There is a particular kind of fatigue that only LinkedIn can produce.

It is not the loud kind. It is not even offensive most of the time. It is worse than that. It is a soft, padded, endlessly looping trade show floor where everyone is smiling just a little too hard and speaking in complete sentences that somehow contain no content. You scroll past declarations of gratitude, lessons learned, pivots embraced, and journeys honored. Everyone is either “humbled” or “excited” or “thrilled,” often all three at once, which must be exhausting.

And then, occasionally, there is Chris Hadfield.

He does not arrive with a trumpet. He does not announce a framework. He does not tell you he is about to share something “important.”

He just… says something.

A photo of a tool. A short explanation of how something works. A quiet observation about orbit, or physics, or practice. Sometimes a reminder that if you let go of something in space, it does not fall, it just stays there, which is both obvious and oddly profound when stated without ceremony.

There is no performance in it. No attempt to position himself as a thought leader. Which is interesting, because if anyone has earned the right to stand on a digital soapbox and explain the universe to the rest of us, it is probably the guy who commanded the International Space Station.

But he does not do that. Instead, he writes like a person who has done things. Experience that has settled into clarity. Most LinkedIn content feels like it is written in anticipation of approval. His feels like it is written after understanding.

He will explain how astronauts train for simple tasks, and you realize that what looks like talent is mostly repetition under constraint. He will show you a picture of Earth from orbit and not try to turn it into a metaphor for leadership. He will just let it be what it is, which turns out to be more powerful than any metaphor anyway.

What makes it even better is that his posts are short. Not performatively short. Not “ten lessons I learned from orbit” short. Just… appropriately sized. He says the thing, and then he stops. Which on LinkedIn feels like watching someone stand up from a table and leave after finishing their meal, while everyone else is still explaining the menu to each other.

There is a quiet confidence in that restraint. And maybe that is the real point. LinkedIn is full of people trying to demonstrate that they know. Hadfield demonstrates that he understands. You scroll, you read, you think, “oh right, this is what a clear thought looks like,” and then you continue on into the fog of inspirational anecdotes and career gratitude essays.

It is not that the rest is malicious. It is just… inflated. Like balloon animals, carefully twisted into shape, colorful, technically impressive in their own way, and suspiciously temporary. Hadfield brings a wrench. Not metaphorically. Sometimes literally. And in a place full of balloons, a wrench is refreshing.

NVIDIA, which supplies much of the compute behind modern AI, is making a clear bet. A meaningful share of AI inference will not stay in the cloud. It will move closer to where decisions are made, running locally, in real time, inside the environments where the output actually matters.

At first glance, it looks like a hardware cycle. Faster chips, tighter systems, more performance at the edge. But the bet is not really about performance. It is about where AI lives, and how it behaves when it gets there.

Before getting into that, it is worth being precise about what kind of AI we are actually deploying today. Most AI systems in production are probabilistic. Large models, deep learning systems, pattern recognizers. They produce outputs that are best understood as likely, not certain. That is their strength. They generalize. They handle ambiguity. They interpolate across messy real-world inputs.

They are also, by design, not deterministic in the traditional engineering sense. Given the same input, they may produce slightly different outputs. Their internal pathways are not fully inspectable or fixed in the way classical systems are. At the same time, these models absolutely influence performance characteristics. Model size, architecture, and optimization determine how fast inference runs, how much compute is required, and how stable latency appears under normal conditions.

But there is an important boundary.

A model can be fast. It cannot, on its own, guarantee bounded execution, consistent latency under load, or predictable failure behaviour. Those guarantees come from the system it runs in. The distinction did not matter much when AI lived comfortably in the cloud. Most applications could tolerate variable latency, retries, and loose timing. If a response took longer than expected, nothing broke.

It starts to matter when the value of the AI is tied to a moment.

Take a surgical robot. The system is ingesting imaging, sensor data, and feedback in real time. If AI is assisting with positioning or interpretation, the output cannot arrive late, and the system cannot stall or jitter unpredictably under load.

Or consider intraoperative imaging. If AI is guiding acquisition or flagging anomalies during a scan, the value exists only if the feedback is immediate and consistent. A delayed answer is not just less useful. It is irrelevant.

In these environments, the problem is not just model accuracy. It is system behavior.

This is where platforms like IGX Thor come in. To be clear, they do not make models deterministic. They make the execution of those models more predictable: Inference completes within a defined window, latency is bounded, the system does not depend on a network round trip and failure modes are known and controlled. While the model remains probabilistic, the system becomes auditable, constrained, and reliable in how it operates.

That is the shift NVIDIA is signalling.

For the past decade, we have assumed that AI belongs in the cloud. That assumption holds for training, aggregation, and any workflow where timing is flexible. But if the value of the system is tied to real time decision making, continuous signals, or embedded workflows, the cloud is the wrong shape. You cannot afford the trip, and you cannot accept variability in execution.

The practical outcome is not that everything moves to the edge. It is that a meaningful class of applications cannot stay in the cloud.

And those applications will have a second requirement.

It will not be enough for the model to be generally good. It will need to be well characterized, constrained to a defined task, and supported by an execution environment that is predictable, testable, and auditable. In other words, moving inference to the edge does not relax the requirements on AI systems. It tightens them. So this is not just a hardware story. It is a signal about the next phase of AI deployment.

Cloud based, probabilistic systems will continue to dominate where flexibility and scale matter most. Alongside them, a smaller but critical class of systems will emerge where timing, behaviour, and reliability are part of the product itself.

And for those systems, intelligence is only half the problem. The other half is how it behaves when it counts

General intelligence is impressive, timely intelligence is often more useful. Lighthouses have been doing that from the edge for centuries. Moonlight, Coast of Tuscany is a 1790 landscape painting by the British artist Joseph Wright of Derby.

Printers occupy a strange place in modern life. They are devices that almost never work properly, yet the entire world behaves as if they do. Somewhere in a product brochure a printer is depicted calmly producing page after page of perfect documents while a smiling professional nods approvingly. In the real world the printer is blinking a hieroglyphic error code, claiming it is out of cyan even though you are printing in black, and insisting that the paper tray is both empty and jammed at the same time. This is not a rare failure; it is the standard operating condition. How has it come to this and why as a society did we allow this to happen?

The underlying reason is economic theatre; printers are not built to work. They are built to exist long enough to sell ink and replacement units. If a printer truly worked like a toaster or a kettle, you would buy one every ten-year years and forget about the category entirely. Instead we live in a cycle of ritual troubleshooting, restarting, cleaning the heads, align the cartridges, download drivers, reinstalling drivers, updating firmware and that dreaded thing where the printer has simply taken a vacation from being part of the WIFI network and can’t be reached right now. The industry has discovered a remarkable business model: Sell a machine that functions just well enough that people blame themselves when it fails.

A Stoic philosopher would approach the printer differently. Marcus Aurelius would remind us that the printer is not ours to command, only our reaction to it. Epictetus would calmly note that the printer belongs in the category of things outside our control, alongside weather, politics, and other people’s opinions. Seneca might advise that one should rehearse adversity in advance and therefore assume from the beginning that the printer will not work. In this light the printer becomes not a device but a training instrument for character: The blinking error light is a lesson in detachment, the paper jam is a meditation on impermanence, the printer-not-found is an invitation to cultivate patience. Of course the Stoics did not have school permission slips that must be printed and signed at eleven at night, so their philosophy might soften slightly when confronted with modern bureaucracy.

Which brings us to the final Stoic conclusion. The rational answer is obvious: do not own a printer. I tried, but the world is not yet designed for that reality. A form appears that demands wet ink as if we are still operating a colonial trading post, a kid’s school project requires printed images of volcanoes, a government document refuses to acknowledge the existence of electrons. So we keep the printer in the corner like an unreliable relative. We know it will disappoint us, we know it will waste our time. The Stoic solution, then, is not to expect it to work.

And, at some point, the realization dawns that the printer industry solved its problem perfectly; the machine does not need to work, it only needs to exist.

Anyone who has spent time building a company eventually encounters a curious phenomenon that does not appear in most business textbooks. It is not a financial metric or an operational KPI. It is better understood as an empirically characterized psychological instrument. I call this phenomenon the Executive Delusion Oscillator. Despite its widespread observability and measurable consequences, it remains absent from most business school curricula.

Reality in a growing company tends to move in a rather unremarkable way. Progress is uneven but generally upward. Problems appear, are addressed, and eventually resolved. If plotted on a chart, reality would look like a modest to decent rising line.

Perception behaves differently.

Perception oscillates around reality. At one moment the company appears destined for inevitable success. Shortly thereafter the very same set of facts suggests looming catastrophe. These swings rarely correspond to meaningful changes in the underlying trajectory. They are interpretations, often emotional, applied to incomplete information.

Early in a company’s life the oscillations are small and fast. A promising conversation may produce a burst of confidence that lasts an afternoon. A difficult meeting may produce the opposite reaction before dinner. Fortunately the stakes remain modest and the consequences of misinterpretation are contained.

As the company grows, something interesting happens.

Capital accumulates, expectations rise, careers, reputations, and investor capital become tied to the outcome. At this stage the amplitude of the oscillations begins to increase dramatically. A small positive signal becomes evidence that unstoppable success is now inevitable. Leadership metaphorically puts on sunglasses and begins counting money that has not yet arrived. The opposite swing is equally familiar. A setback produces existential dread and the entire enterprise suddenly appears doomed. Emergency strategy meetings appear, investors begin to bristle, and the CFO quietly begins documenting that this was not their idea.

Neither conclusion is usually correct.

Reality continues along its slow and stubborn path while perception swings wildly above and below it. Empirically, oscillation amplitude can be described as the Executive Delusion Index (Dₑ), though measurement techniques remain unreliable.

Integrating the oscillator across time yields the Cumulative Delusion Load (CDL), a quantity that appears strongly correlated with emergency strategy meetings, late night Slack threads, and the quiet creation of a new “scenario analysis” tab in the financial model:

CDL(T) = ∫₀ᵀ |Dₑ(t)| dt

where

Dₑ(t) = Executive Delusion Index at time t
T = total time horizon of the venture

since

CDL = ∫₀ᵀ |P(t) − R(t)| dt

where

P(t) = perceived momentum
R(t) = actual progress

Interpretation:

CDL represents the Cumulative Delusion Load, defined as the total organizational load generated by the integrated divergence between fantasy and reality over the life of the venture. Or more simply:

CDL ≈ Total accumulated executive wrongness.

It is further observed that

CDL(t) ∝ Dₑ(t)² for t ∈ F

where F represents intervals coincident with late-stage funding rounds. Empirical measurement remains confounded by optimism bias.

Finally, we define the Point of Maximum Delusion, PMD, as the time t* within F at which executive delusion reaches a local maximum.

Formally,

t* = arg max Dₑ(t), for t ∈ F

A necessary condition is

dDₑ(t)/dt |_(t=t*) = 0

and a sufficient second order condition for a local maximum is

d²Dₑ(t)/dt² |_(t=t*) < 0

Therefore, the Point of Maximum Delusion occurs at the critical point during the funding interval where the Executive Delusion Index ceases increasing and begins to decline.

Equivalently,

PMD = Dₑ(t*) = max {Dₑ(t) : t ∈ F}

Interpretation

The PMD represents the moment during at which executive perception is most completely decoupled from reality.

Operationally, this is often observed shortly after the largest valuation number is spoken aloud, but before any corresponding operational miracle has taken place.

Common empirical indicators include:

• the phrase “this changes everything”

• headcount plans that assume frictionless execution

• references to inevitable category leadership

• investors suddenly becoming “strategic”

• a CFO opening a new tab titled scenario upside

Informally

PMD ≈ the precise moment everyone starts acting as though the money has already turned into success.

Experienced operators eventually learn to recognize the pattern. The peaks are rarely as real as they feel, and the troughs rarely represent the end of the story. The task is not to eliminate the oscillator, which appears to be a permanent feature of ambitious undertakings, but to avoid steering the company based on its most dramatic swings. In practice, progress tends to occur somewhere near the middle line, where the story is less exciting but considerably more accurate.

Lately I have been seeing a particular kind of enthusiasm show up in marketing conversations. A company announces it has replaced hundreds of thousands of dollars in content spend with AI agents. Ten agents, or twenty, working around the clock, creating variations, repurposing content, niching, translating, localizing. A 24-hour content engine pushing thousands of posts a day. It’s cool and it smells like progress. But what rarely gets asked is a simpler question:

If the content is synthetic, what do we assume about the product?

I work for a company whose core is heavily centered on machine learning, so this is not a moral objection to AI in communication; it is a perceptual one. Humans have always made unconscious judgments about credibility based on the appearance of effort. Not effort as pure aesthetics, but effort constrained by reality, time, cost, risk, and tradeoffs. Historically, production value functioned as a costly signal. Not because expensive meant true, but because expense implied commitment. You could fake sincerity, but you could not fake effort cheaply, and that friction filtered out a lot of nonsense.

In the early days of syndicated television advertising, audiences learned very quickly to sense mismatches. When the promise outpaced the perceived investment, it registered as off. Those commercials did not just fail, they became jokes. Recurring fake commercial sketches on SNL were built around this exact tension: overconfident claims delivered with just enough polish to be aired, but not enough reality behind them to be believed. People laughed because they instantly recognized the gap; they always have.

Today, when someone profiles the value of the content engine that never sleeps, what I hear is a voice that is everywhere, and when a voice is everywhere, responsibility becomes difficult to locate. What does that do for a brand? I suppose this is fine in some categories, where authenticity is not the product and consumers are content to assume the product itself is disposable, although I am not entirely sure where that boundary sits.

The line becomes clear when trust is cumulative, as in healthcare, finance, infrastructure, education, or anything involving risk, judgment, or accountability. Here, communication is not just marketing; it is a proxy for responsibility. When something goes wrong, someone has to answer for it, and people sense whether that accountability exists long before it is tested.

In this context something like an explainer video, well-crafted with AI, feels pretty acceptable. They are instrumental, they clarify, they orient and get out of thew way. As a product company, we are grateful that we do not need to spend hundreds of thousands of dollars here and can instead put that investment into product quality and support.

But volumized hype content does the opposite. It asks for belief without proportional investment or risk. That has never worked particularly well in the past, and there is little reason to believe it will start working now. The deeper point is that authenticity does not need to mean human made; we should absolutely use the tools available to us. But authenticity does require obvious effort, specifically effort constrained by reality. Constraints leave fingerprints, and those fingerprints cognitively signal responsibility to an audience.

In medical technology, responsibility is far more than a brand position; it is operational. We are audited on it. At Sparrow, we invested in it long before we had a product, through quality systems, documentation, traceability, and review. Good science, done properly, with real people standing behind it. That posture cannot be bolted on later, and I am not sure it can be convincingly simulated at scale.

I am glad we do not need to spend huge sums on explainer videos just to look legitimate. Looking good should always be in service of getting it right, not the other way around. The mistake is believing that the pursuit of frictionless communication comes without loss. When claims are easy to generate and endlessly repeated, accountability becomes harder to locate. In domains where failure has consequences, people instinctively look for signs that someone is prepared to stand behind what is being said.

Trust needs to live somewhere.

Speaking for myself, over the past few weeks I have seen a sharp increase in cold emails, newsletters I did not subscribe to, targeted ads, and even, somehow, cold calls. I have always respected good sales efforts, and I genuinely feel for marketers and salespeople trying to push real signal through an increasingly hostile environment. But this morning took the cake. A record number of personalized intrusions arrived more or less at once.

Instead of impatiently swiping past them, I gave myself a few minutes to be curious, I read each one, I even answered a cold call (gasp!). This was not with intent to engage, but to see if anything could break through to me, or if the entire exercise had become self-contained. I wanted to take stock, to see whether the current state of messaging could move me at all.

They all told me the same thing: I apparently have a problem. They are the solution. And we need to talk now. A few even reminded me that “as CEO at Sparrow Bioacoustics, it falls to me to ensure the problem is solved”

None of it got through. None of it changed anything.

To be clear, like every company, we do have problems to solve. They are addressed in order of worthiness and impact, as resources allow. But this style of outreach does not affect that ordering, the timing, or the decisions. It simply does not intersect with how buying actually happens for us.

More than a decade ago at PathFactory, I wrote “The Attention Economy”, a paper and talk track about getting attention, holding it, and extracting signal from it. That idea still holds, and PathFactory still does a great job executing on it. But something has shifted since then, and I suspect things now feel far stranger for sales and marketing than they did at the time.

AI collapses the cost of content to near zero. When anyone can produce unlimited words, images, and personalized messages, content stops being a differentiator. The constraint is no longer production. It is credible attention, trust, relevance, and timing. Advertising does not die because people stop seeing ads; it dies because people stop attributing meaning to them. Outbound sales follow the same trajectory. Cold email was already decaying; AI simply accelerates the process. When everyone can send ten thousand customized messages, customization becomes meaningless. Inboxes are filtered, phones go unanswered, interruption no longer buys leverage.

At the same time, discovery has shifted inward.

I see this clearly in my own work. I do not assume that buying something will fix my problems. Most products promise solutions before I am convinced the problem is even worth solving right now, and that alone is enough to disengage. If I do believe a purchase might help, the process is deliberate and self directed. We research, weigh tradeoffs (there are always tradeoffs) and actively look for reasons not to buy. No salesperson speeds that up. The desire to act does not come from the outside. It comes from within.

This is where the real gap has opened.

The way many organizations are still trying to sell looks increasingly disconnected from the way businesses actually make buying decisions. Volume prospecting underperforms and discovery calls miss the mark because the buyer has not yet decided the problem is worth their time. Ads and content struggle for the same reason. They assume attention can be generated through effort, rather than recognized when it becomes available.

Sales used to be about changing minds. Now it is mostly about being present at the moment conviction forms, if it forms at all. By the time a company shows up, the decision is often half made or already rejected. This is why the moment feels weird. Not because selling is broken, but because the distance between selling motions and buying behavior is now at a historic high.

And yet, people still buy things every day. They still invest in products and services to improve outcomes, reduce risk, and do their jobs better. The opportunity has not disappeared. What has faded away is the idea that desire can be initiated from the outside simply because a solution exists. The existence of your product does not, on its own, justify action on the buyer’s part. They must first value the problem, decide it is worthy of attention, and believe that solving it matters now. And, they may also have to figure out how they will operationalize whatever the solution may be before they are ready to talk to anyone from the outside.

What will replace today’s ads and outbound won’t be silence however. It will be signal. Signal looks more like clearly articulating a problem before the buyer fully names it, evidence that survives independent verification, language that matches how the buyer already thinks, not how the seller wishes they did. Signal is availability instead of pressure when the buyer is ready.

Selling will feel less like influence and work a lot more like infrastructure. Instead of running a convince and convert process to grab budget, the work is now to build something that integrates seamlessly into how the client already buys. That means being discoverable without demanding attention, understandable without explanation, and evaluable without someone forcing the process along. When a buyer finally reaches out, they are not asking to be convinced; they are asking to confirm.

Sales done well can still be one of the noblest professions, but the answer is not arming teams to push harder or enabling them to manufacture urgency. It is understanding, deeply, how your client makes decisions, and designing a way to be present without interfering, even when that means they are not ready to engage with you or your content. Great sales leaders are going to figure how to make this work.

For our company, business development is a high-precision, high touch activity, where the impetus to engage is driven by forces that have little to do with us and everything to do with timing, context, and readiness. That tells me there is probably no new emerging B2B playbook any of us should be waiting for, and I would be wary of one if it appeared. This will be figured out differently by every business. What I am confident about is that the gap will not close on its own, and things will not revert to familiar inbound outbound dogma, funnels, and sales motions.

It will get figured out. But it will be figured out by teams willing to accept that buying has changed, that desire cannot be initiated from the outside, and that alignment beats pressure every time.

Last week, a colleague said to me that she was “Done with LinkedIn!” Her concern was “the loss of pragmatism and adult business protocol swallowed by posturing and performative flexing.” You could hear the stored momentum in her words, it was the kind of line that only shows up after a long internal build. It made me think, LinkedIn has always had a bit of theater in it, but traditionally there were some inherited guidelines from the real world and perhaps those have shifted.

For instance, business people always knew the difference between what happens on a trade show floor and what happens in a boardroom. On the floor you posture a bit, polish your pitch, wave your hands around, and try to gather attention in a room full of other people waving their hands around. In the boardroom you sit still, talk less, and measure your words. Both modes have their place and everyone understands the boundary.

What feels different now is that LinkedIn has blurred that line almost entirely. The floor has expanded. It has swallowed the lobby, the conference hall, and most of the serious rooms where people used to behave with measure.

After thinking about it, I realized my feed is now full of people with “50 Million Exit” in their bios, “Coaching Founders to Greatness” in their banners, and inspiring carousels about resilience sandwiched between ads for a course they built last Tuesday. Ok, perhaps that is harsh, but I think there is more of that sort of thing than ever. I am going to avoid deciding this is a crisis signalling the end of good behaviour. I would prefer to think of it as a shift in incentives. The algorithm rewards attention more than it rewards competence. It does not know the difference between a thoughtful operating insight and a chest thumping victory lap masquerading as a life lesson. So naturally the feed fills up with whatever gets immediate hits.

If you have a low tolerance for loud posturing, this can make LinkedIn feel like a strange place to spend time. The operators, the people with real jobs and real constraints, still post. They are just harder to find because they do not shout. They write the way they work. Clean, competent, a little dry. They talk about what they are trying to solve, what they have learned, what they would do differently. They share early signals of things worth paying attention to. They go easy on the heroics, the personal brand elevation, the virtue signalling.

That is what I enjoy. I like hearing how people actually think. I like following where former colleagues landed and seeing what they are building. I like picking up the faint buzz around an idea that might become something real. All of that still exists on LinkedIn. It is just slightly obscured by noise, gushing and unchecked overstatement.

The trick is to remember what room you are in. LinkedIn is not a boardroom. It is a trade show floor. People in costume, shouting over the din, hoping a stranger stops long enough to scan their badge. We all like the spectacle of a good trade show! At the same time, there are still serious folks who walk the floor without shouting. They nod, make eye contact, stay curious, and move on.

If you keep that in mind, it becomes easier to stay present without getting irritated. You can let the barkers bark. You can ignore the “90 Million Exit” titles without needing to audit anyone’s cap table. And you can keep using the platform for what it is still good at, even if you have to brush past a few inflatable mascots on the way.

In the end it is simple. Behave like someone who knows what room they are in. And do not forget that you are allowed to leave the floor whenever you want.

The Tribuna of the Uffizi (1772–1778) by Johan Zoffany captures a crowd of connoisseurs performing their expertise in a packed gallery, each gesturing at something different. It is a study in noise, attention, and the small group of quieter figures trying to focus amid the theatrics.

The volume of bold claims surrounding LLM powered engineering has gone vertical lately. Every week brings a new prediction about instant products, infinite roadmaps, and software that builds itself. These make for catchy headlines that power cool newsletters, but they also sidestep the deeper realities of how durable, market ready products get built and how stubborn the market is when you actually engage it. Instant code does not mean instant product.

The problem is not the tools. It is the way we seem to be talking about them. The current narrative frames LLMs as the missing key that changes the physics of product development and makes old constraints irrelevant. That framing feels off and willfully oversimplified.

There is no question that LLMs are a breath of fresh air for engineers. They remove tedious work we are all happy to lose. They help with boilerplate, refactoring, tests, documentation, and cleanup. Some companies are collapsing entire layers of internal engineering and review with these tools. It is real progress and it makes the work nicer. But the rhetoric seems to continue to position "the coding" as the historical bottleneck to innovation. It is fun to say that because anyone can now code, hyper-targeted products will appear instantly, fill every gap, and fundamentally change the economics of technology. Maybe... But the code has not really been the barrier for years. The real hurdles are everything that surrounds it:

architecture

state

integrations

infrastructure

security

QA

compliance

scaling

reliability

release management

and above all the long, slow loop of getting real paying customers to adopt something and stay.

AIs speed up the edges, but they do not collapse these constraints. They do not move procurement. They do not fix broken workflows inside hospitals or banks. They do not create trust or manifest credibility. They do not give you the market signal that keeps a business alive.

I will gladly acknowledge push back from those who point to modern models that eliminate the need for traditional APIs or tools that scaffold full microservices or agents that can write and repair their own code and even synthesize market data. All of it is useful in the right context. These shortcuts help during prototyping and exploration. They remove ceremony. They accelerate early scaffolding. They make certain internal tasks faster. But production software still needs predictable boundaries. It needs correctness, versioning, observability, security, market fit, and long term maintainability. The shiny parts do not remove the physics underneath. There is still engineering on every front.

This is why the fears and fantasies of a market flooded with instant weekend products feel misplaced. People do not buy products because they were built quickly. They buy things that fit into their world, work as promised, and do not break the things around them. The same stubborn barriers that have always mattered will keep mattering.

What excites me is not the idea of instant, economically disposable products. It is the idea of removing enough friction that smart people can go solve smarter problems. Engineers get to engineer. Medical professionals get more time to provide care. Teams get more cycles to do the work that actually moves society forward. And if, as part of that, we get a deluge of noisey, half-baked products, I am equally confident they will collaps and fade away the same way all bad products fade away.

I don't know what will happen next, but I do know no one wants to pay for something that was built in a weekend.

I’m struck by the number of amazing people I have met, those who have achieved mastery and success, who also claim to have been poor students in the past. They talk about their learning and their journey openly, but rarely attribute their hard-won knowledge and understanding to school. At the same time, many of us have kids about to leave home for university or whatever comes next, so the question of how we learn stays close. Watching them step into the same system that shaped us makes me think about what real learning looks like, where it happens, and why it often begins only after school ends.

If you flip through the brochures, the modern university is hard at work selling the idea of learning and inspiration and growth as path to success and happiness. But look closer at the actual educational experience and you might conclude that school is mostly where people learn how to behave: how to decode shifting hierarchies, interpret signals, and perform within arbitrary systems. High school, university, grad school arguably don’t cultivate mastery; they reward endurance.

I won’t deny that great educators have helped shape great people. But for many of us, most of the professors weren’t conduits of knowledge and support as advertised. They are more like obstacles you must learn to navigate. Each has a private ecosystem of biases, blind spots, and expectations. The student’s real task is to map that terrain, to divine what this particular gatekeeper wants, and deliver it in the right format at the right time. The “curriculum” isn’t chemistry or history. It’s compliance under pressure.

This does not make school worthless; far from it actually. Learning to survive an ever-changing hierarchy, to extract meaning from unclear signals, is a life skill. It’s why people who make it through school tend to make it through corporate structures too, and that is important. But that’s not knowledge or mastery. It’s conditioning for survival.

My point is knowledge lives elsewhere. It requires curiosity, time, practice, tenacity, and space to fail: the very things these formalized systems can quietly discourage. Knowledge grows when you’re left alone long enough to look in the dark corners, to pursue questions that weren’t assigned, to fall repeatedly and then get back up to follow your confusion until it turns into understanding. Few people enrolled in university settings achieve this; perhaps a handful of post docs; and that is just minutes before they die from starvation and lack of sunlight.

That’s why autodidacts can often surpass their higher scoring peers in life. I have met a few of these people and what stands out is that they don’t wait for permission to learn. They want to read what they need, manifest their own structure, and test their understanding in the world. For them learning isn’t a performative art; it’s an encounter with reality. For them school was an obstacle course standing between them and what they want.

So maybe school isn’t where knowledge lives, but where we learn to survive the noise long enough to find it later. It gives us structure, a sense of process, and a taste of pressure. The rest, curiosity, time, failure, and the quiet work of figuring things out, comes after and, if we are lucky, persists for the duration.

There are as many ways to steer a growing company as there are books on success and management. Some emphasize people, others finance, markets, or intellectual property. Customer centric or product led, strong operators versus strong visionaries, bulletproof technology, moats, margins, and much more. It’s all good, and it’s all kinda true.

To my mind, the most enduring approach, the one that generates the most luck, is simply to be in service of The Problem: the hole in the world you are pursuing. It is at once a humbling act of respect, obsession, and dare I say it, love. If you can fall in love with the problem you’re trying to solve, you’ll be hard to beat. Everything else flows from this: the solutions you build, the people who choose to join you, the policies you set and the clients who elevate you most in the early days. All of it follows in the shadow of the problem.

The problem may reveal itself in awkward lurches, but it won't lie to you. The problem may not be easy to solve, but it will be equally cruel to your competition. Love the problem more than anyone else, and you’ll become its expert; mastery will follow even when you are still small.

It's fun to try and spot this when meeting leaders of interesting companies; the ones that are gaining traction and becoming hard to ignore. If you listen carefully, they often don’t spend much time talking about themselves, their approach, or even their solution. Instead, they naturally guide the conversation back to the problem: why it exists, what it costs, why it matters, what still eludes them, and all the nuances in between. It’s what they love talking about most.

Those companies have a way of always showing up, always being ready to engage, and often punching above their weight.

In the rush to apply AI everywhere, the applications that stand out are the ones that unlock data in ways that were never before possible. These are the kind of leaps that provide orders of magnitude more access and change our perception of long standing issues. That is what fascinates me most. Our bodies are producing and streaming out data all the time, amongst the richest and most overlooked sources are the sounds of the heart and lungs. These sounds are not just noise. They are structured frequencies carrying rich and specific information about how vital organs are functioning.

Two hundred years of stethoscopes and clinical expertise have proven that the information is there. Trained ears have long been able to hear the subtle signs of disease in these sounds. It is not just about one metric like heart rate or blood pressure. Many diseases leave signatures that can be detected in audio. Valvular disease, pulmonary edema, chronic obstructive pulmonary disease, cancers, atrioventricular blocks, and heart failure can all present acoustic clues, often early in their progression.

The barrier has never been whether the information exists. The barrier has been how to collect it, interpret it, and use it at scale. Traditionally this required specialized training, equipment, and the patient in the same place as the expert. That model was never going to work for population-level screening. But the demand for better early detection is growing, and this is exactly where AI changes the equation. We now have the ability to record these sounds, train machines on them, and apply the same grade of clinical expertise instantly and reproducibly to millions of people.

Heart murmurs are a clear example. A murmur is a distinct sound produced by turbulent blood flow in the heart. These sounds carry signature tones, volumes, and timing that are directly tied to the structure and function of the valves. Some murmurs are harmless, while others are (for example) the first signs of aortic stenosis or mitral regurgitation, diseases that affect millions and often progress silently for years.

For patients, the value of detecting a murmur is straightforward. It can provide reassurance when no immediate problem is found, or it can trigger medical surveillance and timely treatment when disease is present. Valve disease often advances without symptoms in its early stages. Identifying it early can prevent sudden deterioration and make planned interventions safer and more effective.

For health systems, early murmur detection improves patient flow and reduces avoidable emergencies. It helps prioritize and plan care while reducing the cost of late stage admissions and readmissions. Capturing and analyzing these sounds at scale turns a fleeting body noise into a durable biomarker, one that supports medical decision making and makes detection more efficient.

The murmur is just one example that shows the intrinsic value in these sounds. A signal that can calm fears, extend lives, and guide patients into the right care at the right time. In cardiovascular health and beyond, that makes it one of the most powerful forms of data we have.

To make this more tangible, here are two recordings:

• Normal heart

• A heart with aortic regurgitation

Aortic regurgitation, also called aortic insufficiency, occurs when the aortic valve does not close properly during diastole, allowing blood to leak backward from the aorta into the left ventricle. This causes volume overload, which can lead to heart failure, arrhythmia, and higher mortality if untreated. Population level studies suggest moderate or severe aortic regurgitation affects up to 2 percent of adults, with prevalence rising with age.

I hear self-driving cars are getting pretty good at it. People are saying the trend towards robot cars will not stop. Assuming that’s true, and adoption reaches some critical point, it makes me wonder how this all plays out.

Imagine a future where 40 percent of cars on the road are autonomous. They are not perfect, but they are safe, efficient, and polite. They don't tailgate, they don't cut people off, they don't roll through stop signs, and they certainly do not run red light lights.

That sounds like progress.

Now imagine you are a human driver in the same city traffic. You begin to notice something. These robot cars always yield. They never assert themselves. They pause longer than necessary. They hesitate at merges. They stop when someone even considers stepping into a crosswalk.

If you are like many people with somewhere to be, you might take advantage. And if not you, someone else will for sure. That is where the road dynamics begin to shift.

Game Theory in the Left Lane

This is a textbook case of asymmetric behavior. A mix of predictable agents (the self-driving cars) and opportunistic agents (the humans). The robots are bound by code and caution. They are designed to avoid liability and maximize safety. The humans are bound by very little.

The predictable agent always loses a negotiation. If the robot car will always back off, then the human driver always wins. Whether it is lane changes, intersection merges, or deciding who gets to go first at a four-way stop, the human begins to dominate.

This is not theoretical. Researchers have observed that even a small number of aggressive drivers in a mixed environment can paralyze the flow of traffic. The robot cars, in an effort to be safe, overcompensate. The result is friction. Sometimes, it is gridlock.

Social Codes on the Road

Driving has always been a social game. It is not just about rules. It is about reading intent. We glance at the driver across the intersection. We interpret speed, posture, even the car model. People game each other constantly, often without realizing it.

Self-driving cars don't play that game. They don't make eye contact. They do not bluff. They do not assert dominance. They don't respond to the implicit negotiation that defines daily traffic.

This becomes a problem when humans realize the robot will always defer. Because now, aggression is rewarded. Not punished.

What follows is a two-tier traffic culture. The aggressive drivers move faster. The autonomous cars fall behind. They become the cautious minority in a system that still favors speed and risk.

Automation, But Make It Profitable

This raises a more fundamental question. Was driving really the most important thing to automate?

Self-driving cars are a technological marvel. The companies behind them deserve credit for what they have accomplished. But it is worth asking why this problem attracted billions of dollars and global talent, while other forms of automation have not.

I still take out my own garbage. No robot has offered to do that.

Household tasks, elder care, repetitive manufacturing, food service; these are all ripe for automation. They are labor-intensive, unglamorous, and impactful. But they don't scale like transportation. There is no trillion-dollar garbage robot market. No IPO for autonomous dishwashers on the horizon. And I still insist that our most noble goal with intelligent systems should be to extend life and health in accessible and scaled ways.

We chose to automate driving not because it was urgent, but because it was a platform. A platform for logistics, delivery, fleet optimization, and real-time mobility data. These are powerful economic levers. Taking out the trash (apparently) is not.

What Comes Next

If autonomous vehicles become the minority on the road, they may suffer from their own civility. To compensate, we might see:

• New policies or dedicated lanes for AVs

• More assertive behavior models built into their software

• An attempt to “teach” machines how to be slightly less deferential

• Or possibly, a public backlash against aggressive human drivers who exploit the system

Until then, we remain in a transitional moment. A strange era where some cars operate like chess players and others drive like gamblers. One group is ruled by algorithms. The other by impulse.

Personally, I am not against the development of autonomous driving if it saves lives. It holds promise. But I also think we may have prioritized it not because it was hard, or meaningful, or humane, but because it was modelled as being profitable for some key players.

Because I still have to sort my recycling by hand.

And the robot vacuum continues to be defeated by my area rug.

A recent editorial in Circulation (May 2025) calls for a more proactive approach to aortic valve disease, highlighting how many patients remain undiagnosed until the disease is advanced, often too late for optimal intervention. In particular, the piece highlights Aortic Stenosis (AS), which affects about 2% of the population rising rapidly to greater than 10% for older people. The DETECT-AS trial, at the center of the article, shows promise: when care providers were automatically alerted to existing severe aortic stenosis (AS) findings buried in echocardiogram reports, treatment rates improved significantly. That’s a win. ([Circulation. 2025;151:1508–1511](https://doi.org/10.1161/CIRCULATIONAHA.125.074928))

Consider this: in DETECT-AS, the use of an automated electronic notification system increased valve replacement rates from 19% to 26% at 90 days, and from 37% to 48% at one year. That’s not a tweak,  it’s a substantial uptick in life-saving action. Importantly, the alert system improved referral rates equally for women and non-white patients, addressing long-standing disparities in cardiovascular care. [Circulation, 2025]

I agree with this direction; electronic provider notifications (EPNs) are a smart, scalable nudge that closes a dangerous gap in the system. Too often, diagnosis happens but action doesn’t follow. EPNs help fix that.

But it’s important to not mistake this kind of detection for screening.

The patients in DETECT-AS already had an echocardiogram. They were already in the system. Their hearts had already spoken and what EPNs did was prompt someone to finally listen. That’s critical, but it’s not screening in the public health sense. Screening means finding people before they show up at the echo lab. Before symptoms. Before hospitalization. Before the tell tail heart murmur is missed or dismissed. This is the effective front line where we must meet the disease.

The stakes are high. Up to 50% of people with untreated, symptomatic severe AS die within a year. Even among those without symptoms, the rate of death, stroke, or hospitalization in the surveillance group of the EARLY-TAVR trial was 45% over just 3.8 years. [Genereux et al., NEJM 2025;392:217–227]

Early detection can’t wait. That’s the role Stethophone was built to fill.

Traditional approaches to screening have leaned heavily on a narrow funnel of overtaxed experts; people who must be physically present, with specialized equipment, at the same time and place as the patient. In the face of population scale health challenge (like AS), that model is slow, expensive, and fragile. It also leaves far too many patients undiagnosed until their condition becomes critical.

Murmurs, particularly those from low-flow AS, are notoriously difficult to detect, especially when masked by body habitus or  comorbidities. And while auscultation has long been treated as an art form, it hasn’t always been an accurate one. Studies show that murmur detection by traditional means can be inconsistent and subjective.

Stethophone changed that. We’ve radically improved murmur detection sensitivity into the 90%+ range,  not with new gadgets or elaborate workflows to manage, but with a smartphone. We extract signals that often get missed, and we do it without requiring an expert in the room. What was once a specialist skill is now broadly accessible, objective, and shareable.  Whether you’re a frontline nurse, a rural clinician, or a concerned individual at home, you can now instantly generate  clinical marker that leads to timely echocardiography and specialist care. That’s not passive detection. That’s real, proactive screening, the kind that reaches people before symptoms do.

DETECT-AS gets it right, we need to act on what's already known. But we also need to know more, sooner. The system hears late. Screening means listening early

There is something that has bugged me for a while: companies still proudly slapping words like “advanced technology” all over their marketing.

Are we, as buyers, really holding our breath, hoping to spot the "Advanced Technology" label before committing to a toaster or a piece of software? Hardly. Comsumers are already marinating in a soup of tech claims. Everything is smart, AI powered, Bluetooth enabled, and quantum scented. “Advanced” does not reassure anyone anymore; it just sounds like an echo, a faint, slightly desperate signal from a company trying to stand out without actually connecting to why their product matters.

When I see “advanced technology,” it often has the opposite effect. I picture a whiteboard meeting where someone panicked and said, “Quick, slap ‘advanced’ or better yet ‘next generation’ on it!” and everyone nodded like that solved something.

It is not even a new move. Before “advanced technology,” marketers leaned on “high technology” to avoid being mistaken for mundane. And when I was a kid, the magic word was “space age,” as if your new toaster oven had just returned from orbit, trailing cosmic superiority behind it. (“It toasts… in zero gravity!”)

Here is the real truth: building something that genuinely serves people is hard. And it is hard even when you stick to well understood tools and technologies. Great engineering is not about the vision of using shiny new parts; it is about judgment, understanding trade offs, fitting things together wisely, and making tough choices no one ever sees. That is what deserves celebrating, not some empty label.

Sometimes, though, proven tools are not enough. Sometimes the problem you are chasing forces you into new territory. You cannot just pick from the toolbox; you have to invent a tool. Betting on a method or pattern that has not been proven is not about being bold for its own sake. It is what you do when it is the only way forward.

Even then, there is rarely a single, famous “Eureka!” moment to brag about. The road to a real product is a long existential slog, full of setbacks, dead ends, and brick walls that do not announce themselves until you are already face first into them. And even if you do advance the state of the art, here is the twist: that might not even be the most important thing.

If you make it to market, the story you tell should probably not be about your shiny new tech. It should not even be about how brilliant your team is (even if they are).

It should be a love letter to the problem you set out to solve.

Because real innovation is not about “advanced technology.”

It is about stubbornness, empathy, and purpose.

The tech is just what happens along the way.

At Sparrow Bioacoustics, part of our work involves understanding the impact of early intervention in cardiac disease. That means we spend a lot of time staring at data: screening rates, risk factors, survival curves. We try to understand the early signals, those that often manifest before people experience obvious or specific symptoms.

But the more you live inside the numbers, the more you think about how unforgiving they can be. Most people live somewhere near the middle of the bell curve; where things make sense. But medicine doesn’t always stay there. Sometimes, life drags you out to the margins, where evidence is thin, outcomes are uncertain, and survival becomes an outlier event.

If you’ve ever landed on the wrong side of those statistics, you know it’s not just a clinical problem, it’s personal. It’s terrifying. It’s brutal for your care team, your family, and anyone trying to fight their way back through uncertainty.

In 2019, I experienced acute septic shock as a result of necrotizing fasciitis (NF).

…Twice

The odds say I should not be here, and I find that interesting. So I thought I would share a few statistical outliers from my own time out in the tail.

Necrotizing Fasciitis: The Idiopathic Variant

Necrotizing fasciitis (NF) is rare, affecting roughly 0.4 cases per 100,000 people annually in developed countries [1]. It’s most commonly associated with trauma, surgery, diabetes, cancer, immunosuppression, or injection drug use [2]. I had none of those. No underlying conditions, no wounds, no risk factors, just a modest bruise on the knee after a ski trip and a fever of 105.

That puts me in the idiopathic category, a subset that accounts for only 5–10% of NF cases [3]. In these, the infection likely enters through a microscopic skin break or transient bacteremia, with no clear source ever identified.

What makes this variant particularly dangerous is the delay in diagnosis and appropriate intervention. Without a visible injury, portal of entry, or other risk factors, NF can masquerade as a muscle strain, hematoma, or viral illness until the infection explodes beneath the fascia.

Necrotizing fasciitis carries a high baseline mortality rate, estimated at 20–30%, and as high as 70% when septic shock is involved [4]. A retrospective study by Hua et al. (2011) found that NF complicated by shock required vasopressors in over half of cases, with dramatically worsened prognosis. When compounded by multi-organ failure (like mine) mortality climbs to 70–90% depending on severity and ICU course [5].

According to the Sepsis-3 consensus definition (published in JAMA in 2016), septic shock is: A subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality. Septic shock is what happens when an infection doesn’t just stay local, it hijacks the entire body. The immune system launches a chemical war so intense that it starts damaging the body’s own tissues. Blood vessels leak. Organs lose oxygen. Blood pressure collapses. The heart struggles to compensate. Left unchecked, it spirals into multi-organ failure and death. Once it starts, it’s also very hard to stop.

By the time I entered the hospital, I was already in septic shock. My labs showed lactic acidosis, elevated creatinine, and leukocytosis. I was also in respiratory failure. After a CT confirmed the infection, I was intubated, sedated, put on a massive course of broad-spectrum antibiotics, placed on vasopressors, and rushed into emergent surgery to debride the affected areas of my leg. Months later, I would lose that leg.

My kidneys had failed completely, requiring dialysis throughout my ICU stay and for weeks afterward. Liver function was also impaired and failing, but that took a back seat to my heart. After several days on life support, my heart began to weaken, a condition described as a "soft heart," shorthand for septic cardiomyopathy which is characterized by myocardial stunning, reduced ejection fraction, and circulatory collapse [6]. In my case, the heart couldn’t maintain perfusion on its own, and every other organ suffered. Somewhere in the chaos, my left lung also collapsed. It went unnoticed for a time, masked by the fact that I was already on a ventilator. Whether iatrogenic or spontaneous, it only worsened my respiratory compromise.

My blood pressure was often unrecordable without pharmacological support, meaning that, without continuous IV vasopressors, it was too low to measure with a cuff. Systolic BP < 60 mmHg with no palpable pulse, is the kind of hypotension that erases the line between life and death. Problematically when mean arterial pressure (MAP) drops below ~65 mmHg, cerebral blood flow becomes critically impaired. Prolonged hypotension like this can lead to diffuse cortical injury, neuronal death in the hippocampus, and long-term cognitive deficits.

Apparently I also soaked up 40 units of saline. With the kidney failure, most of it was retained in my tissues for weeks. The resulting edema was extreme, and every intervention became a high-stakes balance between keeping organs perfused and not drowning the rest of me.

I continued to decompensate for days. Cardiac decompensation during septic shock is particularly dangerous because it undermines the very measures, like fluids and vasopressors, meant to stabilize the patient. When the heart can no longer serve as a reliable engine, the rest of the body shuts down. Eventually, a combination of time, high-dose Linezolid, and IV immunoglobulin halted the downward spiral and I stabilized.

The Second Shock: A System Already Primed to Fail

A few days later, while still in a coma, the team decided to take me in for a second debridement surgery. Shortly after I began bleeding massively and my blood pressure crashed below 60. This was a second round of septic shock, an event that further, dramatically, reduced my odds of survival.

In critical care, this kind of deterioration is known as a "second hit", a new physiological insult that occurs while the body is still reeling from the first. This secondary inflammatory cascade, marked by renewed cytokine storms and systemic endothelial damage, has been shown to double mortality risk in ICU patients [4]. A study by Luecke et al. (2005) found that surgical patients with secondary septic deterioration faced >70% mortality when combined with multi-organ dysfunction. Other analyses show that survival following recurrent septic shock within the same hospitalization can fall below 10% [5].

The severity and idiopathic nature of my case brought in specialists from across the hospital network. Mount Sinai West eventually presented a report based on my case at the CHEST 2019 Conference, documenting how NF can present without trauma or known immunosuppression, and how devastating delays in recognition can be [8].

Am I Still Me?

A few weeks into it, they brought me out of the coma and extubated me. Though I was awake and breathing on my own, I had profound amnesia. I couldn’t form new memories or retrieve old ones. Neurological injury due to prolonged hypotension was suspected. The hippocampus is especially vulnerable to ischemia. Neuronal death in this region is associated with short-term memory loss, confusion, spatial disorientation, and difficulty forming new memories. On top of that, being in a coma can (on its own) cause lasting cognitive impairment. Being in a coma is not the same as sleeping. Sedation doesn’t replicate REM or restorative brain activity. Your brain basically becomes damaged by prolonged sleep deprivation. Many ICU patients emerge from comas with fragmented sleep-wake cycles, impaired cognition, and something resembling PTSD. This kind of disruption is now known to be a major contributor to long-term cognitive impairment. A 2013 study published in The Lancet Respiratory Medicine found that over 50% of coma patients had cognitive scores equivalent to moderate traumatic brain injury or early Alzheimer’s one year after discharge. [7].

After a few days, I finally did actually sleep, and something suddenly reset. Memory began to return; I could focus and process my surroundings. Mentally, I was myself again.

The Statistical Tail

If you build a model from the data, my odds of surviving the acute phase were less than 5%. The odds of avoiding permanent dialysis, heart failure, a liver transplant, and returning to a neurological baseline with no lasting emotional or cognitive impairment, were likely less than 1%.

There’s no tidy moral here. No heroic arc. Just a shitty hand dealt by biology, and the extraordinary, coordinated response from clinicians, caregivers, and my wife who had to play that hand. My wife never stopped watching. My care team never stopped adjusting. Things went wrong, some even failed, but everyone adapted fast enough to save me.

Does knowing the odds help in the fight? Maybe not. But someone has to know them. Someone has to fight with clarity even when the numbers say you probably won't make it. Especially when you're way out in the tail, where medicine gets less certain, and survival stops being expected. They talk a lot about the bell curve in critical care. But what happens at the margins matters too. Because sometimes, even at the farthest edge of probability, someone survives.

And when they do, the tail talks back.

a few references

1. CDC. Necrotizing Fasciitis: A Rare Disease, Especially for the Healthy. https://www.cdc.gov/groupastrep/diseases-hcp/necrotizing-fasciitis.html

2. Stevens DL, et al. Practice guidelines for the diagnosis and management of skin and soft tissue infections. Clin Infect Dis. 2014;59(2):e10–e52.

3. Chelsom J, Halstensen A, Haga T. Necrotizing fasciitis: Rapid diagnosis and treatment is key to survival. BMJ. 1998;316(7145):654–655.

4. Gajic O, et al. Secondary sepsis and multiple organ failure in the intensive care unit: incidence and outcome. Intensive Care Med. 2003;29(10):1693–1700.

5. Luecke T, et al. Mortality and multiple organ failure after secondary surgical interventions in patients with severe abdominal sepsis. Br J Surg. 2005;92(3):380–386.

6. Vieillard-Baron A, et al. Myocardial dysfunction in sepsis: reversible myocardial depression. Intensive Care Med. 2008;34(1):9–16.

7. Pandharipande PP, et al. Long-term cognitive impairment after critical illness. N Engl J Med. 2013;369(14):1306–1316.

8. Rajeeve S, et al. NECROTIZING FASCIITIS TRIGGERED BY TRAUMA- BUT NOT TRAUMA: A Case Report. Presented at the CHEST Annual Meeting 2019; New Orleans, LA.

Subscribe now

There are urgent things and there are important things. It has been said that urgent things are often the reason that important things don’t get done.

You can drain years responding to urgent things and, as a result, fail to make meaningful progress on the important things. Eventually you may even find yourself rationalizing away those important things in favour of adopting hyper-responsiveness and hustle your branded advantage.

The truth is most urgent things (when viewed in retrospect) did not prove to be all that critical. By contrast, failure to understand and accomplish the one or two really important things, makes companies weak, stuck and eventually dead.

We push off important things in small increments. A day at a time or week at a time; and often we keep doing it. It’s easy because there is often no immediate punishment.

Urgent things, on the other hand are loaded with palpable, anxiety-provoking, uncomfortable threats: like looking bad, losing confidence, or just plain getting caught with your pants down. So it takes a lot of resilience and emotional regulation to avoid conferring upon them rapid and disproportionate levels of priority.

For the most part, people don’t cherish those times they dropped everything to go run into the latest fire. But those times we stayed focused on what was important, while calmly absorbing the bumps along the way, is something we always feel good about.

In 1861, Thomas Crapper was hired by Prince Edward (later King Edward VII) to construct lavatories in several royal palaces. He patented a number of toilet-related inventions that would minimize the time and distraction of relieving oneself.

History teaches us that even when we work diligently, sacrifice bravely and obsess on details with passion, there will still be many things beyond our control. Some of those things will cause us to fail. No one likes to think about this way, but all worthy endeavours come with this risk. Even if you process every input, optimize every resource, and keep at it long after others have given up, you can’t every truly force things to work. Control like that can’t be created. And while many things are beyond your control, the one thing that is within your control is what you choose to pay attention to; and that can tip the scales.

Years ago I rode motorcycles - a lot. I learned how to ride in all kinds of conditions and all kinds of highway traffic. At speed, the bike responds nimbly to even the smallest movements of your body. Because of this, you quickly learn that wherever your attention goes, the bike will also go. So if you want to avoid that deadly pothole ahead, don't stare at it, look past it while you steer around. If you see an accident happing in front of you, relax, focus on the path through, and resist the temptation to fixate on the cars and trucks flying towards you. Under pressure, where you to choose to place your attention matters a lot.

Similarly, if you have big goals and complex obstacles in your view, how you divide up your mind has direct bearing on the outcome. The things we regulalry allow into our heads also bring us lessons, and those lessons draw us either closer or farther from our desired endgame. When we are working hard, the things we pay attention to need to be thoughtfully curated and line up with the goals we set.  This alignment takes deliberate effort, it does not happen on its own. Even if you manage to tune the right attention in, it’s fragile and needs to be defended.

I regularly try to find a way to pause and conduct an honest mental audit of my attention: Am I measuring things that provide the most immediate clarity? Am I spending proper time with the people who know what we need to know? Am I preoccupied with the right obstacles? Am I having the right conversations and debates with the team? - or are we all just (albeit diligently) acting on the things and ideas and data that cross into our collective field of vision? Of course you can never be perfectly certain that you have it right, but I’m convinced it’s a valuable habit. At the very least, it might save us from hitting all the unexpected potholes.

Most of us can’t help being ruled by our perceptions. That’s why people fight and push their view and gravitate to others with similar perceptions. It has also been said that perception is reality; so, managing perception becomes a paramount consideration for individuals and businesses alike.  Perception matters to people; it can't be dismissed.

In practical situations, however, perception is an imperfect tool. It's not helpful because it is unique to you; it’s how YOU see things. You can’t use your perception to effectively lead others or teach people or learn anything new yourself.

For better or for worse, our perception of a situation or a dynamic, or a root cause is wildly coloured by our own biases, needs and past experience. Reality is often far more nuanced and far less concerned about us and our needs. If you rely on the work of others (and in business, we all do) then trying to get people to share a perception is very hard; and perhaps not as desirable as it sounds.

What you need instead is perspective. Perspective is the ability to become an actor outside of your own lens, to put yourself in the shoes of others, to step back and see things as they are from a remote view; to take yourself out of things for a moment. 

We all see people pushing their perception onto others.  Ironically others often resist, if for no other reason than to protect their own perceptions. This makes all the parties mad, because the lack of agreement feels personal.  Building perspective, is a much more useful way to understand an obstacle or characterize a problem. You can be as objective and dispassionate as you need to be, yet still debate and grind without emotionally exhausting yourself and everyone else. Perspective helps because it gets us closer to how things really are, not just how we explain them to ourselves.

The Ambassadors is a 1533 painting by Hans Holbein the Younger. As well as being a double portrait, the painting contains a still life of meticulously rendered objects, the meaning of which is the cause of much debate.

Mark Attila Opauszky

Five years ago I almost lost my life.  For those who know me, the story has become a standard fixture in my narrative; it gets mentioned often.  For those who don’t know me, it’s not that important, but there are a couple of things that came out of it that are worth talking about.

The disease was rare, unexpected and incredibly sudden.  I went from annoying flu symptoms to cascading organ failure in about 24 hours.  While I was on life support (in a coma), an ICU doctor told my wife, I was the sickest person he had seen who was still technically alive.  Even then, I almost slipped away more than once.  They said that even if I did survive, my prognosis was not good.  I would have permanent kidney and liver damage and need transplants. I would likely have heart damage, and I could expect to lose at least some of my arms and legs. There was also good chance that I suffered cognitive damage from lack of blood flow to the brain.  If I came home at all, it would not be in one piece.  I might not even be me anymore. It was a lot to process.

Recovery was both long and painful. In fact it permanently rewired how I perceive and process pain. I spent a lot of time in hospital where I learned to listen to my body, focus on its signals and the metrics of my progress. I remember my pulse O2 indictor dropping and wondering with the nurses if I just collapsed a lung (I did), I remember convincing a doctor that I had a blood clot tearing slowly through my left arm (I did), I remember the exact day I produced more than 20ml of urine signalling hope that a kidney may still come back (it did).  After some months, and numerous surgeries and a lot of physical therapy, I was more less myself again. And during that time, every step forward demanded that I be super proactive, even if that step was going to be the last progress I see.  I’m different now of course, but I’m still me, and I’m grateful.  I’m grateful for the fantastic recovery and for all those who helped me. And believe it or not, I am also grateful for the experience. 

For me, the most profound part of the experience was the realization that your survival and recovery have a lot to do with the quality of participation from your side.  Advocacy, information and understanding had a huge positive impact on my outcome.  The doctors and nurses were outstanding of course, but on at least one occasion, it was my wife (who never left my side while I was on life support) who saved my life by directing attention to something they missed. Advocacy matters: even though my prognosis was poor, she learned everything she needed to know and was a huge influence on the attention I received. I don’t think they were expecting me recover, but she did, and she made them believe it too.  She even found herself occasionally providing leadership and stability to the care team when things got crazy.  

Weeks later, when I was conscious again, I took an active role in my own care.  I learned my biometrics. I understood and processed my odds. I collected my own data and fed the observations back to my care team so we could make better decisions together.  Of course it all could have gone horribly wrong, but it didn’t.  I’m now convinced that people who take an active role in their own care, and have advocates or advocate for themselves, see better outcomes.  I now have a huge appreciation for the value of being part of your own care team, as opposed to a helpless passenger.  I am even more impressed by how responsive good doctors and nurses are to your insights and observations, especially if they are empirical and specific.  They want what you want, and they appreciate the help. 

A few weeks ago, the team at Sparrow Bioacoustics launched Stethophone.  It’s a different kind of app; it’s also cleared by the FDA as a medical device. The app turns your smartphone into a medical grade stethoscope – so it can listen to and record your heart and lungs when you hold it to your chest. 

So why do any of us need a high-end stethoscope in our hands?  For me, it’s about data and advocacy.  Heart symptoms and concerns happen at home or work or on vacation – they don’t always manifest in the few hours a year you are at the doctor.  But the incidence of disease that goes undetected or undertreated in the population is staggering.  People need ways to collect hi-fidelity medical information when it matters. And they need a way to use that data to help doctors, help them. That is what Stethophone is all about.  It’s not a panacea of course, but the information is diagnostically deep.  We have helped people who were anxious about periodic cardiac symptoms and wanted a medically powerful way to show their doctors what was happening.  We have helped detect serious disease hundreds of times during our early pilots.  We are still taking our first steps, and we still have a lot to bring forward, but I feel very close to this kind of value proposition because it scales. It holds the promise that millions  of people can be armed with a whole new way to be proactive about their heart health.  For the record, heart disease is the number one killer of people everywhere - followed by lung disease.  My heart hung on just long enough for me to somehow survive – as long as your heart keeps going, you always have a chance - we want that for everyone.

Andries van Wezel (31 December 1514 – 15 October 1564), latinized as Andreas Vesalius (/vɪˈseɪliəs/),[2][a] was an anatomist and physician who wrote De Humani Corporis Fabrica Libri Septem (On the fabric of the human body in seven books), which is considered one of the most influential books on human anatomy

Subscribe now