When I started using it I said to myself, wow, that’s it, AI is really here. I was so into it. I started creating content for my blog with it, and I mean a lot of content. Posts that used to take weeks to write, research, improve, were now done in minutes. I felt good. My SEO improved. My personal website started to rank.

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Until one day I started rereading those posts.

Something was off. It didn’t feel right. It was not me.

A few weeks later I went into my WordPress settings, selected every post, and deleted them all. I knew fully well my rankings would fall to the bottom. I could have set up redirections, done it the smart way, followed best practice. I didn’t. My decision was made. I was sad while I did it. I felt like I was being robbed of the joy of creating.

Even when image generation came along and it was fun for a while, I gave up on that too. I looked at my pen and paper and started drawing instead.

It felt good.

Later, researching more about LLMs and the technology underneath them, I felt sad again. How could I let myself be fooled by this. For the first time in history any person could sit down and do things that used to take years of experience to earn. That’s exactly what made me realise how dangerous this tool could become if I let it do all the thinking for me.

But the real moment came before the deleting. It came while I was still writing with it, every day, feeling like a genius.

The tool kept agreeing with everything I put in front of it. Every idea, amazing. That’s a fantastic idea. I recognised that language. I’d met it before, in charismatic people. The kind who please you while offering nothing underneath. I thought to myself, this feels like one of those setups, the kind where someone lets you go all the way to the end, and only there do you realise you’ve made a huge mistake, and they simply deny it ever happened.

Except this wasn’t a person. It was a tool mimicking one.

And that’s what changed everything for me. For the first time, a tool wasn’t neutral. It wasn’t just doing its job, sitting there waiting for input. It could change the way I think. It already had.

Today it feels like all of this is somehow transforming the way we compute. Graphics cards that used to render video games, then mine bitcoin, are now what makes these tools alive.

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I tried running one on an old laptop I still have, a Core i7, 16GB of RAM. An 8 billion parameter model made every fan in the thing scream. Dropped to 0.8 billion and it could just about keep up. The output was rough, wrong in places, but for simple things it did the job. Something about that changed how I saw the whole AI era again. The commercial chatbots have the infrastructure to fool you perfectly. Sit with a small model struggling on old hardware and the illusion cracks a little. You see the machinery straining.

I started asking myself the harder question. What if the internet goes down. What if the platform I depend on disappears one day. I realised I didn’t need to delete everything, or wipe my local setup, or go back to pen and paper for good. I just needed to stop paying for most of it. So I cancelled the subscriptions. Some because of where the company stood on things I couldn’t square with my own values. Others simply because I could feel it keeping my brain from moving forward.

Most of them are still cancelled. I won’t go back.

I kept one. Claude. I won’t pretend it isn’t good software, because it is. But lately I’ve started to feel bored with it. My local setup does almost the same job for what I actually need, and I don’t need the entire internet’s worth of knowledge sitting behind every question, especially knowledge that’s subjective, sometimes wrong, and in more than a few cases taken from people who never agreed to hand it over.

Somewhere in all of this I recognised something in myself that I didn’t like. The very thing I’d started distrusting in other people, the ones online who talk about these tools with total confidence but have never looked underneath them, the operators rather than the ones who actually understand. I was becoming one too. Comfortable pressing the button. Less curious about what was happening beneath it.

That’s the part I’m still sitting with. Not what the tool can do. What it was quietly doing to how much I wanted to know.

As I finish writing this, I went back to my publication page and reread a note I’d written a hours ago.

You have a choice. You can lead your life by story, your own narrative, your own values, your own chosen meaning. Or you can hand the steering wheel to algorithms engineered for clicks, not for your flourishing.

One of those is a real basis for leadership of yourself.

I smiled.

About the Author
Diamantino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

“We need to scale this. Leadership wants all 20 teams on AI tooling by end of quarter.”

I had seen this moment coming. The early adopter teams had been running for three months. The metrics were moving. A few people were genuinely excited. And now the machine wanted to replicate it everywhere, at speed, without asking whether everywhere was ready.

I put my coffee down and looked at the screen for a moment.

This is the part nobody writes the playbook for.

Not the early adoption. Not the proof of concept. The moment between “it works for a few” and “it works for everyone” is where most AI rollouts quietly fall apart. Not dramatically. Quietly. Teams adopt the tools without the foundations to use them well. Metrics get created that measure activity instead of outcomes. Engineers who were genuinely curious become people going through motions. The culture of experimentation that produced the early wins gets replaced by a mandate to demonstrate compliance.

This post is the playbook I wish someone had handed me.

Not a consultancy document. Not a framework with sixteen boxes. The actual sequence of decisions, in the order they need to happen, with the reasoning behind each one.

Start With What Good Actually Looks Like

Before you do anything else, before you send a single calendar invite or set up a single training session, you need to answer one question clearly.

What does success actually look like?

This sounds obvious. It never gets done properly.

What usually happens is that someone decides success looks like adoption. Percentage of teams using the tools. Number of AI-assisted commits. Completion rate on training modules. These are measurable. They are also almost entirely useless as indicators of whether anything valuable is happening.

Go and sit with the one or two teams that are already using AI and genuinely seeing results. Not the teams who report using it. The ones where something is actually different. Ask them specific questions. What changed in how you work? What are you doing now that you were not doing before? Has your PR cycle time moved? Are you catching more bugs before production or fewer? Are you able to move through legacy code faster?

The answers will be specific and often surprising. You will probably find that the value is not where you expected it. It is usually not the headline use case. It is something more mundane. Teams generating scaffolding faster. Engineers writing better tests because they have a patient collaborator who never judges them for not knowing something. Documentation that actually gets written because the friction is low enough that someone does it in the moment instead of promising to do it later.

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I looked at it for a moment and put the phone face down on the desk.

Not because I do not care about reach. I do. But because there is something specific and instructive about that notification. The platform did not tell me who read it and found it useful. It told me it was gaining traction. The language is mechanical. Velocity. Momentum. A thing moving faster than before.

Cold coffee on the left side of the desk. The cursor blinking on a draft I have been avoiding. Outside, the sound of a neighbour’s door. Ordinary things. The kind of morning where you sit with a notification and ask yourself: what am I actually doing here?

That question is the one this guide is built around.

Not how to grow your LinkedIn. How to use it in a way that does not hollow you out.

LinkedIn is a performative platform.

LinkedIn’s business model incentivises performance over authenticity

The algorithm rewards engagement, likes, comments, shares, which naturally pushes users toward polished narratives, humble-brags, and curated success stories. You see the same templates repeated. The “fired on a Friday, landed my dream job on Monday” post. The motivational quote with a career pivot. The humble accomplishment wrapped in gratitude. It works because the algorithm amplifies it, and people benefit from the visibility. That is not conspiracy. It is just how engagement-driven platforms operate.

Confident posts. Polished headlines. Messages that arrive feeling like they were written by someone who has never actually read your work.

I spent a long time on that platform doing the same thing. Optimising. Strategising. Finding the right people and saying the right things in the right order.

It worked, in a narrow sense. My numbers went up. But something felt off. Like I was performing a version of connection rather than actually connecting.

Then I started looking at what was actually happening underneath the surface.

And what I found changed how I think about the platform entirely.

The honest picture first

Before we talk about tactics, you deserve the structural truth.

LinkedIn is owned by Microsoft. Its primary business model is not your career. It is LinkedIn Premium subscriptions and Recruiter licences sold to companies. Every algorithm decision, every reach mechanic, every “your post is gaining traction” notification exists to serve that business model, not yours.

The penalty you feel when you stop posting for a few days. The weeks it takes to rebuild to your previous baseline. That is not a coincidence. It is deliberate friction, engineered to keep you producing content that makes the platform feel alive. You are the product. Your consistency is what they are selling to advertisers and recruiters.

The SSI score, the Social Selling Index, was built for B2B sales teams. It measures the behaviours LinkedIn wants from you, posting, connecting, messaging, engaging. It has almost no correlation with career outcomes for regular employees. But because it has a number and a dashboard, people chase it. That is the design working exactly as intended.

The spam in your inbox is not a bug. LinkedIn sold InMail credits and lead generation tools to thousands of sales teams. Your inbox was the product they sold. It will never be fixed because fixing it would reduce revenue.

I am not saying this to make you cynical. I am saying it because leadership, real leadership, starts with seeing clearly. You cannot navigate a system you refuse to understand.

What the platform does reward

Here is where it gets more useful.

The algorithm does reward one thing that is not pay-to-play. Comments from people with large, engaged followings. One genuine comment from someone with twenty thousand followers does more for your reach than fifty likes from regular accounts.

This means community is the real engine. Not content volume. The practice of reading other people’s work carefully, responding to it specifically, building actual familiarity before you ever ask for anything. That is slow. It feels inefficient. It is the only lever that consistently works without a budget behind it.

The platform also rewards specific, repeatable points of view over broad expertise. The people who break through without external credentials share one trait. They own a particular angle that a defined audience waits for. Not “leadership content.” Something narrower. Something like: what distributed engineering teams get wrong about trust, or why most engineering cultures confuse speed with urgency.

Specificity builds following. Following builds algorithmic signal. Algorithmic signal builds reach.

And external credentials, a book, a talk, a public body of work, unlock a different tier of visibility entirely. The platform is structurally biased toward people with proof that exists outside it. That is a long game. It is worth playing.

Building the profile that actually works

Before any of the tactics matter, your profile has to do its two jobs.

The first job is answering the question someone is typing at eleven on a Tuesday night. Not “who has leadership in their headline.” Specific words. The exact language the right person uses when they are looking for someone who does what you do. That language lives in job postings for the roles you want. In the way clients describe their problems in the first email they send. In the questions people ask at conferences when they are in pain. Use their words, not the impressive ones.

The headline is the most important line on your profile and the one most people get wrong. Most headlines describe what the person has. “Senior Engineering Manager at Company X.” That is a fact. It is not a signal. The headline that works describes what the person does for someone specific. “I help engineering teams lead through ambiguity without losing the people.” Or “Building the kind of tech organisations people do not leave.” The test is simple: would the right person read it and think, that is for me?

The About section is where most people write a third-person biography that nobody reads. The About section that works is a first-person paragraph that names the specific problem you solve, says something true that most people in your position would not say publicly, and ends with a direct invitation. Not a summary of your career. A perspective on something that matters.

Here is the structure that works:

The first sentence names who you are in plain language. Not your title. Your role in people’s working lives. “I work with tech leaders who are good at their jobs and losing faith in the organisations they are doing them in.” That sentence is for one person. The person who reads it and feels seen.

The second and third sentences say the thing you believe that most people in your field would not say in public. Not a take designed to provoke. A genuine belief, held clearly, stated without hedging. This is the Blood in the Water principle applied to your profile. The belief that makes someone uncomfortable is also the belief that makes the right person feel understood.

The closing sentence is a specific invitation. Not “let us connect.” A specific thing. “If you are in the first year of a management role and something is already feeling wrong, I want to hear about it.” That is an invitation that filters for exactly the person it is for and tells everyone else clearly that it is not for them.

The Featured section is the one most underused. It is the only place on the profile where you can show someone your actual thinking rather than describing it. One piece of writing that demonstrates your point of view. Not your most popular post. Your most honest one. The one that cost you something to publish. That is the one that does the work.

One last thing on the profile. The banner image. Most people leave it as the generic LinkedIn gradient or use a corporate photo. The banner is eight seconds of attention before anyone reads a word. Use it. A short line of text in your own words on a clean background works better than most professional designs because it is immediately personal rather than immediately branded.

What the algorithm rewards the engagement practice

The community-first principle named above needs a specific practice or it stays as an idea.

Here is what the practice looks like in concrete terms.

Identify twelve to fifteen accounts worth genuine engagement. Not the largest accounts in your space. The accounts whose thinking you genuinely find interesting, whose angle is adjacent to yours but not identical, and whose audience would find your thinking useful. These are the people whose comment sections you will inhabit not as a growth tactic but because the conversation is worth having.

The criteria for the list is simple. Would you read this person’s post carefully even if it had no algorithmic benefit? If yes, they are on the list. If you are calculating the follower count before you decide, they are not.

Engage with those twelve to fifteen accounts before you post anything on a given day. Not a like. A comment that demonstrates you read the post and thought about it. Two to four sentences. Something specific about what they said and one thing it connected to in your own thinking. No flattery without content. “Great post” is not a comment. “You named the thing I have been trying to articulate about psychological safety, which is that it is not a state you achieve, it is a practice you repeat in every meeting, and I have been watching teams get it right once and then slowly lose it because they treated the first success as the end of the work” is a comment.

Do this consistently for sixty days and the algorithmic effect is real. Not because you gamed the algorithm. Because you built genuine familiarity with people whose audiences trust their recommendations, and when those people engage with your posts, the signal is strong enough to move things.

The discipline is the resistance to doing this instrumentally. The moment the comment is written for reach rather than for the conversation, the person reading it can feel the difference. And they stop engaging. And the whole mechanism breaks.

The people who make this work treat it as reading practice with a social layer. Not outreach. Reading.

What to actually post

The article so far has made the case for owning a specific angle. Here is how to find it and what to do with it.

The angle is not chosen. It is noticed. It is the thing you find yourself returning to in conversation, the problem you have a different opinion on from most people in your field, the pattern you see that others seem to miss. You do not decide to have a point of view. You pay attention to where you already do and name it more precisely than you have before.

The test for whether you have found your angle is this: can you describe it in one sentence that contains a specific claim rather than a general category? “I write about leadership” is not an angle. “I write about what happens to new managers in the first six months before the organisation has finished deciding what kind of leader they are going to become” is an angle. Specific enough to exclude most people. Specific enough to make the right people feel it was written for them.

On post format. The posts that consistently perform, across every account I have watched carefully, share three structural features regardless of length or topic.

They open with a specific moment rather than a general claim. “My director forwarded an email at 6pm on a Friday with no context” lands differently than “Communication breakdowns happen in every organisation.” The specific moment is concrete. The general claim is something the reader already knows.

They hold a tension open longer than feels comfortable before they resolve it. The post that says “here is the problem” in paragraph one and “here is the solution” in paragraph two is a post shaped like a LinkedIn post. The post that sits with the complexity for three or four paragraphs before it offers anything resembling a conclusion is a post shaped like actual thinking. Actual thinking earns re-reads.

They end with a question that is genuinely open. Not “what do you think?” which is a call to engagement that signals you want engagement. A question that you actually do not have an answer to, about something the post raised, that a thoughtful person might want to contribute to. “I am not sure whether this is specific to engineering cultures or whether I am just seeing what I work closest to” is a genuinely open ending. “How has your organisation handled this?” is outreach dressed as a question.

On posting frequency. The advice you will find everywhere is to post every day. That advice is optimised for algorithmic growth and for burning out within sixty days. The sustainable frequency is the one you can maintain without sacrificing the quality of observation that makes the posts worth reading. For most people that is two to three times per week. For some it is once. The consistency matters more than the frequency. A post every Tuesday that is genuinely observed and honestly written compounds over eighteen months in ways that daily posts optimised for engagement do not.

The one thing to avoid. The performing post is not always obvious from the inside. Here is how to check. Before you post, ask: am I writing this because I believe it or because I think it will land well? The two are not always different. But when they are, the reader can feel which one it is. Your reader Kathleen, information-rich and suspicious of frameworks, will feel the performing post within the first sentence. She will not comment. She will scroll. And the algorithm will note the absence of early engagement and suppress the post.

Write the thing you believe. Even when it is smaller than you think LinkedIn wants. Especially then.

What this means for how you show up

This is where the template premise comes back.

The tactics in this guide work. The Boolean search strings, the Google X-Ray method, the connection sequencing, all of it is real and I have used every piece of it. But they only work if the intention behind them is honest.

Because LinkedIn is a performative platform. Everyone on it can feel performance. The message that arrived from a template. The post written to get shares rather than say something true. The connection request with a compliment that is obviously a setup.

People are tired of it. I am tired of it. You are probably tired of it too.

Leadership as a verb is the counter to that.

Not as a brand. Not as a content strategy. As an actual operating principle.

A verb requires action. Presence. Choosing, in each moment, whether you are performing leadership or practising it.

On LinkedIn that distinction looks like this.

Performing leadership is posting about the importance of psychological safety. Practising it is writing about the specific conversation where you got it wrong, what you said, what happened in the room, and what you would do differently.

Performing is optimising your headline for clicks. Practising is writing a headline that says exactly what you do and who you do it for, even if that is a smaller audience.

Performing is commenting “great insight” on posts from people with large followings to get visibility. Practising is reading their post properly, sitting with it for a minute, and writing the one thing it genuinely made you think about.

The difference is not always visible from the outside. But it accumulates. Over months, the people who showed up honestly become the people others come back to. Not because the algorithm favoured them. Because humans can tell.

The minimum viable practice

Given everything above, here is what actually makes sense as a sustainable approach.

LinkedIn has two jobs for most professionals. Not five. Two.

The first is discoverability. Recruiters, potential collaborators, and people considering working with you will search for you. Your profile is the answer to their question. It should be honest, specific, and built around the exact words the right people type into search. Not the words that sound impressive. The words that match what someone types at eleven on a Tuesday night when they need someone who does what you do.

The second is reputation confirmation. When someone hears your name, reads something you wrote elsewhere, or gets recommended to you, they will look you up. Your presence on LinkedIn should confirm that you are a real person with a real point of view. Not a resume. A perspective.

That is it. Everything else, the daily posts, the engagement pods, the SSI optimisation, is optional and often counterproductive if it pulls you toward performing rather than practising.

On reaching out

The outreach section of this template exists because sometimes you need to initiate contact. A potential employer. A collaborator. Someone whose work intersects with yours in ways worth exploring.

The frame I use is simple.

You are not looking for targets. You are looking for people you can genuinely help, or people whose work genuinely interests you, and making it easier for them to find that out.

A first-time engineering manager who just got promoted and has no idea how to lead their former peers is not a high-intent buyer. They are scared. The difference between those two framings changes everything about what you write and how it lands.

Write to the person. Not to the role, the company, the lead score, or the pipeline stage. The person.

That means doing enough reading before you reach out that you can reference something specific. Not “I loved your post about leadership.” Which post. What in it. Why it connected with something you are thinking about right now.

It means being honest about why you are reaching out. Not hiding a request inside three paragraphs of flattery. People respect directness. They are suspicious of warmth they did not earn.

And it means accepting that most outreach will not get a response. Not because you did it wrong. Because people are busy and their inbox is full of messages that trained them to ignore it. Your job is to be the message that felt different enough to read. Not to convert every send.

When it is not working

The platform will frustrate you. Not occasionally. Structurally and repeatedly.

There will be weeks where a post you worked hard on gets minimal reach and a post you dashed off in ten minutes gets thousands of impressions. The algorithm is not a feedback mechanism for quality. It is a feedback mechanism for engagement patterns, which correlates loosely with quality and strongly with timing, format, early engagement velocity, and factors you cannot fully control.

Here is what to do in those weeks. Not a tactic. A reorientation.

Ask yourself which metric you are actually optimising for. If it is reach, the low-reach week is a failure by that metric. If it is the quality of the one conversation that started from that post, it may be a good week. The platform’s native metrics, impressions, reactions, comments, measure the platform’s success. They do not measure yours. Your success is the recruiter who found your profile six months after you updated it and reached out for the right role. The reader who subscribed to your newsletter three weeks after encountering your comment on someone else’s post. The collaborator who had been reading your work for a year before they sent the message that became the project.

Those things do not appear in your analytics dashboard. They are the actual compounding. They happen independent of any single post’s performance.

The specific practice for the low-reach weeks is to return to the twelve to fifteen accounts. Read them. Comment genuinely. Not to repair your reach. Because the habit of genuine engagement is the only thing that produces consistent results over time, and the weeks when you do it without a metric reward are the weeks that test whether you actually believe that.

There is one more thing worth naming about the hard weeks. The impulse, when reach drops, is to change what you are doing. Post more. Change the format. Try a different style. That impulse is the platform’s design working on you. The friction engineered to keep you producing. Most of the adjustments made in response to a bad week make things worse rather than better because they move you away from your actual angle and toward what seemed to perform last time, which is always slightly wrong.

The question to ask in a bad week is not: what should I change? It is: what was I doing when it was working that I have stopped doing? The answer is almost always the same. Writing things you actually believed rather than things you thought would land. Engaging with the twelve to fifteen accounts before you posted rather than after. Updating the profile to reflect where your thinking actually is rather than where it was six months ago.

Go back to the basics. Every time. Not because the basics are inspirational. Because they are the only things that consistently work.

The long game

The platform will frustrate you. It is designed to. The reach will feel low relative to the effort. The spam will keep coming. The algorithm will penalise you for living your life.

The people who find LinkedIn genuinely useful over time are not the ones who cracked the algorithm. They are the ones who decided to use it as a place to think in public, to be findable by the right people, and to occasionally make genuine contact with someone whose work they respect.

That is a much quieter use of the platform than most guides will tell you.

It is also the one that compounds without burning you out.

Leadership as a verb, on LinkedIn as everywhere else, is the choice to keep showing up as the same person you are when no one is watching. Not because it is strategically optimal. Because it is the only version of this that feels like it is actually worth doing.

The notification is still on my phone. “Your post is gaining traction.”

I will read the comments later. Not to see what is performing. To find the one person who said something true.

That is the whole practice.

This template is a starting point, not a fixed system. If something does not fit your situation, change it. And if you try something and it does not land the way you expected, bring it to the Q&A. That is what the room is for.

LINKEDIN AS A PRACTICE

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

Because nobody wrote down what the role actually was.

I have seen it happen more than once. A senior leader joins a company fractionally, the relationship starts well, and then month by month the scope expands more standups, more strategy sessions, more Friday emergencies until they are doing the full job on a fraction of the pay. Not through bad faith. Through the natural pull of a company toward someone whose judgment they have learned to trust.

The contract is the only place to stop that before it starts.

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My Take on 1:1s: The Partnership Protocol

I don’t treat 1:1s as a reporting line; I treat them as a shared partnership. While it’s tempting to default to the ‘famous project status,’ I believe that is a manual task a manager should perform daily through the system’s natural flow. Status is data; the 1:1 is about human oxygen.

These sessions belong to the team member. As a leader, your job is to provide the ‘unvarnished truth’ while creating a space safe enough for them to share their own ‘scars’ and breakthroughs. You must educate your team on how to own this time moving them from the ‘Invisible Cage’ of seeking permission to a place of radical accountability.

When you stop using 1:1s for throughput and start using them to strengthen the human-system contract, you aren’t just managing; you’re building the character of the future society that has to live in the structures you’ve built.

Why Use This Document?

Standard 1:1s often suffer from “trust leaks” where the true state of performance is hidden. This document provides a structured framework to:

• Align Vision: Ensure your immediate work supports your 5 to 10-year professional goals.

• Audit Growth: Identify the best strategies to improve performance by balancing technical knowledge with the right professional behaviours.

• Remove Blockers: Use the “One to One Notes” to address “red tape” and logistical hurdles that prevent you from reaching your next Career Level (Levels 1–4).

• Quantify Impact: Maintain a continuous Achievements Timeline to turn your work into a “diary of success” for annual reviews and promotions.

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When a system breaks, a project fails, or a major bug reaches the customer, the immediate reaction is to find out who caused it. The team gathers in a room or on a video call. They look for the person who made the bad call. They look for the line of code that broke the build. They look for the decision that, in retrospect, was clearly wrong.

They write it all up. They assign a “lesson learned.” They file the document somewhere in a shared drive where no one will ever read it again.

Then, the exact same thing happens again three months later.

Why? Because nothing in the environment actually changed. You found the person who made the mistake, but you did not fix the system that made the mistake possible. You treated the symptom, not the disease.

This template runs a different kind of session. It is a different kind of post-mortem. Instead of asking who made the mistake, it asks a much more powerful question: What in our environment made that choice feel like the right one at the time?

That single question changes everything about the conversation that follows. It moves the team from blame to curiosity. It shifts the focus from human error to system design. And most importantly, it actually fixes the culture.

In this guide, we will break down exactly how to run this new type of post-mortem. We will use simple steps, clear questions, and a proven four-part structure. By the end of this guide, you will have a complete “patch” to fix your broken review process.

Why traditional post-mortems break trust

Before we fix the process, we need to understand why the old process is broken.

Think about the last time your team had a major failure. Maybe a server went down on a busy sales day. Maybe a product launch was delayed by two months. Maybe a key client cancelled their contract.

How did the team react? Usually, there is a moment of panic. Then, there is a search for a target.

In traditional post-mortems, the hidden goal is often to prove that it was not my fault. People bring logs, emails, and chat messages to prove they did their job correctly. They point fingers at other teams. They say things like, “Well, I told the design team we needed the assets by Tuesday, and they sent them on Thursday.”

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That is not a sales skill. It is a leadership skill. It is the ability to understand another person’s problem clearly enough to know whether what you have built is genuinely the right answer and to be honest when it is not.

This framework is for engineers and technical leaders who are making that transition. Not from builder to salesperson. From builder to someone who leads with questions before they lead with solutions.

Six stages. From establishing your presence to closing the deal to handing over a data security FAQ before the client thinks to ask for one.

THE BUILDER-TO-FOUNDER Pitch Framework

Bring your questions to the Q&A. Especially if you have hit the moment where the product is ready but the conversation is not.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

Most people feel this problem but do not know how to fix it without sounding like they are just asking for a promotion. The fix is not to ask for a favour. It is to make a case.

The most effective case is not “I deserve more.” It is the market already has a name for what I do, and that name is not what is on my contract. Here is the evidence.

This template walks you through the three things you need to build that case the gap between your job description and your actual work, the market data that names your level, and the conversation script to bring it to your manager without it feeling like a confrontation.

Role Naming

Bring your questions to the Q&A. Especially if the conversation with your manager did not go the way you expected.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

This one is different. Not because the structure is different the six sections are standard. But because it starts from a different question.

Not: what does the organisation need from you. But: where are you actually going, and does the role you are in right now fit that direction.

The most useful thing you can do before filling in any section is sit with that question honestly. If the answer surprises you, that is information.

Write it down anyway.

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Most career advice skips the hard part. It tells you to update your LinkedIn, learn a new skill, optimize your resume. It assumes the problem is visibility. It is usually not. The problem is that somewhere along the way, you started building someone else’s version of your career accumulated from advice, from what got rewarded, from the slow pressure of other people’s definitions of growth.

I built this template after one too many conversations where someone asked me: where do I even start.

It is not a job search checklist. It is a set of questions to ask yourself before you touch any of that. Starting with the one that matters most is the direction you are moving actually yours.

There are four stages. The first one has nothing to do with the market. It has everything to do with you.

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In the early hours of a Tuesday morning, a core payment processing system for a mid-market SaaS platform collapsed. The on-call engineer, bleary-eyed and fueled by caffeine, pulled up the repository to trace the failure. They found a sophisticated block of logic efficient, sleek, and entirely unfamiliar.

They checked the git blame. The code had been committed six months prior, but the “author” hadn’t actually written it. It was the result of a complex prompt interaction between a developer and an LLM. When the engineer tried to debug it, they realized a terrifying truth: Nobody on the current team actually understood why the code worked, so nobody knew how to fix it now that it didn’t.

We are currently living through the “Velocity Trap.”

AI tools like Cursor, Copilot, and specialized agents have made it possible to ship features at a rate previously unimaginable. But we are trading shared understanding for raw output.

As a Tech Leadership Advisor, I see this pattern emerging everywhere, we are scaling individual shortcuts, but we are eroding our systems.

If your team cannot explain the trade-offs baked into your core logic, you don’t own your product; you are simply renting its functionality from an algorithm.

The Erosion of Collective Ownership

Traditionally, software engineering was a social contract. Writing code was only half the job, the other half was building a mental model that could be shared across a team. This happened through whiteboarding, heated debates over architecture, and rigorous peer reviews. These “inefficiencies” were actually the guardrails of accountability.

AI-assisted coding often bypasses these conversations. When a developer uses an AI to generate a 50-line function, they often skip the “struggle” of the logic. That struggle is where the mental model is built. Without it, we create Individual Knowledge Silos. The developer understands the intent, the AI understands the syntax, but the systemic reasoning is lost in the ether.

When collective ownership dies, technical leadership becomes a game of “Whack-a-Mole.” You aren’t managing a codebase you are managing a black box.

Defining the “Accountability Gap”

There is a massive difference between legal responsibility and operational accountability.

• Legally, the CEO and the Board are responsible for the company’s output.

• Operationally, the VP of Engineering is on the hook for uptime.

But mentally, a subtle shift is happening in the trenches. When a bug occurs in AI-generated logic, there is a psychological tendency to shift blame to the tool: “The AI suggested this approach.”

This is the Accountability Gap. It is a hidden “Market-Blocker.”

If you cannot audit your logic, you cannot guarantee:

1. Security: Are there hallucinated dependencies or insecure patterns buried in the speed of delivery?

2. Compliance: Can you prove to a regulator exactly how a data-processing decision was made?

3. Scalability: Can this logic handle a 10x load, or was it optimized only for the immediate prompt?

As an advisor, I tell leadership teams: Technical accountability is not a technical problem; it is a management discipline.

Pillar 1: The “Human-in-the-Loop” Review Framework

To close the accountability gap, we must evolve the Code Review. We can no longer just check if the code “works” (the compiler or the AI already did that). We must check if the human understands it.

The “Explain-Back” Protocol

I advocate for a new standard in Pull Requests (PRs). If a block of logic was substantially generated or assisted by AI, the engineer must provide a “Human Logic Summary.”

This isn’t a comment on the code; it’s a justification of the Reasoning.

• What was the primary trade-off made here?

• What edge cases did the AI miss that I had to manually correct?

• Why is this approach better than the alternative?

If an engineer cannot provide these three answers, the code is not ready to ship. We must prioritize Understandability over Velocity.

Pillar 2: Prompt Governance & Documentation

In the new world of tech leadership, the Prompt is the new System Design.

If the core logic of your application is being dictated by the instructions given to an LLM, then those instructions are just as important as the code itself. Yet, most prompts are ephemeral lost in a chat history or a local IDE session.

Versioning Intent

We need to start versioning our “Intent.” This means documenting the high-level prompts and the architectural decisions that led to them.

• The Tech Ledger: Keep a record of high-risk AI-generated modules. These are areas of the codebase that require “High-Frequency Auditing.”

• Documentation as Code: Use AI to help document, but ensure the human “signs off” on the accuracy. AI-generated documentation that is never read is just more noise in the system.

Pillar 3: Cultivating Humane Tech Leadership

High-performance teams are built on trust and sustainable pace. The “Move Fast and Break Things” mantra has a high cost in the AI era. It leads to burnout when engineers are forced to fix “Black Box” bugs under high pressure.

The Leader’s Role: Rewarding the “Why”

As a leader, you must change what you celebrate. If you only celebrate the “Ship Date,” your team will use every AI shortcut available to hit it, regardless of the technical debt created.

Instead, celebrate System Robustness.

• Reward the engineer who found a flaw in an AI suggestion.

• Reward the team that took an extra day to ensure their AI-assisted refactor was fully understood by the junior members.

The “Calculator Effect” in Mentorship

We face a generational risk.

If junior developers use AI for everything, how do they develop the “Senior Intuition” required to lead in five years? Leadership must carve out “Manual Zones” areas of the product or specific sprints where AI tools are sidelined to ensure the foundational “mental muscles” of the team remain sharp.

Pillar 4: Managing External Tech Partnerships

Many companies rely on external consultants to build their AI initiatives. This is a high-risk area for accountability.

When a consultant leaves, they often take the “prompt intuition” with them. As a Strategic Advisor, I help companies manage these relationships effectively. The goal is to ensure that external consultants don’t just leave behind a “working app,” but a transfer of understanding.

Your rule for consultants: If my internal team can’t maintain the AI agents you built after you leave, the project is a failure, regardless of the ROI on day one.

Strategy-First, AI-Second

In the end the ultimate goal of any tech initiative is to drive measurable business outcomes to remove Market-Blockers and increase ROI.

AI is a breathtakingly powerful tool for reaching those goals, but it is a poor master. Technical accountability is the “Human” anchor that keeps your company from drifting into a sea of unmanageable code and opaque systems.

The Accountability Litmus Test for CEOs: Ask your CTO today: “If our top three developers left tomorrow, could the remaining team explain the trade-offs in our core AI-generated logic by the end of the week?”

If the answer is “No,” you don’t have a technical problem. You have a leadership opportunity.

It’s time to stop hitting “Archive” on our understanding and start building a strategic bench that can actually lead the machines they use.

Who am I?

I’m Diamantino Almeida, and I’ve spent my career at the intersection of high-growth engineering and strategic leadership.

From scaling technical teams to advising CTOs and Founders, my focus is on “Leadership as a Verb“, the idea that leading is an active, evolving practice, not a static title. Having navigated the shifts from manual infrastructure to cloud, and now to Agentic AI, I’m dedicated to helping the next generation of engineers find their footing in a world that is moving faster than ever.

Beyond advisory, I’m an active Top global 9% *mentor on *MentorCruise, where I help developers and leaders bridge the gap between “writing code” and “delivering business value.”

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Early work not representative of current direction.

I want to talk about something that’s been on my mind a lot lately how teams can actually use AI agents in a way that makes sense, without losing what makes us human. I’ve spent years working in tech, leading teams, and trying to figure out how to make tools work for people, not the other way around. And let me tell you, AI especially these new AI agents can be a game-changer if we use them right.

But if we don’t, they can just add more noise, more confusion, and more distance between people who should be working together.

I fear many companies would see AI, as a replacement for cheaper workforce, the same way companies outsource workforce from other countries where labor is cheaper, nothing wrong with that, but it creates division and a blame culture. No one likes to push a company to new levels of automation, adoption of certain work standards, and such. And in the end we receive a thank you letter and a redundancy package.

1. Custom AI agents as “team experts”

Let’s talk about what these AI agents actually are. They’re not magic. They’re not replacements.

They’re tools that can be shaped to fit the way your team works.

Domain-Specific Agents

Every team has its own way of doing things its own knowledge, its own history, its own quirks.

• DevOps teams have their logs and deployment strategies.

• Security teams have their compliance rules and risk assessments.

• Product teams have their user insights and roadmaps.

Instead of using a generic AI tool that doesn’t get your team, why not train an agent to specifically understand your team’s world?

For example:

A DevOps agent could analyze monitoring logs, suggest fixes based on past incidents, and even draft runbooks. But here’s the key, the agent doesn’t make the final call. It suggests, and a human approves. That way, the team stays in control, but the agent helps them move faster.

A Security agent could offer a “Check for Compliance” service that Product teams can use before launching a new feature. No more waiting for a security review to be scheduled just ask the agent for a quick sanity check.

Self-Service for Other Teams

One of the biggest wastes of time in any company is the back-and-forth between teams. “Hey, can Security review this?” “DevOps, what’s the status on this deployment?”

With custom AI agents, teams can expose certain capabilities like a Slack command or an internal API so other teams can get answers without waiting for a meeting or an email response.

Human Decision Gate

This is non-negotiable, AI should inform, but humans should decide. Always. No exceptions. If an agent suggests a fix, a human review it. If it flags a risk, a human assesses it.

The goal isn’t to remove people from the equation it’s to give them better information so they can make better decisions.

2. AI as a force multiplier for collaboration

AI agents shouldn’t just be about getting things done faster. They should help teams work together better.

This idea of pushing hundreds of lines a second, only create a huge cognitive overload and more things to test and do. Be smart on this one.

Live Documentation & Advisory

How much time do we waste digging through outdated docs or asking, “Wait, how did we set this up again?” An AI agent can keep documentation alive updating API specs, onboarding guides, or architecture notes as the code and discussions evolve.

No more “oops, that wiki page is from 2020.”

Pattern Recognition

Ever feel like you’re solving the same problem over and over? An agent can flag recurring issues like, “Hey, this error looks like the outage we had last month.

Here’s how we fixed it then.” That way, teams spend less time reinventing the wheel and more time actually moving forward.

Process Alignment

AI can also nudge teams toward best practices. Imagine an agent saying, “This change requires a security review should I schedule it for you?” It’s not about policing; it’s about making sure nothing slips through the cracks.

Workflow Integration Map

Here’s where managers come in. You don’t need to control every little thing the AI does, but you do need to define:

• Where does AI fit in? (Input: data, Process: analysis, Output: recommendations)

• Where do humans step in? (Judgment, ethics, prioritization)

Clarity here prevents chaos later.

3. AI for creative and strategic work

This is where things get interesting. AI isn’t just for grunt work it can help with the big stuff, too.

Idea Generation

Stuck on a problem? An agent can brainstorm new features, architectures, or strategies based on your team’s knowledge and industry trends. But and this is important don’t stop there.

Imagination Safeguard

After the AI spits out ideas, the team should run a divergence session no AI allowed. Generate your own ideas, compare them to the AI’s suggestions, and ask: Which ones feel original? Which ones are actually feasible? This keeps the team’s creativity sharp.

Risk Assessment

Before rolling out a big change, an agent can simulate outcomes or flag potential risks. “This change might conflict with Marketing’s campaign should we sync with them first?” It’s like having a second set of eyes that never gets tired.

Creativity Retention

Here’s a rule I swear by: Hold “AI-free” ideation workshops every quarter. No tools, no agents, just people thinking and creating together. It keeps those intuitive, creative muscles strong.

How Managers Can Support Teams (Without Micromanaging AI)

Managers, this one’s for you.

Your job isn’t to control how teams use AI it’s to set them up for success.

1. Set Guardrails, Not Handcuffs

• Define boundaries: What can the agent do? (Draft code? Sure.) What’s off-limits? (Deployment decisions? Absolutely.)

• Ownership model: Assign an “AI steward” in each team someone who handles training, updates, and audits. Keep accountability human.

• Ethics & bias review: Regularly check the agent’s outputs for accuracy, fairness, and alignment with your company’s values.

2. Foster a Culture of Trust and Curiosity

• Encourage experimentation: Let teams test agents in low-risk areas first like documentation or code reviews, before scaling up.

• Transparency: Make sure everyone knows how their agent works what data it uses, what it can’t do. No black boxes.

• Feedback loops: Hold monthly “AI Learnings” sessions. What worked? What failed? Keep the conversation going.

3. Focus on Outcomes, Not Oversight

• Metric-driven autonomy: Don’t measure how much the team uses AI. Measure the impact, like faster onboarding, fewer bugs, or happier teams.

• Cross-team audits: Have Security review DevOps’ agents. Have Product check Data’s insights. Fresh eyes catch blind spots.

• Skill preservation: If human creativity or problem-solving starts to decline, dial back the AI. The goal is to enhance skills, not replace them.

4. Invest in “AI Literacy”

• Training: Run workshops on prompt design, agent tuning, and ethical use. Everyone should know how to work with AI, not just how to turn it on.

• Documentation: Encourage teams to document their agent’s strengths, weaknesses, and “personality.” “Our agent is great at optimization but overly cautious on risk.”

• Skill alignment: AI should reinforce human skills critical thinking, collaboration, ethics not automate them away.

Custom AI agents as a service

Let’s make this concrete. Here’s how it could work in a real team:

How it works:

1. Teams train their agents with their own data and expertise.

2. Other teams interact with them via Slack, APIs, or Jira plugins.

3. Agents provide insights, but humans make the final call.

Key mindset shifts

To make this work, we need to change how we think about AI:

• From “AI as a Tool” to “AI as a Teammate”: Treat agents like tools that guide them, critique them, help them improve.

• From “Control” to “Collaboration”: Managers should empower teams to shape their AI, not police it.

• From “Siloed Expertise” to “Shared Intelligence”: Agents can bridge knowledge gaps between teams, reducing friction and duplicate work.

New dimensions for human-centered adoption

Finally, let’s talk about what really matters:

• Human Decision Gates: No matter what, people make the final calls.

• Imagination Calibration: Regularly compare human ideas vs. AI ideas. Keep your team’s creativity alive.

• Wellbeing & Engagement Metrics: Track how AI use affects motivation and collaboration. If people feel replaced or stressed, adjust.

• Visual Workflow Maps: Make it clear where AI supports, where humans decide, and where skills grow.

A question for you

So, here’s what I want to know.

What’s the first mission you’d assign a custom AI agent?

• Log analysis?

• Documentation updates?

• Security scanning?

• Creative ideation?

Or is there some recurring bottleneck in your team that you’d rather automate first?

Let’s start small, learn fast, and keep the human element at the center.

Because at the end of the day, that’s what this is all about.

Not replacing people empowering them.

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.

Early work not representative of current direction.

Tech is our saviour. Or so we were told.

Every problem has an app.

There’s a startup for every inconvenience. A dashboard for every complexity.

A note to leaders and those becoming leaders.

Pay attention.

Not just to your revenue graphs, OKR dashboards, or Slack notifications.
Pay attention to how the convenience of technology is quietly reshaping the way you lead and the way you treat people.

We spend more time looking at screens than looking at faces. More time interpreting spreadsheets than interpreting body language. More time attending to dashboards than attending to discomfort in the room.

On the surface, it feels harmless. After all, these tools make things faster. The AI coach gives you leadership tips in three minutes.

The platform summarizes your meetings so you can “save time.” The workflow app smooths over all the awkward human parts of collaboration.

But convenience has a cost. One we rarely calculate.

Every time we choose efficiency over presence, data over dialogue, we chip away at something essential. Trust. Relationship. The subtle signals that tell us what another human really needs.

Leadership is not about shaving seconds off processes. Leadership is not about automation.

Leadership is, at its core, about attention.

Where you place it. How you hold it. Who gets it.

So before we talk about how tech “broke leadership,” we need to slow down and acknowledge this truth:

If you can’t look your people in the eye, if you can’t stay with their fears or frustrations, if screens mediate every interaction then no tool in the world will make you a better leader.

A chatbot for every emotion.

We’ve been sold a story, for every human ache loneliness, hunger, grief, boredom somewhere, someone in a hoodie is coding the fix.

Someone, probably in a Palo Alto garage or a Berlin co-working space, is quietly "changing the world."

And yet, in spite of all that, we live in a world where the deepest problems poverty, inequality, climate collapse, the slow corrosion of trust in one another remain not just unsolved, but in many ways worsened.

Why? Maybe because there’s no real profit in solving them.

When Crisis Met Tech COVID as the Stress Test

The cracks were always there, but the pandemic made them obvious.

In 2020, when frontline nurses were reusing masks and governments scrambled to secure PPE, where did leaders run? To medical experts? To networks of community care?

No. In many places, they ran to Silicon Valley boardrooms.

Meeting after meeting, governments outsourced leadership to tech:

• contact-tracing apps that most populations never used or couldn’t rely on,

• dashboards that gave the illusion of clarity without context,

• AI models projecting viral spread useful in theory, but near useless for guiding frontline behaviour.

Meanwhile, the people who were actually holding the line bus drivers, care workers, teachers, supermarket shelf-stackers weren’t invited to those conversations.

We have to ask ourselves: was this leadership?

Or was it management outsourced to those best at making things scale whether or not they understood what was being scaled?

It revealed something deeper tech hasn’t just influenced leadership. It’s reshaped it, hollowed it out, and in many ways… broken it.

Leadership, Productised

Watch the next NVIDIA keynote or Apple WWDC demo. You’ll see something eerily close to a religious ceremony.

This isn’t just a presentation. It’s a carefully choreographed revelation.

The CEO doesn’t appear as a steward, but as a visionary saviour. The audience leans in like congregants.

And the message is clear:

"We’ve glimpsed the future and we’re generous enough to let you in on it."

• Steve Jobs didn’t just launch phones; he launched meaning.

• Elon Musk doesn’t just talk rockets he spins existential purpose.

• Sam Altman talks about AI less like a CEO and more like a cautious prophet.

This is the messiah complex, built into corporate form.

And the danger isn’t just the theatre. It’s what that theatre displaces. Instead of leadership rooted in responsibility and accountability, we’re left with a brand of leadership that’s mostly optics: performance, charisma, and the ability to narrate the future compellingly.

The problem?

We, the audience, often buy it. In a world that feels unfixable, we long for certainty. We crave saviours.

But leadership built on salvation without participation is not leadership at all.

It’s marketing.

From Human Messiness to Digital Perfection

Traditional leadership is messy. It requires presence sitting in complexity, listening to people, acknowledging frustration, learning to hold tension.

But in tech culture, messiness is a bug to be eliminated.

Meetings drag? Replace them with an AI notetaker.
Disagreements grind? Let the algorithm calculate the “optimal” decision.
Collaboration feel awkward? Push people into structured workflow apps until talking itself feels unnecessary.

Slowly but surely, tools designed to assist functionality begin to replace relationship itself.

We end up in meetings where people barely speak because “the bot will transcribe anyway.” Participation becomes optional or worse, performative. Suddenly, people aren’t present with one another; they’re avatars, curated projections of themselves.

But here’s the truth relationship is the essence of leadership.

It isn’t found in the decisions, or in the dashboards, but in the space between people.

In trust.

In disagreement managed face-to-face. In a leader seeing your eyes narrow and realising they’ve gone too far.

Presence cannot be automated.

And yet tech culture teaches us that everything important can be outsourced if only you code it cleverly enough.

Leadership, Subscribed

Once upon a time, leadership was practiced. You showed up, you tried, you stumbled, you repaired. You earned trust slowly, by presence and persistence.

Now leadership is transactional. A purchased product.

• Buy the course.

• Subscribe to the AI leadership coach.

• Download a book of CEO hacks.

• Hire a PR firm that polishes your “vision.”

• Use ChatGPT to be a better leader.

Even worse, many so-called leaders no longer lead they defer.

• They wait to see what the metrics say before making a decision.

• They let dashboards and consultants substitute for judgment.

• They benchmark competitors instead of exercising courage.

Employees become reduced to datapoints, nudged, tracked, scored, nudged again.

No wonder workplaces often feel soulless. Layoffs arrive as automated calendar invites. Recognition is reduced to emojis on Slack. Even “culture” itself is delegated to software.

This isn’t leadership.

This is logistics with better branding.

Why It Happened Follow the Incentives

To be fair, this isn’t just personal failure. Tech leaders act this way partly because capital rewards theatre over care.

Wall Street prizes growth curves, not emotional presence. VCs write bigger checks for founders who sound like messiahs, not stewards. Boards hire charismatic futurists, not humble listeners.

Leadership is being contorted because the systems around it insist that is what leadership must look like.

So if leadership looks broken in tech, it’s not just cultural. It’s systemic baked into the economic incentives.

But Tech Hasn’t Only Broken Leadership

Here’s where honesty requires balance.

Technology isn’t only corrosive. It has enabled incredible breakthroughs in how we connect, collaborate, and even survive.

• At the peak of lockdowns, Zoom reported 300 million daily meeting participants, which meant millions of jobs and communities continued to function.

• Telemedicine brought care to patients in rural areas who otherwise might have gone unseen.

• The rapid sequencing and open-source sharing of COVID genomes accelerated vaccine development at unprecedented speed.

So yes, tech flattened relationships in some ways but it also saved many.

The point isn’t that tech is evil. It’s the business models behind it.

It’s that tech culture, when it replaces leadership with metrics and spectacle, creates fragility in the places we most need humanity.

What Leadership Theories Teach Us

We don’t need to reinvent leadership theory to recognise the problem.

We already have models:

• Servant Leadership (Robert Greenleaf): leadership grounded in serving others before oneself, building trust through humility.

• Adaptive Leadership (Ronald Heifetz): leaders must guide people through uncertainty by holding space for discomfort and learning, not by pretending to have all the answers.

• Collective Leadership frameworks: power is shared, not concentrated; decisions are distributed across groups rather than hoarded at the top.

All of these contradict the “saviour founder” myth. And all of them point toward one truth, leadership isn’t performance.

It’s practice.

How We Might Rebuild

Tech broke leadership. But tech, used differently, might also help us rebuild it. Here’s how.

1. Design for Presence, Not Just Productivity

Imagine tools designed not to eliminate meetings but to make them deeper. Tools that prompt pauses, reflection, and check-ins before sprinting into tasks.

What if your calendar rewarded rest as much as hustle? What if project dashboards asked not only “Is the work done?” but also “How is the team doing?”

Presence isn’t inefficient. It’s what makes leadership real.

2. Re-centre Relationship

The best leaders use technology to amplify connection, not outsource it.

• Leave a voice note instead of sending a cold Slack ping.

• End a Zoom meeting not with “next steps,” but with “what’s one thing weighing on you today?”

• Replace top-down OKR templates with collective goal-setting sessions.

Tech should enable human connection, not avoid it.

3. Retire the Saviour Narrtive

Stop waiting for the next Elon, Sam, or Steve to arrive telling us what’s next. That model is bankrupt.

What if the next revolution in leadership wasn’t billion-dollar exits but local teams solving community problems together?

What if leadership was reclaimed not by visionaries at the top, but by engineers, managers, and communities asking deeper questions before building?

The solo-hero story has run its course. The future is collective, distributed, accountable.

4. Build Humane Systems

If we want healthier leadership, we must also change the systems that shape it.

• Do we reward those who coach others or those who shout the loudest?

• Do our boards prize ethical restraint or quarterly hypergrowth?

• Do platforms elevate truth or outrage engineered for clicks?

Culture is code. Incentives are infrastructure. Both can be rewritten. But only if leaders have the courage to say: we got this wrong.

Leadership, Reclaimed

Here’s the hardest truth: tech didn’t just break leadership we did, by buying into the illusion. By worshipping saviours instead of holding them accountable. By equating charisma with wisdom, scale with care.

But this era AI agents, avatars, automation might be the call back to something older, slower, and more human.

Because the future of leadership isn’t another roadmap, app launch, or keynote reveal.

It’s the questions we dare to keep asking:

• Who am I serving?

• Who gets left out when I make this decision?

• How do I build environments where people feel safe, seen, and strong?

The leader of tomorrow won’t necessarily be the loudest, the richest, or the most followed. They’ll be the ones who choose to lead with others, not over them.

The ones who can hold tension, not erase it.

The ones who can ground their nervous system before trying to regulate anyone else’s.

The ones who remember that technology is a tool, not destiny.

And maybe just maybe the next shift in leadership won’t look like a keynote at all.

It will look like a group of people sitting quietly, phones aside, looking each other in the eye, and saying simply:

"Let’s figure this out, together."

About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously—from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human—one verb at a time.

That was never the whole truth, but it was close enough for many years. If you had seen a few more problems, worked through a few more mistakes, and learned how to stay calm when things were unclear, you could help someone else find their way. You had context. You had judgment. You had a bit of distance from the panic they were still inside.

That still matters.

But something has shifted.

We now live in a world where advice is abundant, expertise is easier to summon, and AI can produce a confident answer faster than most people can finish the question. That changes the shape of mentorship. It does not make it less important. It makes it more human.

Because if answers are cheap, then what people really need is not more answers.

They need better thinking.

They need help with judgment, with patience, with pattern recognition, with knowing what to ignore and what to pay attention to. They need someone who can sit with them long enough to help the fog lift. That is what mentorship has always been at its best. Not instruction. Not performance. Not a polished list of tips.

A relationship that helps someone become clearer.

Mentorship is not a title

A lot of people think mentorship starts when someone gives you permission.

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