The title was the joke. The principle inside it was not.

The book was about data centre operations. Essentially about the chronic tolerance for systems that did not work, practices that persisted through inertia rather than merit, tools that had outlived their usefulness but remained in place because replacing them felt harder than living with them. Its argument was simple: anything that looks like a process, throw a system at it. Identify what is ineffective. Remove it. Replace it with something better. Do not be sentimental about it. The window is right there.

I was in my twenties. I filed it under useful and moved on.

Twenty years later I think about that book almost every week.

What defenestration actually means

Defenestration is the act of throwing something out of a window. The word has a specific historical gravity: it entered the language via political violence, the throwing of people from buildings as an act of decisive, irreversible rejection. You do not defenestrate something and reconsider. There is no managed transition. The ground is right there.

The Unix guide used it as a metaphor for organisational clarity. Stop tolerating what does not work. The ineffective system, the redundant process, the tool that made sense in 1995 and has been carried forward on habit ever since → out the window. Not deprecated gradually. Not sunset over three planning cycles. Thrown.

What made the principle powerful in the data centre context was the discipline it imposed on decision making. The question was not “is this system familiar?” or “do our people know how to use it?” or “what will it cost to change?” The question was: does it work well enough, given what is now available? If the answer was no, the window was the only honest response.

That question has not changed. What has changed is how many things now fail to clear the bar.

The scope problem

When the book was written, the category of “things that look like a process” had legible boundaries. Data entry looked like a process. Scheduled reporting looked like a process. File transfer, system monitoring, backup routines, access provisioning, all of these were clearly mechanical, clearly automatable, clearly candidates for the window.

Everything else felt safe. The judgement calls. The synthesis work. The decisions that required context, experience, and the kind of pattern recognition that accumulates over a career. These were not processes. They were expertise. And expertise, by definition, could not be thrown out a window and replaced with a system.

That assumption is now wrong.

Not partially wrong. Not wrong at the edges. Wrong in a way that is accelerating every month as the capability of AI systems expands into territory that, two years ago, was considered permanently human.

Legal review looks like a process. Financial modelling looks like a process. Customer support looks like a process. Recruitment screening looks like a process. Strategic synthesis looks like a process. Market research looks like a process. First draft everything looks like a process.

The Unix principle, applied today with intellectual honesty, leads somewhere its author almost certainly did not intend. Because the scope of what can be thrown at a system and replaced effectively, now includes most of what organisations spend most of their money on.

What is actually being defenestrated

It is important to be precise here, because the conversation tends to collapse into the wrong argument.

The thing being defenestrated is not people. It is systems. Organisational systems, being the accumulated architecture of how work gets done, which tools are used to do it, which processes exist to govern it, which layers of coordination have been built up around it.

Most of those systems were not designed. They were inherited. A SaaS tool was adopted to solve a specific problem. Then another. Then another. Then a set of internal processes grew up around the tools. Then additional staff were hired to manage the processes. Then reporting layers were built to give visibility over the staff managing the processes. Then the whole structure became the de facto way the organisation operated, and the original question, does this work well enough, given what is available? stopped being asked because asking it felt destabilising.

That is the definition of a system that belongs out the window.

I know this not as an abstract argument but as something we executed at Audalize. We looked at the constellation of SaaS tools running our operations. Jira, HubSpot, Cognito Forms, and a collection of internal tools that had accumulated over years of patching problems with whatever was available and we made a decision. We would build our own. A single, purpose-built internal platform, replacing multiple enterprise subscriptions, integrated with an LLM as the connective tissue across the whole system.

Not in months. In days.

We are a managed services company. Not a software house. Not a funded technology startup with a dedicated engineering team. A managed services company that decided to throw its operational stack out the window and rebuild it from scratch because the tools now available made that the rational choice.

If we can do that, so can your competitors. To themselves. Or to you.

The inertia taxonomy

Organisations that fail to defenestrate what should go are not making a technical mistake. They are making a cultural one. The reasons for keeping ineffective systems in place tend to cluster into a few recognisable patterns.

Sunk cost attachment. The system cost two million dollars and three years to implement. The people who championed it are still in the building. Acknowledging that it should go means acknowledging that the investment was wrong. So the question stops being asked.

Expertise conflation. The people who operate the system have built genuine proficiency in using it. That proficiency feels like organisational value. In some cases it is. In more cases it is proficiency in operating something that should not exist, which is a different thing entirely.

Risk asymmetry in decision making. The person who approves a major system replacement bears the downside risk if it fails. They bear almost none of the upside if it succeeds. The rational response to that incentive structure is conservatism. Do not replace. Extend. Patch. Add a module. Defer.

The comfort of familiarity dressed as caution. This is the most insidious one. It presents itself as prudence: “we need to be sure before we act,” “we should run a pilot,” “let’s see how the technology matures” but it is really just discomfort with discontinuity wearing a business case. The organisations run by people who can distinguish between genuine caution and rationalised inertia are the ones that move. The rest write strategy documents about digital transformation and buy more SaaS subscriptions.

The Unix guide had a name for all of these: things that look like reasons but are actually just the window being kept closed.

The irreversibility asymmetry

Here is the argument that the comfortable version of this conversation always misses.

Defenestration feels irreversible. Keeping the current system feels safe. The asymmetry seems obvious, throw something out and you have to rebuild; keep it and you maintain continuity.

That asymmetry has inverted.

The cost of rebuilding, given current tools, is a fraction of what it was two years ago and declining rapidly. The cost of keeping an ineffective system, in direct spend, in operational drag, in the compounding opportunity cost of not running on a better architecture, is fixed and, relative to the cost of replacement, growing.

More importantly: the risk of keeping the current system is no longer the risk of inefficiency. It is the risk of being outpaced by a competitor who made the decision you deferred. Who replaced their stack while you were running your pilot. Who is now operating at a speed and cost structure that makes your next renewal cycle a strategic event rather than an administrative one.

The irreversible act is not throwing the system out the window. The irreversible act is waiting long enough that the decision is made for you.

The discipline the principle requires

The book was not advocating chaos. It was advocating clarity. There is a difference.

Defenestration without judgement is just destruction. The principle requires that you actually answer the question: does this work well enough, given what is now available? You need intellectual honesty rather than institutional convenience. Some systems clear the bar. Some relationships, some accumulated expertise, some structural advantages are genuinely worth protecting. The principle does not say throw everything. It says stop tolerating what clearly should go.

That discipline is harder than it sounds. Because the systems that most need throwing are usually the ones most deeply embedded in how the organisation understands itself. The processes that have been in place long enough that they have become invisible. The tools so integrated into daily operations that questioning them feels like questioning the organisation’s identity.

Those are exactly the ones to start with.

The question the security head in Melbourne was really handing me, with that book, was not a technical question about Unix. It was a question about how to look at what you have built and see it clearly, not as it was when you built it, not as it represents the effort you put into it, but as it actually functions against the best available alternative today.

Twenty years later that question is harder to ask than it has ever been, because the gap between what organisations are running and what is now possible has never been wider.

And easier to answer. Because the window is right there.

The organisations that will compound

The businesses that move through this period well will not be the ones that had the best systems going into it. They will be the ones that defenestrated fastest.

Not recklessly. Deliberately. With the discipline the original principle required, clear-eyed assessment of what is no longer fit for purpose, decisive replacement with what is, and the organisational willingness to keep asking the question rather than settling into the next comfortable equilibrium.

Because the mistake the data centre administrators of the early 2000s made, the ones who read the book and agreed with it in principle but kept their legacy systems running because replacement felt hard, was not a failure of intelligence. It was a failure of pace. They understood what needed to happen. They just did not move before the window closed on them.

The window is open now. It will not stay open indefinitely.

The Unix guide to defenestration was right. It was just twenty years early on how much of what organisations do would eventually look like a process when viewed from sufficient altitude.

It does not look like the early 2000s anymore.

Ross Woodhams is the Founder and CEO of Audalize, a managed atmosphere services company operating across Australia, New Zealand, and the UK.

Then ProTools arrived. For around ten grand, you could record at home. By the early 2000s, studios that had seemed immovable were going broke. Not because their gear stopped working. Because access to professional quality stopped being scarce.

The gear did not disappear. The economic model built around controlled access to it did.

ProTools was a deprecating innovation. Not an incremental improvement on what existed. The kind of shift that does not erode the previous model gradually but makes it structurally irrelevant at the moment it arrives. The investment, the infrastructure, the pricing logic. All of it, invalidated not by a better version of the same thing but by a different thing entirely.

The Mainframe in a Chat Window

We are watching the exact same pattern play out in AI infrastructure right now. The moat is compute. Nvidia silicon. Power. Cooling. Capital at a scale only governments and hyperscalers can assemble. OpenAI, Anthropic, Google are essentially Abbey Road. You pay by the token to get in the room.

The LLM frontier is still a mainframe world dressed up in a chat interface. Impressive and genuinely useful. But fundamentally centralised, metered, and controlled by actors whose entire business model depends on the compute remaining expensive.

DeepSeek runs on consumer hardware. Llama 3 runs on a Mac. Quantisation keeps improving. The gap is closing faster than the infrastructure investment cycle can respond.

DeepSeek did not just embarrass the frontier labs on cost. It sent a signal to every CFO with a datacenter contract signed on 2023 assumptions: the floor you built on may be moving.

The Arms Race and the Efficiency Curve

The datacenters are still running hard into the capex arms race. Billions committed to infrastructure with depreciation schedules that assume the current pricing model holds for a generation.

The depreciation schedule is a fiction. What is top tier compute today is outdated compute tomorrow. The datacenter is not a one time capital commitment. It is a treadmill. You do not build it once and extract margin. You rebuild it continuously just to stay relevant and each rebuild costs more for less return.

The LLM scaling law has a diminishing returns problem that runs in the most expensive possible direction. Each marginal improvement in capability requires a disproportionately larger increase in compute. It is the same principle that killed the dream of infinite CPU scaling in PCs. Raw parallelism hits a ceiling. Each additional core contributes less than the last. The LLM scaling law has the same shape, just with the cost running in the opposite direction.

The deprecating innovation does not just disrupt the hardware cycle. It breaks the scaling relationship at the architectural level. A fundamentally more efficient model does not need to climb that curve at all. It starts somewhere else entirely.

The studios could have bought cheaper gear and stayed in business. The datacenter cannot just buy cheaper racks. The entire investment thesis is load bearing on a scaling assumption that is already showing its ceiling.

The Consumption Wheel

There is a reason Microsoft and Google build their sales motion around consumption metrics rather than seats or licences. It is the load bearing wall of the entire capital structure.

Every token consumed, every API call, every GPU hour is a metered capital event. Consumption revenue funds the next generation of hardware. The next generation of hardware requires more consumption to justify it. The sales quota is not closed deals. It is units consumed. Growth is not new customers. It is deeper consumption from existing ones.

This only works while two things remain true. That the scaling law holds. And that running a capable model requires infrastructure only a hyperscaler can provide.

When a capable model runs locally with no token bill, consumption becomes optional. The funding mechanism for the next hardware cycle evaporates. The wheel seizes.

One Innovation Away

The industry is one deprecating innovation away from a personal AI revolution. Not a better API price. A model architecture shift that hits the capability threshold where running locally stops feeling like a compromise.

When it arrives it will not look like disruption in the way disruption usually gets discussed. It will look like a category of things that did not exist before suddenly existing everywhere. The home studio did not just replace Abbey Road. It created an entire universe of artists who never would have existed inside the old model.

A sixteen year old in Perth or Nairobi with a capable model running locally, no token bill, no terms of service between their idea and execution. That is not a cheaper version of the current thing. That is a different thing entirely.

Where Does the Value Land?

The dramatic reading is that the hyperscalers die. History suggests otherwise.

The big studios did not all disappear. Some became post production houses. Some shifted into publishing. The ones that survived stopped selling access to rooms and started selling something else. The hyperscalers will do the same. Microsoft does not need API margin if Copilot is embedded in every development workflow on earth. Google does not need per token revenue if the model is what keeps you inside the ecosystem. They will vertically integrate into the application layer and the data layer, and the commoditised model in the middle becomes infrastructure they absorb rather than a margin centre they protect.

The pure play inference business is the one with nowhere to go.

In AI the durable value is data, distribution, and the application layer above the commoditised model. The companies building genuine domain expertise, genuine user relationships, genuine proprietary self reinforcing data loops, those are the ones whose moat does not evaporate when inference cost hits zero.

The value does not disappear. It moves. And the ones who built their identity around the room rather than the music are the ones who do not make it through.

The most dangerous position in any technological transition is the one that mistakes access for value. The studios had extraordinary gear. They just confused the gear with the music.

Ross Woodhams is the Founder and CEO of Audalize, a managed atmosphere services company operating across Australia, New Zealand, and the UK.

If don’t know this already, then you need your head read: Staff can make you, or they can break you.

The wrong people will stall momentum, create drama, and suck the oxygen out of your company. The right people will multiply value, lift culture, and make brilliance look effortless. I love the term “force multiplier”, because thats what the right people are. They project power (being capability, competence and certainty - all things customers love) outward. They amplify your brand.

This is my doctrine. The philosophy I live by in building teams at Audalize and UpscalarAI. It’s simple, but it’s not soft. It’s sharp, uncompromising, and built on three pillars:

1. Ownership: Own your shit. No blame. No drama.

2. Outcomes: Results matter, not optics or hours. Expect.

3. Brilliance: Demand brilliance. Build with the best.

Pillar One: Ownership

Ownership isn’t about titles, it’s about accountability. In a culture of ownership, staff don’t hide behind excuses, they don’t point fingers, and they don’t drown you in drama. They stand in the mess, they own their part, and they fix it. It also means the mindset towards mistakes when they do happen aren’t treated with punitive action.

Own Your Shit, or Move On

Excuses are easy. Ownership is rare.

I once had a staff member who was always blaming someone else. If a project stalled, it was because “so-and-so didn’t send the file.” If a deadline slipped, it was because “the process wasn’t clear.” If something broke, it was “because someone else dropped the ball.”

And here’s the pattern: the blamers are always the ones with the most drama. They stir it, feed it, and then use it as cover for why they aren’t delivering. I’ve never sacked someone for making a mistake. Mistakes are growth. You own it, you learn, you get better. Mistakes are brilliant, they are a KPI, they offer a window into reality, mistakes, like wins, should be celebrated. But I’ve sacked plenty for blaming others.

Because blame is poison. It kills trust. It corrodes accountability. It infects culture. If something’s blocked, escalate it. If you dropped the ball, own it. If you failed, admit it and fix it. That’s leadership at every level.

The rule is simple: own your shit, or move on.

The Ops Manager Story: Roles Aren’t Sacred

One day, my ops manager resigned.

Most companies would panic. They’d launch a six-month recruitment drive, stall the business, and inflate the drama. We didn’t.

Within two hours, we redistributed his entire workload across the team. Each person took a slice. They got a bump in pay, some extra stock options, and we kept moving. No drama. No sacred titles. No wasted time.

The lesson? Roles are not sacred. Outcomes are. When you build a culture of ownership, staff don’t wait around. They step up. They want the responsibility.

Ownership isn’t about position. It’s about accountability. And when staff have that, hierarchy becomes lighter, faster, more flexible.

Pillar Two: Outcomes

If ownership is the foundation, outcomes are the measure. Because in the end, only one thing matters: did you deliver?

Outcomes cut through optics, hours, and excuses. They’re measurable and quantifiable. They turn the game from looking busy to being effective.

Hire Fast, Fire Faster

Hiring is always a gamble. Resumes are marketing, interviews are theatre, references are biased. You don’t really know someone until they’re in the seat, under pressure, in your culture.

That’s why you hire fast. Don’t waste months chasing the “perfect” candidate. Put them in the chair and test the only thing that matters: can they deliver here?

Some of the best hires come from instinct. I once hired a stranger at a cafe I got talking to in a queue. Today, he’s one of our best.

And when it’s wrong? You’ll know fast, often in a week. Because the right people dive in. They swim. They engage the team, find their way, take ownership. The wrong ones hesitate, wait to be told, make excuses, and sink.

Deadweight isn’t neutral. It corrodes culture, slows decisions, drags your best people down. Keeping them isn’t kindness, it’s cruelty to them and to your team.

So act. Quickly. Cleanly. Decisively. Because if you hire fast but fire slow, you teach your team you’ll tolerate mediocrity. Fire faster, and you set the opposite tone: this is a place of clarity, accountability, and momentum.

Kill the Gunna Guys

Every founder knows them:

“I’m gunna finish that tomorrow.”

“I’m gunna call that client.”

“I’m gunna start next week.”

But gunna never comes. It’s procrastination disguised as intent. Thats putting it politely. Laziness or outright uselessness is also words that could be used.

The difference between the right people and the wrong ones is simple: the right ones dive straight in. They find a way, make a start, deliver something. The gunna guys linger at the edge, waiting for perfect conditions that never arrive.

You can build a company on mistakes, experiments, even failed bets, because at least those move you forward and you have this layer of data you took away from it. But you can’t build anything on promises that never leave the person’s mouth.

Worsestill, deadweight talkers don’t just waste their own time. They slow everyone else down, train the culture to accept delay, and poison momentum.

Execution is everything. That’s why you cut them. Quickly. Cleanly. Decisively.

Day Rate: Outcomes > Optics

At Audalize, we switched to a day rate. The reason is simple: I don’t care how long you sit at a desk, I care if you deliver. If you can crush a week’s work in two days and spend the rest at the beach sipping Pina coladas in a tall glass with the little umbrella and a slice of pineapple?, that’s fine. You delivered.

The old model rewards optics. People collect points for staying late, looking stressed, racking up hours. They look busy, so they’re seen as valuable. But busyness isn’t business.

It’s one of two things:

1. Inefficiency: They can’t focus, can’t prioritise, can’t say no.

2. Overloading: They’re carrying broken systems, bad hires, or other people’s slack.

Neither is good. Neither builds a sustainable business.

When you reward outcomes instead of optics, the truth surfaces quickly. The efficient thrive. The overloaded get relief because inefficiency is exposed. And the excuse makers have nowhere to hide.

That’s the point. Hours don’t matter. Optics don’t matter. Outcomes do. Always.

Pillar Three: Brilliance

Adequacy is expensive. Brilliance compounds. It is a force multiplier. I can honestly say every staff member at Audalize gets up in the morning and pisses excellence.

When you hire brilliance, it raises the bar. It attracts more brilliance. It compounds into a culture where excellence is the baseline. You don’t get competitiveness. Just sheer brilliance.

Everyone a Shareholder

The fastest way to kill apathy? Give staff skin in the game.

When people are shareholders, they stop thinking like employees and start thinking like owners. They don’t just protect their patch, they think about the whole field.

And we’re not talking token shareholding, a few crumbs thrown for optics. Real ownership means meaningful upside: 50 to 100% of their base pay, locked in and growing over time.

At Audalize and UpscalarAI, a team member starting on $150k base might get $150k in stock. They’ll vest $75k in 12 months and another $75k in 24 months. It’s locked, it’s part of the package, and it means their success is tied directly to the company’s success.

That shift changes everything. Suddenly, “that’s not my job” disappears. It becomes, “how do we win?” Because when everyone has skin in the game, the game belongs to everyone.

Ownership kills apathy. It fuels urgency. It aligns ambition.

Expect Brilliance, Not Adequacy

Too many leaders settle. They hire “good enough” and then spend years compensating for mediocrity. The government is full of it, entire departments staffed with people who tick boxes but never move the needle. Large enterprise is no different. Layers of “adequate” staff, producing adequate results, slowing everything to a crawl.

Adequacy is a tax on meh!. It drains productivity, breeds bureaucracy, and drags culture down to the lowest common denominator.

Brilliance, on the other hand, compounds. One brilliant hire changes the game. They raise standards, inspire others, and make the impossible look normal. That’s why demanding brilliance should be the baseline, not the bonus. Don’t apologise for expecting it. Don’t water down the standard to make people comfortable.

Set the bar high. Watch how the right people rise to meet it and how the wrong ones quickly self-select out.

Meritocracy Wins

Hire, reward, and promote on merit. Not on gender. Not on colour. Not on persuasion. On merit.

Because merit can be measured. Outcomes can be measured. Contribution can be measured. Woowoo can’t be measured.

Too many organisations get tangled in politics, quotas, or appearances. They promote people because they “fit the profile” or tick a box, not because they deliver. The result? Culture rots. High performers leave. Mediocrity thrives.

A true meritocracy is ruthless but fair. The only currency is results. The only question is: did you deliver?

That doesn’t mean sameness. In fact, meritocracy drives diversity, but the right kind. Not diversity by optics, but diversity by brilliance. Different backgrounds, different voices and different minds all united by one measure: contribution.

When you hire, reward, and promote on merit, you build trust. Staff know the game isn’t rigged. They know politics won’t beat performance. They know effort, skill, and outcomes are the only way forward.

Meritocracy is clarity. And clarity builds culture.

Wood, Not Diamonds

We often say this at Audalize: most business music is a diamond. Hard, polished, forgettable. It glitters, but it doesn’t glow.

Wood, on the other hand, carries scars, story, warmth. It makes you feel.

Staff are the same. Shiny CVs are diamonds, all glitter, no glow. The best people are wood. They’ve got story, resilience, and soul.

Hire wood, not diamonds.

Culture is the Invisible Architecture

Culture is to a company what music is to a venue: invisible architecture.

It shapes mood. It sets rhythm. It defines experience.

You don’t always see it, but you always feel it.

Culture isn’t built by accident. It’s curated. Like a DJ curating a vibe, you set it intentionally, relentlessly.

That’s leadership.

Closing: The Law of Three

This shit to me isnt hard. It’s self evident, which makes it a universal law.

• Ownership: Own your shit. No blame. No drama.

• Outcomes: Results matter, not optics or hours.

• Brilliance: Expect brilliance. Build with the best.

That’s the doctrine. Not advice. Not theory. A law. Follow it, and you don’t just build staff. You build a team. And teams build companies.

For many, this seems like progress. But for those who create, who listen, who still feel, it raises deeper questions.

How did we get here, and what have we lost?

Let’s begin with music. Once a sacred art form that demanded patience and intimacy, music today is consumed with the flick of a finger. I remember saving to buy a single album. I remember the ritual: opening the sleeve, studying the lyrics, listening, not once, but over and over until the songs lived in me. That experience has become rare.

Streaming offered us access. But it took away attention. Now, with AI, even the effort behind a song is suspect. You write something honest, something earned, and someone shrugs: “It’s probably AI.”

This is not simply a technological shift. It is an existential one.

Albert Camus once wrote that man is the only creature who refuses to be what he is. That refusal, the restless push to transcend, to innovate, is what gives birth to meaning, but also absurdity. We now live in a world where machines simulate our refusal. They write our poems, paint our dreams, compose our songs. And in doing so, they blur the very line between creation and replication.

But what they cannot do is suffer. Or yearn. Or remember.

In Camus’ world, meaning was not something given, it was something made, in defiance of a silent universe. And so, too, in this new era, we are called not to compete with machines, but to reclaim our own defiant humanity.

Last week, Audalize held an INXS event for the 40th anniversary of Listen Like Thieves. No filters. No AI. Just music, memory, and people. They weren’t glued to screens. They were present. They engaged. They remembered what it felt like to be part of something real. That moment mattered not because it was rare, but because it was true.

There are three ways forward:

First, we can continue down the path of scale of infinite content, frictionless production, indistinguishable experience. Everything becomes cheaper. And emptier.

Second, we can attempt to out AI the AI by building better tools, faster systems, smarter curation. But in doing so, we risk becoming the very thing we fear: efficient, optimised, and emotionally hollow.

The third way is quieter. It is harder. It will not scale easily. But it is real.

It is the choice to create what cannot be faked. To tell stories only you could tell. To make art that carries the imprint of a life, not just a prompt.

Most won’t notice. Some will dismiss it. But a few, the ones who still feel, they will know. And they will stay. And in that connection, something sacred returns.

We don’t need to save the world from AI. We need to save our selves from forgetting what it means to be human.

Peace, and stay foxy.
Ross

We’ve seen the gimmicks, the deepfake Drake tracks, AI-generated lo-fi playlists, “virtual” artists in Spotify with hundreds of thousands of plays. None of that is the real threat. Those are just warm-up acts.

The real disruption, the one that changes everything, is still coming. And when it hits, it won’t feel like a revolution. It’ll feel like comfort.

Here’s what it will look like:

And now we meet in an abandoned studio…

You sign up for a new app. It asks for your favorite songs, artists, memories, moods. You fill in a few fields. Maybe connect your Spotify. Maybe answer a few psychological prompts. You think you’re giving it preferences.

What you’re actually handing over is your emotional fingerprint. The system spins for a moment.

Then… a world appears. Not a playlist. A universe.

Entire bands generated just for you. Names you’ve never heard because they didn’t exist until sixty seconds ago. Custom profile pictures. AI-crafted biographies. A fully-formed aesthetic.

And the music? It’s perfect. Too perfect. A blend of everything you love. The voice timbre that gives you chills. The tempo that matches your heartbeat. Lyrics that sound like they were pulled straight from the hardest night of your life. It’s like discovering your favorite band, over and over again.

Only one problem: None of it is real. No studio. No conflict. No story. No blood in the ink. Just frictionless content, built in real time, for one person: you.

Rewritten by machine on new technology...

You start liking songs. The system adapts. It introduces new “bands”, each more dialed in than the last. You build a relationship with these artists. You follow them. You feel connected. But you’re not discovering. You’re training a simulation.

You’re not joining a fandom. You’re wandering in a garden built by code, for an audience of one. No licensing. No royalties. No egos. No delays. Just infinite content with zero cost and 100% retention. It’s everything the industry has ever wanted.

And yet…

Pictures came and broke your heart…

What happens when your favorite artist only exists for you? What happens when there are no more breakout albums? No more anthems of a generation? No more world tours, autograph lines, or interviews that go off the rails? What happens when every listener is sealed inside their own sonic echo chamber?

This is the death of shared meaning.

This is the death of the messy, flawed, communal human art form we once called music. This isn’t about AI replacing musicians. This is about AI making audiences forget they ever needed them. You won’t need connection.You won’t need story.

You’ll just get what you want. Instantly. Infinitely. Passively. And most people will love it.

We can’t rewind, we’ve gone too far…

Make no mistake, the business model is irresistible.

• Unlimited music, tuned to the listener’s brain.

• No licensing headaches.

• No risk of scandal.

• No arguments over royalties.

• No shelf life. No fatigue.

Just content that feels like art…without any of the trouble it takes to make it.

But here’s the cost:

When the only songs that move you were built by something that can’t feel… what happens to you?

Art is the negotiation between the creator and the audience. It lives in the space between what you meant and what I needed. It’s flawed. Slow. Irrational. But that’s exactly what makes it human.

Remove the artist and you remove the humanity. Remove the humanity and you remove the meaning.

And you remember the jungles used to go…

This future is inevitable. And it will be popular. But beneath the surface, a hunger will grow. A hunger for friction. For surprise. For something that wasn’t made for us and still moves us. Real art will become a counterculture. Human-created music won’t be “mainstream.” It’ll be an act of rebellion.

The new punk won’t be loud, it’ll be real.

And when that happens, when the pendulum swings back, it won’t be because AI failed.

It’ll be because we finally remembered:

We don’t need more songs. We need something to believe in.

Until then, enjoy the perfect track. Just know: it was never sung for you.

Fast forward three decades and everyone’s talking about generative AI. They’re calling it a bubble. They say it costs too much, burns too hot, and lacks a real business model. But I don’t buy it. This isn’t a dead end. It’s just a story we’ve seen before. Technology that arrives too early is expensive. Until it isn't.

The Bubble Fallacy

We love the word "bubble." It lets us feel smart without taking risk. Dotcom. Crypto. Now, generative AI. The pattern is predictable: overhype, overspend, underdeliver. But not all hype is equal. Tulips didn’t change the world. Tokens won’t cook your dinner. But language models? They’re already writing scripts, debugging code, generating music, and redesigning workflows.

So what’s the real problem? It’s not value. It’s cost.

Compute as Constraint

Generative AI today is like trying to build a skyscraper with bricks made of gold. It’s technically possible. But economically, it’s insanity.

Training GPT-4 reportedly cost over \$100 million. Inference, just running the model, costs real money every time someone hits "submit." And we’re doing it millions of times per day.

Right now, the tech is centralised, cloud dependent, and compute hungry. But compute cost isn’t static. It’s the one thing that always drops. Moore’s Law might be slowing, but architectural innovation is just getting started. The future of AI isn’t server farms; it’s edge chips that sip power and infer at the speed of thought.

Lessons from Low Poly

Anyone who thinks today’s AI is a failure has forgotten what the future used to look like. In the '90s, VR was nausea in headset form. 3D games were jagged, slow, and barely immersive. Anyone remember F-15 Strike Eagle by Microprose? Hills were empty pyramids and tanks, just boxes. And yet, we saw the promise.

Every great technology wave starts clumsy. It moves awkwardly. It fails publicly. Then, quietly, it becomes inevitable. What mattered in 1994 wasn’t the polygons. It was the paradigm shift. Today, we’re in the same place with AI. This isn’t a crash. It’s an awkward adolescence.

The Real Business Model Isn't Here Yet

Most people misread technological revolutions by judging them too early through the lens of old business models. We saw this with mobile. The early apps were gimmicks. The real models like Uber, Instagram and TikTok emerged only after the hardware, networks, and behaviour matured.

Right now, AI is stuck in per token pricing, prompt engineering packages, and SaaS wrappers. But the real shift will come when AI becomes ambient. Embedded. Local. Invisible.

Imagine a future where:

· Your lighting system infers your mood.

· Your car DJ generates tracks to regulate your cortisol.

· Your fridge predicts your emotional eating pattern.

That’s not a feature set. That’s an operating system for life. And it’s only possible when compute is cheap, inference is local, and intelligence is ambient.

We’re Not in a Bubble. We’re in the Basement.

Everyone sees a pop. I see a crawlspace. The tech isn’t in its peak. It’s barely in it’s infancy. The economics aren’t ready. But the trajectory? Unstoppable.

The mistake isn’t overhyping the potential. It’s underestimating the timeline. Give it ten years. Let the chips evolve. Let the costs drop. When that happens, generative AI won’t just disrupt. It’ll disappear into everything.

Just like it was always meant to.

The Teenager Test

It seems far-fetched now but only because history's acceleration hides in plain sight. A 90MHz Pentium struggled to play a single MP3. Today, a sub $100 mini-pc streams 4K video with lossless audio while drawing less than five watts.

That same curve will bend for AI.

In a few years, teenagers won't just be scrolling TikTok. They'll be crafting entire music videos from scratch: choreography, beats, lyrics, visuals all generated, directed, and refined by AI models running locally on laptops. No cloud. No server bill. Just raw creativity, unthrottled by infrastructure.

The future isn’t some sci-fi dream. It’s a teenager in their bedroom, rendering the extraordinary with tools we haven't even priced yet.

Ross, signing off. Still seeing pyramids. Still seeing the future.

Let’s be honest. Most B2B marketing looks like a toddler yelling into a megaphone. On fire. While juggling LinkedIn carousels.

The logic goes something like this: “No one knows who we are, so let’s spam the internet into submission.” And for B2C, that kind of brute-force awareness works just fine. If you’re selling a new coffee brand or yet another DTC pillow, sure…just keep throwing ads at people until they click out of boredom or existential crisis.

But B2B? Whole different game. You can’t shout your way into someone’s infrastructure.

In B2C, network effects are elegant. You start with two users: a buyer and a seller. They interact. You add a third. A fourth. Suddenly, you’re scaling like wildfire, raising Series B money to hire people who’ll spend half their week “syncing” and the other half planning a launch party in Mykonos.

But in B2B? Try plugging two CFOs into a Slack channel and calling it a platform. You’ll be laughed out of the boardroom, politely, by someone who bills $500 an hour to tell you you’re wrong.

The real win in B2B isn’t being added to the stack. It’s being the thing that makes the stack work.

The goal isn’t to sit on top of the business like some cloud-based cherry. It’s to vanish so deep into the business that if someone tried to remove you, they’d have to shut down operations and sacrifice a goat.

The kind of presence that’s not flashy, but fundamental. Like electricity. Or oxygen. Or that one guy in IT who knows where the passwords are.

The best B2B platforms are boring in the best possible way. They’re not loud. They’re not “disruptive.” They’re so ingrained that no one even questions them. They are the operating logic, the invisible mesh holding it all together. And once they’re in, removing them is like asking, “What if we just… didn’t use the internet anymore?”

Here’s the twist: getting to that point has nothing to do with how clever your tagline is. It’s about trust. Integration. Relevance. Being in so many workflows that uninstalling your product would require a 37-tab Notion doc, three all-hands meetings, and a week of mourning.

That’s the real network effect in B2B. Not just more users. More interdependence. More “If we rip this out, someone will cry.”

So while everyone else is optimizing their CTA buttons for marginal gains, I’m over here thinking: what if we just made something people couldn’t run their business without?

That’s the play. Not to be seen. To be assumed.

Are you chasing visibility? Or designing inevitability?

Happy to trade war stories.

Ross is CEO and Founder of Audalize, the company helping venues sound unforgettable. Learn more at audalize.com.

There’s a lot of hype right now about AI music. Platforms like Suno and Udio can write entire songs in seconds. Other systems, used by mass-market background music providers claim they can build the perfect playlist using “AI-powered curation.”

And yes, it’s clever. It’s fast. It’s scalable.

It also completely misses the point.

Because while these systems can assemble music, recommend tracks, and even generate melodies… they don’t understand music at all.

What AI Music Curation Actually Does

Let’s pull back the covers.

Whether it’s generating a playlist or writing an original track, AI music systems are built on the same principles:

1. Train a model on massive datasets of audio, tags, and listener behaviour

2. Turn those songs into data (tempo, genre, valence, skip rate, etc.)

3. Use statistical patterns to predict what comes next

It’s not curating. It’s not composing. It’s predicting.

When AI builds a playlist, it’s not asking, “What’s the emotional journey here?” It’s asking, “What other songs share similar metadata?”

It sees music as numbers in a spreadsheet:

• BPM: 90

• Valence: 0.8

• Energy: 0.6

• Genre: chill-electronic-pop-with-male-vocal

It doesn’t know why that track makes you feel something. It just knows other people didn’t skip it. That’s not music intelligence. That’s audio autocomplete.

A Quick Experiment That Tells You Everything

A few years ago, I ran a simple experiment using the Spotify API.

I searched for playlists containing a phrase, like “Two for Dinner” and scraped every track uri across every one. Each time a song showed up, it scored a point. The more lists it appeared in, the higher its rank.

I ran this on thousands of playlists. The result? A single, massive playlist with over 2,500 songs ranked by cultural consensus.

And the top 100 or so?

Unreal. Nina Simone. Nat King Cole. Sinatra. Sade. Even a bit of Lionel Ritchie made the cut. Warm, nostalgic, textured… exactly what the phrase “Two for Dinner” should feel like.

Further down? You start to get fringe picks. Sade followed by John Mayer, then a weirdly confident bossa nova version of “Careless Whisper.” Not wrong. Just… bold. But still makes emotional sense.

And then, right at the bottom? Absolute chaos. Tracks that sound like someone fed an AI “romantic dinner music” and it returned a dubstep remix of Slayer recorded on a potato. A reminder that humans are beautifully, bafflingly inconsistent.

And that’s the difference.

Every one of those playlists was made by a person. There was intent. Flawed, messy, brilliant intent. That intent gave the whole thing structure, even if it drifted.

AI Doesn’t Do Intent

AI doesn’t know what “Two for Dinner” means. It doesn’t know time of day. Or lighting. Or whether the wine’s open or still in the bottle.

It doesn’t ask, “What fits here emotionally?” It asks, “What’s statistically similar to other tracks in this cluster?”

That’s why most AI-powered playlists feel hollow. They’re not wrong. But they’re not right, either. They’re optimised for consistency, not connection.

The Risk of Optimising for the Average

What happens when background music becomes machine-chosen? You get what’s statistically safest. You get playlists built on collective meh.

You get spaces that sound like everyone else, because they’ve all bought into the same automated logic.

This is what’s happening in background music right now.

Mass-market platforms are automating curation. They’re building music experiences using models trained on Spotify skip data, genre tags, and popularity scores.

It’s neat. It’s scalable. And it’s utterly soulless. Because in the end, these models don’t know music.

They just know data that once came from music.

Ross is CEO and Founder of Audalize, the company helping venues sound unforgettable. Learn more at audalize.com.

Every high street is visually different, yet sonically identical.

Step into any bar, café, or retail space and you’ll encounter the same generic background music: inoffensive, indistinct, forgettable.

This isn’t a coincidence. It’s a system failure. And it’s costing you differentiation, memorability, and ultimately profit.

The Hidden Cost of Mass-Market Background Music

Most venues use mass-market background music (BGM) services. These are built for scale, not distinction. Their goal is to serve the largest number of businesses with the least friction.

In other words: commoditisation.

When you use the same music platform as 10,000 other businesses, your venue becomes indistinct by design. You may think you’re saving time or cost, but the real price is that you’ve surrendered your most emotionally powerful brand lever to a vendor who doesn’t know or care who you are.

And if your customer can’t tell you apart from the venue next door, you’ve already lost.

Differentiation Is Not Optional

In business, there are two viable positions: monopoly or commodity.

A monopoly owns a space. It has a clear identity, a distinct proposition, and pricing power. A commodity competes on convenience and cost, and dies by it.

Most venues have invested heavily in physical differentiation: architecture, menus, design, lighting and even beverage selection. But very few have considered sonic differentiation, despite music being the most persistent and emotionally resonant layer of the customer experience.

The result?

A beautifully designed space that sounds like everywhere else and from a brand standpoint, this is strategic malpractice.

Music Is a Strategic Asset

Music isn’t decoration. It’s infrastructure.

It influences customer mood, perception, dwell time, and spend. And unlike a new bar top or light fitting, it operates continuously. It’s the only part of your brand that runs 100% of your open hours.

To treat it like a background detail is to ignore one of your most powerful tools for shaping behaviour and building loyalty.

The brands that understand this will create experiences that aren’t just visited, they’re remembered.

The Opportunity: Own the Feeling

Every space communicates something, whether intentionally or not. Most mass-market BGM services reduce that communication to neutrality.

But neutrality isn’t a brand. It’s an absence.

The opportunity is to own the feeling in your space. Not just by what people see, but by what they hear and feel the moment they walk in.

Sound is the last great unclaimed territory in hospitality branding.

And right now, it’s being wasted.

So…what’s the point?

If you’ve invested in creating a venue with unique visual and culinary identity, but you’re outsourcing your music to a generic system, then you’ve created a brand with a strategic void at its centre.

The answer isn’t more volume or more tracks. It’s better curation, aligned with intent, behaviour, and emotional impact.

The venues that realise this early will own the future.

The rest will fade into background noise just like their music.

Ross is CEO and Founder of Audalize, the company helping venues sound unforgettable. Learn more at audalize.com.

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