising headbangers around the world have for the past half a century now.

Long live metal.

In memory of Ronnie James Dio, Lemmy Kilmister, Cliff Burton, Randy Rhoads, Peter Steele, Jeff Hanneman, Chuck Schuldiner, and Layne Staley.