DEV Community: Samson

Tethering the Exponential: My strategy for keeping up w/AI

Samson — Sun, 26 Oct 2025 09:14:20 +0000

Estimated Reading Time: 9 minutes

Contents

Preamble

On AI & The Overton Window of Weirdness

On Strategy: Rules of a tether

On Tethering to an exponential

On Execution

On Attention Systems

On Intelligence Engines

On Agent Chains

On Competitiveness

On Accountability

On Meaning

"The pace of change is so fast that soon enough visionary will become a lifestyle, rather than a breakthrough thing." - Salim Ismail

Preamble:

It's been a long time since journaling; Two years, and everything has accelerated. In that time I've been mentored by world-class operators in AI, Data, Cybersecurity, Strategy and Transformation. They rewired how I think about all of knowledge work & professional services.

Along the way I've built a strategy to keep up with AI and it's working (for now).
I became the AI SME at a major Professional Services Firm and a Finalist in three enterprise categories for the 2025 Australian AI Awards (Leader OTY, Consultant OTY and Rising star OTY, alongside impressive operators).

A key learning that matters for this piece: Strategic impact scales with massive action. Define Win Conditions, build an Attention System, and tether yourself to the complexity curve long enough to see the non-obvious edges. That’s how you convert an exponential into an advantage.

Soon I'll be stepping into an AI Solution Architect role with people I'm excited to build with and learn from.

Consider this post a trade: I'll share my proven strategy to meet you - the serious operators pushing this once-only moment in a humanist direction at the beginning of the Age of Intelligence.

AI and the Overton Window of Weirdness

In 2018 I watched OpenAI 5 play some of the worlds best Dota 2 players real-time. The bots showed mechanical skill, psychological pressure and even used a novel exploit mid-match. As a former Australian pro in real-time-tactics games, I felt the floor move, the writing was on the wall "A country of geniuses in a data centre" had been proven feasible.

"A country of geniuses in a data centre." - Dario Amodei

Not many knew it yet, but this small team of ambitious researchers had just blown open the Overton Window of Weirdness. Since GPT 3 I've treated AI as a domain where reality reorganises faster than our priors update.

"The Overton window of weirdness, is the range of things acceptable to spend one’s time doing, when it is narrow we are optimisers, and when it's wide a societal random walk occurs leading to discovery."- Matt Webb

Highlight of OpenAI Five - inventing an exploit to win,
Listen to the crowd roar to hear the magnitude:

On Strategy: rules of a tether

Fast Definitions

Win Conditions: self-evident or measurable states that, if achieved, mean "we won".
Axioms: explicit strategic bets about the interplay between how the world behaves and your win conditions.
Plan: next actions contingent on the axioms of strategy.
Tether: a designed Attention System that keeps you attached to an accelerating Complexity Curve long enough to find the non-obvious edges of any problem space.

Hard rules

Write out your win conditions. If they aren't defined, it isn't strategy.
Input mode beats willpower. Cognitive load shifts, pick appropriate mediums for how you feel on the day.
Build an attention system. Compress entropy into signal with a repeatable stack. Take your trusted sources and work their content for digestibility.
Publish artifacts regularly. Build tools, slide-decks, diagrams, write articles, attend events. Increase your luck surface area when solutioning.
Trade IP for network. Collaboration compounds faster than hoarding.

Tethering to an exponential

Axiom A - Frontier problem convergence:

As experience in a field compounds, the problem space for experts typically narrows. Teams working on the frontier tackle near identical problems on similar timelines. Most options to solve a problem are not yet possible, or too expensive; a small number of options remain. Advantage accrues to those who stay on-task in the problem space the longest.

Axiom B - Complexity scales with exponentials:

AI progress is now exponential; capability is scaling across every industry. The complexity curve is rising in lockstep with each improvement, and individuals are outpaced in every industry unless they build Attention Systems that denoise the release landscape and stay linked to macroeconomic signals.

In this high-complexity state, non-technical executives are overwhelmed, and their strategic axioms drift out of alignment with future win conditions. The remedy is explicit: tune your Attention Systems to remain tethered to the curve as long as possible. Even if you’re eventually bucked off, you will see more and sooner than peers who let go early.

On Execution

"Advantage accrues to those who stay on-task in the problem space the longest."

Assumptions:

1. Move quickly to accumulate deep AI experience in the target area.
2. Design highly leveraged Attention Systems that keep us with the problem for as many hours per week as possible.

→ Result: We stay tethered to the exponential and pull our thinking far ahead of the status quo.

Core question:
How do we manage cognitive load while increasing our weekly hours on the real problem space (vs passive consumption)?
Peers average ~10 hrs/week; aim for ~30 hrs/week by restructuring your input modality dynamically.

Plan (don't fight your attention or schedule):
Some days you won’t want another AI report. Others, a documentary, podcast, or YouTube breakdown will land better. Sometimes the move is hands-on: build an AI tool in Cursor or spin up images in Midjourney. Switch the channel, not the mission. Change how info enters, is processed, and is expressed to match your energy and schedule so you can stay in the problem space longer and actually master it.

Input modalities: Video, audio, text, images/diagrams.
Adaptive processing: Turn documents into podcasts, podcasts into mind maps, YouTube explainers into structured docs and convert any of the above into a live quiz with Voice AI. Use custom tools in Cursor or products like Google’s NotebookLM, OpenAI’s ChatGPT, or Copilot’s Voice Mode.
Output modalities: Publish small artifacts, notes, posts, quick diagrams, or even a structured chat with a colleague - to lock learning and create feedback loops.

Caveat (expect the buck): The curve will throw you eventually; knowledge will be superseded before your priors fully update. Your job is to extend time-on-curve so when it happens, you’ve seen more, earlier and are better positioned to anticipate what’s next.

AI Capability map (modality alignment)

Use these broad categories to define intake/processing/output modes needed to reduce cognitive load and acquire durable advantage.

· Generate: Create new artifacts from existing content.
· Transform - Convert content between formats or data types.
· Distill - Summarise, Key quotes, Concepts etc to increase digestibility.
· Find - Pinpoint critical information hidden in complex data.
· Reason - Analyse, Draw conclusions, chart decisions.
· Do - Automate action, execute tasks, Initiate Attention Systems.
· Represent - recreate characteristics or attributes of the person in speech, video, image, text.

On Attention Systems

Plain definition: An Attention System continuously pulls the right signals closer, at the right fidelity, with the least friction - so you can stay tethered to the complexity curve longer.

An Attention System is a small pipeline with five parts:

Sources – frontier research, release notes, market news, internal docs, dashboards.
Collectors – scheduled searches/feeds (web search api's, RAG over internal wikis, cloud docs, email digests etc).
Processors – LLM transforms to Distill (summaries), Transform (format shifts), Find (targeted pull), Reason (quick takes/implications).
Memory – lightweight store of highlights, decisions, and “watch items” (tags + timestamps).
Surfaces – a single daily/weekly brief, plus live boards for “What changed?”, “Why it matters?”, “What do we do?”

Workflow example:
Say “Collate latest discussions about AI Security Research across all frontier labs such as Anthropic, Google, OpenAI, xAI”
The system runs queries across web API's, scrapes release databases, pulls internal deep research notebooks, distills to a 1-pager with links and a “so-what” section, and opens tasks tagged Next Bets. You skim, decide, move.

On Intelligence Engines

Plain definition: An Intelligence Engine is your multi-modal thinking workspace that supercharges critical thinking, ideation, aligned to core objectives. A “team brain” that builds noisy off-the-cuff thoughts into blue ocean strategy or detailed risk-adjusted next-steps.

The future of critical thinking is a team effort. As foundational AI models improve and humanity demands more from itself, knowledge workers will step up an abstraction layer - raising expectations of quality.

I’ve already built much of the following, albeit haphazardly, across emerging workflows. Unification into a single engine becomes feasible as frontier gaps close and the operating landscape stabilises.

(Crude historical analogy: just as Assembly → BASIC → C → Python → Rust/Go each pushed programming up an abstraction layer, Intelligence Engines push knowledge work up a layer. You still think - just with better leverage.)

An intelligence engine might be made of four layers:

Intake – voice/text/file drop (meetings, briefs, specs, data excerpts).
Retrieval & Contexting – pull internal knowledge, metrics, prior decisions; attach provenance.
Reasoning Models & Tool-use – orchestrated prompts/agents to Distill, Compare, Scenario plan, Critique, Translate modality (e.g., doc → diagram → mind-map).
Artifacts & Memory – outputs as decision memos, PRDs, diagrams, mind-maps, plus an indexed reasoning trail.

Humans still need to think in the Age of Intelligence. Companies may soon view talent as unique contributors to the Intelligence Engines they use - hiring not only for role fit but for the measurable lift a person brings to the organisation’s “team brain.” As Satya Nadella has suggested, employees may effectively be hired with their agents. In that world, experience across Intelligence Engines - and the ability to improve them -raises the value of prior work, and can broaden pathways for diverse talent whose strengths become legible in shared reasoning systems.

Workflow example:
Speak into the engine or prompt it and it ideates alongside you, sustaining longer flow states. It bounces ideas back, reframes problems through different critical-thinking tools, and translates your rough notes into structured options (with sources), raising the resolution of emerging ideas.

On Agent Chains

Plain definition: Agent Chains are outcome-oriented automations that coordinate tools and approvals - under explicit GRC guardrails - to complete real, risk-adjusted workflows, then hand results back to humans for report or review.

What:
Bring approvals and actions to the user’s fingertips. Surface only what requires attention. Execute the rest safely in the background.

Why:
Handle the work people must do (comms distribution, distillation, data in/out of databases) without swivel-chair effort.
Orchestrate end-to-end actions across other agent systems.
Produce clear outcomes that a human can approve, reject, or amend.

How:
Integrate systems so agents can coordinate tasks across the stack: Microsoft Graph, SharePoint, Xero, CRMs/ERPs, data warehouses, OpenAI Enterprise, Claude, Google AI Studio, Perplexity Labs, and more - each with least-privilege scopes and auditable trails.

Modern work adjustments:
Users receive or initiate actions that pass cleanly between human and agent teammates for further review.

Attention Systems feed fresher signals in; Intelligence Engines work the data into blue ocean strategy alongside you; Agent Chains carry approved actions out. Same tether - three layers of leverage.

On competitiveness: a brief note

In any strategic game, advantage boils down to decision speed, decision quality (impact on Win Conditions), and how those two variables constrain a competitor’s options. All of it sits under the clock: time governs speed, and it governs quality because real quality in ambiguous domains comes from iterations and reps that sharpen your read on the Win Conditions.

The tethering rules raise your odds of winning in any industry.
Remember: in private enterprise you compete with firms and with every individual seeking the same roles. No sector is insulated. Even trades touted as “future-proof” will absorb disruption as automation reshapes the job mix; displaced workers will retrain into whatever remains economically viable.

Apply the tethering rules to your field now. Build the advantage for your team and for those who depend on you.

On Accountability

How do you know you’re ahead of the curve and not falling off?
Anchor yourself to reality with a community of visionaries, shared artefacts, and discussion with outside voices, trusted technical opinions, macroeconomic signals.

Community checks: publish small artefacts, invite critique, compare notes with credible peers.
Aggregated expertise: track consensus and dissent across trusted evaluators; log what changed and why it matters.
Open trading of ideas: treat selective IP sharing as a hedge collaboration compounds faster than hoarding.
Accountability is the tether’s safety line: it keeps you honest when the curve gets steep.

On Meaning

"Each person is responsible for all people and for all things." - Brothers Karamazov by Dostoevsky.

"It may be possible for each to think too much of his own potential glory hereafter; it is hardly possible for him to think too often or too deeply about that of his neighbour. The load, or weight, or burden of my neighbour's glory should be laid daily on my back, a load so heavy that only humility can carry it, and the backs of the proud will be broken." - The weight of Glory by CS Lewis

If you do not take it upon yourself to build AI solutions for your fellow neighbour that encourage human flourishing who will? Even when incentives pull the other way, you should try. Progress is hard and markets often reward short-term self-interest; act anyway. The work is to align capability with consequence: design systems that make it a competitive advantage for companies that do the right thing, and harder to be anti-humanist, at scale.

"Fight hard to build the best future for all of us. We get to invent these first-conditions of our economy - living in the Age of Intelligence - just one fragile time. Ethical solutioning for executives, politicians and technical employees is critical, before third-degree path dependence and institutional lock-in causes a permanent underclass to be entrenched. The need for a big beautiful vision is paramount to our success. We can do better than this. Please fight for it." - Samson Blackburn

If sending a connection request, only those with messages that introduce your intentions in this space will be accepted: https://www.linkedin.com/in/samsonblackburn

On living through AI

Samson — Sun, 26 Oct 2025 05:49:21 +0000

The Age of Intelligence is changing a lot, for all of us.

I often receive feelings that challenge me to action philosophically - they come through the pit of my stomach, one might describe as the Holy Spirit. Before Jesus lived Socrates already had the name δαιμόνιον (theh-ee-MO-nee-on) for this feeling.

Often to meet the criteria of such feelings I need to frequently reground myself amidst the immense pace of change. While giving it my absolute all day-to-day, I must continue to hold space for balance between building for the future and living in today.

Acting on this feeling, I wrote the below Facebook post which I hope would help any young person in setting their north star for the future.
The quote from Albert Camus, means to me - the encapsulated burden, beauty and boldness required to be everything you can be, to everyone you can be it for.

Facebook: 26/10/25

You should live to the point of tears. - Albert Camus

On demands of the moment

At all times, the question you should ask yourself is;
if I was unable to change the course of today, how exactly would I live it to the fullest?

For those of us still tethered to our jobs, I hope you might say something like - I will smell the flowers I usually walk passed, I will smile the biggest smile and speak as tenderly as possible to my loved ones, I will set my eyes to the sky and marvel at the colour, the clouds, or the stars. I will dance to music and keep my attention present to the people I am sharing space with.

Perhaps you might go as far as pushing yourself to do something like; "Plant the seed, of a tree which others will get to sit in the shade."
These would all be noble goals and they are a great place to start envisioning your last day. The responsibility you are gifted after such an exercise is in pushing your ability to contextualise a day, into several days and eventually into weeks strung together. Your appreciation and the goals should grow accordingly.

The dream state here would be for you to contextualise what could be your entire life and then to extend in addition to the lives of your community, then your city, country and world.

I could not live up to such greatness as that.
But it's the most meaningful goal I could try for.

The Lesson of the Lamp
(Mark 4:21–25)
16 No one lights a lamp and covers it with a jar or puts it under a bed. Instead, he sets it on a stand, so those who enter can see the light. 17 For there is nothing hidden that will not be disclosed, and nothing concealed that will not be made known and brought to light.
18 Pay attention, therefore, to how you listen. Whoever has will be given more, but whoever does not have, even what he thinks he has will be taken away from him.”

To build personal agency in the Age of Intelligence, my bets are on the comprehensivist.

"
A Comprehensivist can:
Synthesize large amounts of information
Put a broad amount of knowledge into practice
What is a Comprehensivist?
Someone who’s great at several things, likely to make money in a way that’s inscrutable to the lay person.
This happens by combining skills, one by one, over a long period of time.
Keep in mind that: Most people are mediocre at most things, great at one thing, good enough at many things. Few become great at many things.
The lay person should choose a well-paid profession and specialize.
There is no arguing that the specialist doctor is better paid (and preferred) to the generalist doctor. But the generalist doctor is not a comprehensivist. The doctor who runs the entire clinic is a comprehensivist.

Ludvig Sunström: https://startgainingmomentum.com/comprehensivist/
"

Unlocking the comprehensivist inside you requires an incredible up-shift in personal agency, you need to be generating massive action to realise such a feat.

On Agency & Mindset

Actors are at the first level of awareness. They spend most of their time in their scripts, following beliefs that they have learned either consciously or sub-consciously.
Agents have been actors. They have since become aware of the scripts they’d previously been following and are now doing something about it.
Architects have been agents. They have created great personal change and, having ‘come through the minefield’, they have decided to do something about bringing other people with them.
They are no longer just creating change for themselves, they are designing change for others. They are teachers, inspiring actors to become agents and designing ways to do this more effectively. They are demonstrators rather than preachers, they are examples rather than precepts.
They operate at the level of creation, bringing more life to all through their demonstration of how to raise awareness. They operate in the ‘WE’ rather than the ‘ME’ and critically, architects show people how to do the same.

Tom Cassidy: https://medium.com/@tom.cassidy_40820/actors-agents-architects-where-do-you-see-yourself-f171ffdcc71f
"

From Animation to Cybersecurity & AI: How I Battled Self-Doubt, and 225 Job Applications to Find My Calling

Samson — Sun, 20 Oct 2024 07:49:58 +0000

samsonblackburn.tech

Linked In - Samson Blackburn

Back in 2017, I read a global report that hit me like a ton of bricks. It studied the likelihood AI was going to automate every job you could think of and fast. The only way to survive this automation wave? Score high on LSATs and become the ultimate critical thinker. The problem? I didn’t have a background in physics, maths, or law... the highest scoring fields, thankfully somewhere around fifth on the list scoring highest in critical thinking... was Philosophy!

That’s when I decided to pivot hard - not even sure if I'd make it into Tech longterm before AI kicked my arse. I switched my degree from animation to philosophy diving deep into formal logic and epistemology, I absolutely loved it. Philosophy became my way of sharpening my mind for the challenges ahead.

The Roadblocks and the Warehouse Gig

Fast Forward a couple of years ... While studying philosophy and theology, I started teaching myself the ropes of robotic-process automation and Python. I even took on a job as a business finance manager for a bit, but it wasn't my passion. Eventually, I enrolled in a fast-tracked IT diploma at a coding bootcamp in Brisbane. After graduating as a full-stack developer, I hit the job market—only to find that most opportunities were in sectors I didn't want to support, like gambling, marketing, or certain media conglomerates.

After sending out 225 job applications and facing nearly a year of rejections, ghost-applications or final stage interviews, I gratefully though sadly accepted a job as a warehouse picker-packer working with a friend. just two business days before I was supposed to start, as if divine intervention - I received a call from a recruiter. Aced the two interviews and landed my first IT job. Sure, it was minimum wage, but I was finally in the field I wanted.

Man, this role threw me a ton of curveballs! This role involved 24/7 upkeep of everything IT. The position was in Critical Healthcare. This means we look after everything from nursecall systems, DECT Phones, to technical uplift of every area of the business, including modern work and Robotic process automation. Being part of a tiny two-man team maintaining eight sites—it could be intense at times. Add in the fun of constantly getting sick from the site visits and you've got one wild ride. Although grateful that this role provided the chance to genuinely bring joy to elderly and palliative care residents, I was absolutely spent.

The Cybersecurity Leap

I didn’t land a cybersecurity job right away. I went to SecTalks after SecTalks, AISA Meetup and Networking event after event, until I was introduced to an absolutely lovely chap. They gave me a connection to the owner of a recruitment company called The Decipher Bureau. I looked up this recruiter and thought ...wow... that website sure looks cool Decipher Bureau. Generously, they agreed to meet me mentor and potentially assess suitability for a role as a cybersecurity recruiter, we set a date and he came to visit me at a cafe nearby where I was working. The owner listened to my hopes and dreams, with an honest heart he said "Hey buddy, look I think your heart is in the technical stuff, I'd recommend you get some serious skills by joining an MSP (Managed Services Provider) - so I made that my mission.
I joined an MSP and kicked my learning into overdrive. It was a transformative time. I realized through this experience that with relentless effort, I could achieve just about anything.

Pictured [Colleagues Just Horsin' Around - Colourized 2023]

MSP work became my crucible; developing a self-belief that money can't buy I finally understood why the wise og tech-heads would constantly say "You've got to show me projects Samson!". After about a year, a recruiter from Decipher Bureau contacted me, and that’s when things finally clicked - I landed a role as a cybersecurity analyst.
Joining under the wing of a cybersecurity visionary and finding myself surrounded by thoughtful, incredible cybersecurity leaders is a story for another time.

Now, I work on all kinds of things within the Security Engineering Team, from DevSecOps & Cutting-Edge Microsoft Implementations to conducting critical cybersecurity audits based on frameworks like the Essential Eight and AESCSF. In my downtime, I'm all about building applied use solutions of AI. I'm excited about the future for the first time in a long time and more motivated than ever to mature my career in cybersecurity.

For the Newbies: What I Wish I Knew

When a young woman reached out after a SecTalks meetup, her message took me back and frankly brought about some tears. She was dealing with the same uncertainty and overwhelm I felt all those years ago. Here's some of the laundry list of things I wish someone had told me back then admitting even if they did I probably wouldn't understand: Cybersecurity is tough to crack because employers expect you to know "the whole of IT." But they rarely spell out what that means for aspiring cybersecurity professionals.

Here's a bit of the advice I gave recently, and what I'd tell anyone trying to break into this field:

Employers in Cybersecurity Expect You to Know "The Whole of IT"
Yeah, it's a big ask, and they won't always lay it out clearly. For juniors, it's tough to read between the lines during interviews. Don't let that intimidate you. Focus on building a broad base of basic understanding across various areas—networking, systems, security principles etc.
Diarize Your Learning
Keep track of what you're learning as you go. Documenting your progress not only reinforces your knowledge but also provides tangible proof of your skills to show employers. Plus, it's satisfying to look back and see how far you've come.
Build Projects to Demonstrate Competency
Start small and gradually take on more complex projects:
The objective here isn't always to succeed, it is to learn and show your learning. Although you do want to build lots of micro examples of applied knowledge - that you can be proud of.

Choose an area from here https://chatgpt.com/share/674ac02d-6544-8009-9fab-78ba04242e0e and dive into understanding, it's going to be really hard without hands-on-experience to map the context but use ChatGPT O1-Preview to help expand your mental schema, know at least within an Australian environment Microsoft has marketshare and focus on knowing their products to get employed.

Develop a Philosophy of Learning I'm constantly improving the way I approach learning. Over the years, I've developed a learning philosophy rooted in cognitive science and philosophy. It's about understanding how our brains absorb information and using that to our advantage. Map what you're learning back to other things you already know and strengthen the memory.

Key Takeaways

If there's one thing I've learned from my journey, it's that relentless pursuit is key. You can achieve anything if you're willing to learn, adapt, and keep pushing forward.

And if you feel like it's taking too long, trust me ... I’ve been there. Just remember: keep learning, keep building, and don't let this stranger from Australia believe in you - more than you believe in yourself!

That's my story, and I hope it helps anyone out there trying to find their footing in cybersecurity. Whether you're just starting out, or someone feeling stuck, I hope this saves you time and frustration. Stay motivated, stay curious, and most of all, believe that you've got what it takes.

Feel free to reach out or leave a comment if you have any goals re; cybersecurity, AI, or even philosophy. Happy to contribute to meaningful outcomes.

samsonblackburn.tech

Linked In - Samson Blackburn