Code generation is easy. Building a production-grade AI system is not.
The latest Technology Bytes shares a real-world experience from building an AI Life Coach — what breaks when LLMs move from demos to production, why system thinking matters more than prompts, and where engineering judgment and responsibility show up in human-facing AI.
If you’re working with LLMs beyond experimentation, this may resonate.
If AI is making feeds “smarter,” why do YouTube and LinkedIn feel more repetitive than ever?
The real question is simpler: Are you actually learning? Or just consuming?
The issue isn’t volume. It’s value.
There’s more content than ever, yet fewer moments of genuine insight. Scroll for a while and the patterns repeat:
What’s being optimized isn’t learning. It’s attention.
The first wave of AI-powered feeds optimized for metrics: Clicks, watch time, replays.
But engagement is not the same as understanding.
Algorithms crave momentum, not nuance. The result? Feeds that feel busy—but don’t move you forward.
Time gets consumed. Perspective stays static.
That’s why the next shift won’t come from generating more content. It will come from value-led curation.
People (or purpose-built agents) who filter for:
The platforms that matter won’t just capture time.
They’ll justify it.
AI can distribute content.
AI can scale reach.
But wisdom requires judgment.
And judgment remains human.
What AI was in 2025 — and what it becomes in 2026.
2025 was the year many assumptions around AI met reality.
Not in labs or demos, but inside real systems—codebases, workflows, agents, and production environments.
This first Technology Bytes edition of 2026 reflects on that shift:
The full blog looks back briefly at 2025, then focuses on what genuinely changes in 2026 for teams building and running AI systems end to end.
📖 The full piece is published as the latest Technology Bytes edition.
Thank you to everyone who continues to read and share Technology Bytes.
Wishing you a thoughtful and focused start to 2026.
AI doesn’t take control loudly.
It takes control quietly—one micro-choice, one nudge, one unseen influence at a time.
Most people think AI is a technical problem.
It isn’t. It’s a human story we still don’t fully understand.
We can keep building bigger models, faster transformers, smarter agents—but none of it matters if we don’t see how they reshape identity, intention, trust, fear, ambition, and the quiet ways technology rewrites us while we’re not looking.
AI doesn’t change the world through code.
It changes the world through narrative—through the stories we accept without question.
And stories are how we uncover the things we can’t measure:
the subtle shifts in purpose, the invisible pressures, the ethical awakening, the psychology behind “intelligent” systems.
To celebrate the holidays and spark deeper reflection, my entire four-book AI Novel series is FREE worldwide for the next 5 days.
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Beyond the Software Code | The Perspective on Transformation
Will the AI “CodeMaster” replace you—or force you to rediscover your purpose?
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Echoes of Tomorrow | The Perspective on Responsibility
A 2045 world forced into an ethical reckoning. (Golden Book Award Winner 🏆)
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Deceptive Success: The Master Mind | The Perspective on Psychology
When AI understands your mind better than you do—is it empathy or manipulation?
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Download them, read them over the holidays, and if any of the stories resonate, a short Amazon review helps them reach more readers.
Happy holidays, and wishing you a reflective and meaningful start to the new year.
Our last Technology Bytes newsletter of the year looks at how AI met reality in 2025—and what engineering will demand in 2026.
From AI excitement to engineering reality, this edition shares six clear predictions informed by what became visible in 2025—behaviour, failure modes, trust, cost, and the role of people as the real asset.
Thank you for reading Technology Bytes this year. Wishing you a calm close to 2025 and a focused start to 2026.
Happy to share the SCI for AI specification from our Green Software Foundation
As AI systems moved beyond traditional software patterns with model training, inference, and increasingly agentic behaviours, it became clear that existing software carbon metrics needed careful extension to remain meaningful. Measuring AI emissions is not just about more compute; it is about different lifecycles, boundaries, and usage patterns.
Within the Green Software Foundation’s standards work, SCI for AI emerged as a focused workstream to address these gaps by extending the ISO Software Carbon Intensity (SCI) framework for AI specific scenarios. This work has been shaped through focused workshops with 20+ member organisations early this year, followed by sustained weekly working sessions that helped turn ideas into a concrete, implementation ready specification.
The outcome reflects the strength of an open and inclusive community, where collective intelligence and diverse perspectives turn complex problems into practical, implementable standards. Sincere thanks to all the members who participated and shaped this specification, and to the member organisations whose contributions made this work possible.
As a next step, the community is inviting case studies for SCI for AI implementation. If you have existing AI systems or use cases and want to measure the carbon impact of your AI workloads and identify reduction opportunities, do reach out. Real world implementations will help validate and strengthen the specification.
Check out the details and the full specification at
👉 https://lnkd.in/gkvZeMm5
Grateful to be leading this effort alongside such a committed community.
Standardization is what turns an emerging idea into a usable system.
Agentic AI is moving fast. Agents can reason, plan, call tools, and coordinate with other agents. But beneath that progress sits a structural issue: each agent framework defines its own way of connecting to tools, exchanging context, and collaborating with other agents. Agents work well in isolation, but fragility appears at scale.
This is not new.
Before containerization, software was tightly bound to machines and environments. Docker standardized how applications are packaged. Kubernetes standardized how they are orchestrated. Together, they solved a systemic problem: build once, deploy anywhere.
Agentic AI is at a similar stage.
Today, an agent is often inseparable from the framework it runs on, how tools are integrated, and assumptions about context and execution. Without shared contracts, portability breaks. Multi-agent systems become tightly coupled. Tool providers repeat the same integrations across ecosystems.
Standardization addresses this at three levels:
It is good to see the formation of the Agentic AI Foundation under the Linux Foundation, where leading players are coming together to address interoperability rather than solving it in silos.
From experience with the Green Software Foundation, one lesson stands out: collective intelligence and open collaboration consistently lead to more durable outcomes.
Standardization does not decide what agents do. That remains your business logic. It defines how they connect, communicate, and scale.
For more details, visit @ https://aaif.io/
LLM models are improving fast — but every upgrade comes with a cost most organisations still aren’t prepared for.
This week’s edition of Technology Bytes breaks down the hidden technical debt behind AI systems, and why model upgrades are now one of the biggest threats to production stability.
If your workflows, copilots, or agentic systems depend on predictable behaviour, this is a must-read.
New Edition: The Hidden Technical Debt of AI: Why Model Upgrades Will Break Production
👉 Read the full analysis here. (https://lnkd.in/dVARsdtk)
And thank you to everyone who continues to support Technology Bytes. If you haven’t subscribed yet, now is a good time — each edition explores the fundamentals shaping the future of intelligent systems.
A large amount of AI-generated code could very well become the legacy code of the near future.
Not because it was “bad,
but because it was never designed with holistic context, reasoning, constraints, or long-term evolution in mind.
Legacy has never been about age.
It is about clarity, intent, and the ability to evolve a system confidently.
The moment no one understands why something works — or what it might break — it becomes legacy, regardless of who wrote it.
2026 might still become the year of vibe narratives and tools — rapid generation, fast demos, impressive-looking automation, and louder claims about code being “solved.
Useful progress, but also a phase where speed starts overshadowing system thinking.
And the unvibe reality may arrive much sooner than 2028.
It could be 2027.
It could even be 2026.
Because the moment generated code meets real systems, integration gaps, rewrites, and long-term maintainability challenges show up quickly.
Because generating code is not the hard part.
Designing systems that scale, behave predictably, integrate safely, and remain maintainable is where the real work lives.
Copilots accelerate what they already know.
Ask for a new flow, a new API pattern, a different architectural approach, or a genuinely better design — and the limits appear.
That’s not a failure.
That’s simply where engineering begins.
And here comes a twist everyone forgets:
what happens when the training data itself had vulnerabilities?
If an AI model learned from open-source code containing flawed patterns or unpatched issues, those vulnerabilities don’t just repeat — they replicate at scale.
Suddenly, the concern isn’t just “AI-generated legacy code,
it’s AI-propagated vulnerabilities quietly spreading across systems.
Meanwhile, organisations keep chasing higher leaderboard scores — replacing one AI model with the next slightly-better version of their own previous model.
Impressive on paper, yes —
but without grounding in real environments, the complexity and risk introduced by each “upgrade” eventually land somewhere, often far earlier than expected
And even if everything became deterministic tomorrow,
which industry, regulator, or compliance body would accept systems without explainability, traceability, or operational guarantees?
2026 may bring the vibe.
2028 will bring the understanding — that AI can generate code, but only engineering prevents it from becoming legacy.
Hopefully much sooner than 2028
Happy to share the launch of my experimental app — AI Life Coach.
The idea began a few years ago while exploring how personalised guidance could become more meaningful, contextual, and accessible in everyday life. That exploration first took shape in the book “The AI Life Coach: A Journey of Human Transformation with Generative AI” (Aug 2023), which looked at how technology can support clarity and decision-making without replacing personal judgment.
The same idea has now taken a new form as an app — a simple, everyday experience built around:
🌟 Ask Anything — real-time, personalised insights
🌟 Life Blueprint — a unique model for you, combining your behavioural patterns, contextual understanding, and ancient wisdom traditions.
🌟 Eight Focus Areas — Career, Health, Relationships, Finance, Creativity, Family, Spirituality, Personal Growth
🌟 Evolving Insights — reflections that adapt as your choices and context change
🌟 Social Analyzer — a way to understand your true self vs. your projected self
The purpose remains the same:
Not to claim certainty or offer predictions, but to offer perspective — something to reflect on, something that may reveal patterns or bring a bit more clarity when needed.
Building the app also became an insightful validation exercise. AI coding copilots clearly help generate code along known, predictable paths, but they struggle the moment deeper reasoning or system-level decisions are required. That gap reinforces why thoughtful architecture, clarity of design, and system-level thinking matter far more than auto-generated code, and why engineering rigour is essential for anything aiming to be truly production-grade. More on this later.
🔗 Explore AI Life Coach: https://ailifecoach.in/
📱 Available on Google Play Store and Apple App Store
📘 Original book (Aug 2023): https://lnkd.in/dngcs7SN
Do try the app — would genuinely value your perspectives and feedback as the experience continues to evolve.
From Code Generation to System Integration: Why AI Coding Tools and Agentic IDEs Must Evolve to Solve Real Software Development Challenges
Since GPT-3 went mainstream, AI coding tools have sprinted through three waves.
Every cycle starts the same way:
Wow. Impressive.
Look at how much this can do for me.
But the uncomfortable truth is this: most of what these tools automate is commodity knowledge.
Framework boilerplate, CRUD patterns, standard integration glue, typical test shapes – once a pattern exists in public code, a model can learn it and repeat it very well. That used to feel like expertise. Now it’s autocomplete on steroids.
The real problems have barely moved:
This is where vibe-coding tools become dangerous.
The model writes the feature, generates the tests, explains the diff. Everything looks green. It feels safe enough to ship on vibe.
Without deep expertise and a solid workflow around it, that is not productivity. It is an efficient way to inject new risk into a live system.
If code patterns are now cheap, differentiation shifts somewhere else:
Writing code is becoming a commodity.
However, writing holistic, thoughtful systems, and continuously evolving and governing them safely, is where the true value lies
AI coding copilots and agentic IDEs now need to evolve from “look what I can generate” to “look how I help you integrate, operate and transform”.
That’s when it stops being “wow, impressive demo
and becomes “yes – this is finally solving the real problem.
Software is being built faster than ever, thanks to AI.
But speed only matters when the architecture underneath can survive it.
This week’s edition breaks down the Ten Principles of Modern Architecture Design — a practical framework for building systems that balance speed with discipline, automation with intent, and intelligence with responsibility.
AI speeds up creation.
Architecture shapes what that creation becomes.
The AI narrative bubble has finally burst — not because the technology is slowing down, but because the story around it became bigger than the systems, architectures, and processes that actually support it.
When narrative disconnects from engineering reality, correction becomes inevitable — and that moment has arrived.
🌐 Technology Bytes — Latest Edition Is Live
In this week’s edition, I unpack why this shift was unavoidable, where the pressure has been building for months, and how the industry is now moving from hype to hard truth.
We explore the widening build–run gap, the unsustainable cost of intelligence, the fragility of trust, and the fundamentals that every organization must return to if they want AI to deliver value at scale.
The AI narrative bubble has burst.
The real journey starts now — and we have a long way to go.
Is the Current AI Wave a Bubble Waiting to Burst?
Yes — but not because the technology is weak.
The bubble sits in the story we are telling about AI, not in the capability itself.
Right now, four pressures are building at the same time:
Inflated Expectations
AI is being spoken about as if it can “replace thinking.” That’s not how real work functions. The distance between what AI produces and what organizations can reliably deploy is still large. When expectation runs ahead of operational reality, correction becomes unavoidable.
The Build vs. Run Gap
Building with LLMs is one aspect. Operationalizing them is another.
Generating ideas, designs, code, or workflows is the easy part. Making that output work across systems, data pipelines, security controls, compliance processes, latency budgets, user experience, and long-term maintainability is where the real challenge lies.
Without operational grounding, AI remains a demo — impressive, but not durable. The narrative breaks the moment teams try to move from prototype to product. And blaming AI for layoffs is misdirection — AI doesn’t create the gaps; it exposes the ones already there.
Unsustainable Cost of Intelligence
Running large models at scale is expensive — financially and environmentally.
And yet, many organizations are chasing Leaderboard success rather than delivering actual value.
The competition has quietly shifted into “who is using the most advanced model” instead of “who is solving a real problem well.
If efficiency, right-sizing, and design discipline do not catch up, cost will slow adoption faster than capability can accelerate it. The fight increasingly looks like one expensive model trying to outperform another expensive model — while value, outcomes, cost and sustainability stay on the sidelines.
Organizations that continue scaling without purpose will feel the correction first
Fragile Trust
Trust is not just about accuracy. It’s reliability, attribution, data boundaries, and accountability. If outputs cannot be traced, verified, or governed, systems will not scale — no matter how impressive the demo.
So yes — there is a bubble — but it is a narrative bubble, not a technological one.
What will burst is the belief that AI replaces thinking.
What will remain is the understanding that AI generates possibilities — humans still choose, commit, and deliver.
This is not collapse.
It is correction — from excitement to clarity, from promise to proof, from output to outcome.
The bubble bursts.
The work continues.
The recent issue between Perplexity and Amazon is not just about one company against another. It raises a much bigger question: who controls your digital actions when AI starts doing things for you?
Perplexity believes that AI assistants, like its Comet agent, should be allowed to act on your behalf, finding and buying items online just as you would. Their point is simple: if it is your account and your data, you should decide which tools can help you use it.
Amazon’s concern comes from another side. When AI starts automating user actions, it changes how platforms operate — how they manage security, ensure fair use, and maintain customer loyalty and trust. That is not an easy change for any large company to handle, especially one that has spent years building an ecosystem around reliability, user experience, and consistent service.
Both sides have a point. Users should have the freedom to choose their digital assistants, but platforms also need to protect their systems and ensure fair use.
The real challenge is to build trust, where AI acts for users without breaking platform rules, and companies respect innovation without shutting it down through fear or control.
We have seen this before. When new technologies started automating user actions on the web, platforms worried about privacy and misuse. The result was not a shutdown but a standard called OAuth.
OAuth was created to solve a trust problem. It allowed users to give third-party applications limited access to their data without sharing full credentials. That model protected platforms and empowered developers, turning potential conflict into collaboration.
The rise of AI assistants needs a similar evolution. Just as OAuth defined a safe way to authorize access, we now need a secure and transparent way to authorize actions, where AI agents can act on a user’s behalf with verified consent and clear boundaries.
We have a long way to go before that balance is reached. Until then, it is important to respect what platforms have built: their infrastructure, governance, and trust models developed over years. Innovation should challenge limits, not disregard them. Real progress happens when both sides, platforms and pioneers, evolve together. Perplexity Amazon
💡 Blaming AI for Layoffs Misses the Bigger Problem
AI influences layoffs — but it doesn’t define them.
AI didn’t take those jobs. Inefficiency did.
It’s easy to point fingers at technology — to say AI replaced people.
But the truth is far more complex: most layoffs start long before any model is deployed.
They begin when strategies are built on hype, not adaptability.
When billions are spent chasing bigger models instead of building better systems.
When engineering discipline gives way to “vibe coding.
The real issue?
Our Software Development Lifecycle is broken.
We’ve plugged AI into isolated stages — design, code, test, deploy — expecting transformation.
But intelligence doesn’t work in fragments.
It needs deep, seamless integration — every decision traceable from design to production.
AI should be invisible yet indispensable — quietly powering design, development, testing, and deployment behind the scenes.
That’s not where we are today.
Right now, copilots can autocomplete, not architect.
They can suggest, not sustain.
And they fail the moment something truly new appears — because AI doesn’t learn context, it repeats consensus.
Forget AGI for now.
Let’s first make the basics work — build systems where AI enhances human creativity and accountability, not replaces it.
Because progress isn’t about fewer engineers.
It’s about better engineering — the kind that builds, connects, and sustains the intelligence everyone’s racing toward.
🎧 Tune in to my latest podcast episode of Agentic AI: The Future of Intelligent Systems —
“Blaming AI for Layoffs Misses the Bigger Problem” — https://lnkd.in/dTdDYeYJ
where I unpack why AI may influence layoffs, but the real story is about broken strategy, disconnected systems, and the urgent need to re-engineer how we build intelligent systems.
The Three Levels of Vibe Coding
Vibe coding” is the new frontier of AI-assisted development — where you describe what you want, and the model writes the code.
It feels effortless. Instant. Almost creative.
But like all shortcuts, it has layers — and limits.
Level 1: Idea-Level Vibe Coding
Perfect for non-engineers, designers, and students.
You can translate ideas into code-like outputs — websites, scripts, or visuals — without deep technical knowledge. It’s exploration, not execution. Great for learning, concept demos, and creativity.
Level 2: Prototype-Level Vibe Coding
Used by early builders, startups, and innovators.
AI helps rapidly create proof-of-concepts or MVPs. But when the prototype meets production — testing, scaling, security — things break.
Because vibe code rarely accounts for the unseen layers of real-world systems.
Level 3: System-Level Vibe Coding
Where professional engineers work with AI copilots to accelerate repetitive tasks while keeping control over architecture, dependencies, and performance.
AI becomes a partner, not a replacement.
The value lies in combining human judgment with machine speed.
The illusion of vibe coding is that anyone can build production software overnight.
The reality? AI can create code, but not systems.
The truth is that real progress still depends on engineering discipline.
AI can vibe with your idea — but it takes engineers to make it live.
🌌 When AI Meets the Cosmos
Before algorithms learned to predict, humans learned to perceive — connecting stars into stories and meaning into motion.
This week’s blog explores how modern AI meets our oldest system of intelligence — the cosmos — to decode symbolic and emotional patterns in real time.
It’s not about prediction, but perception.
Not about automation, but awareness.
Built using Lean Agentic AI principles, this live experiment tests whether today’s AI agents can collaborate end-to-end to generate meaningful, contextual, and instantaneous insights from cosmic data.
Engineering remains at the core.
AI doesn’t replace it — it amplifies it.
🪐 Read the full story: “When AI Meets the Cosmos: Special Agents of Meaning.
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The Multi-Billion Dollar Copilot Illusion - Why LLM Coding Copilots Are Failing to Deliver Real Value.
Billions spent. Benchmarks won. Real systems broken.
This week’s newsletter dives deep into the growing gap between marketing narrative and engineering reality surrounding LLM coding copilots.
Despite billion-dollar investments, copilots continue to struggle with architectural context, system-level reasoning, and non-functional requirements — issues that become visible only when prototypes meet production.
Our latest article explores real-world insights from end-to-end implementations and new sections like The Large Context Trap — showing how “bigger context windows” don’t translate into true understanding — and The Benchmark Mirage, where leaderboard success masks operational inefficiencies.
It’s time for a complete rethink of how copilots are integrated into the software engineering lifecycle — from architecture to deployment — with context, accountability, and sustainability at the core.
Stay tuned — the next wave of transformation won’t replace engineers.
It will demand more AI engineering skills than ever before.