When you look at where AI is headed, it is hard not to notice a familiar pattern. It looks a lot like cloud computing in its early and mid-stages. A few players dominate the market, racing to abstract complexity, while enterprises struggle to comprehend it all. The similarities are not superficial. The architecture, ecosystem dynamics, and even the blind spots we are beginning to see mirror the path we walked with cloud.
Just like cloud computing eventually became a utility, general-purpose AI will too.
From First-mover Advantage to Oligopoly
OpenAI had a distinct advantage, not only in terms of model performance but also in terms of brand affinity; even my non-technical mother was familiar with ChatGPT. That advantage, though, is shrinking, as we witnessed during the ChatGPT 5 launch. We now see the rise of other foundation model providers: Anthropic, Google Gemini, Meta’s Llama, Mistral, Midjourney, Cohere, Grok, and the fine-tuning layer from players like Perplexity.This is the same trajectory that cloud followed: a few hyperscalers emerged (AWS, Azure, and GCP), and while niche providers still exist, compute became a utility over time.
Enter Domain-Specific, Hyper-Specialized Models
This abstraction will not be the end. It will be the beginning of a new class of value creation: domain-specific models. These models will be smaller, faster, and easier to interpret. Think of LLMs trained on manufacturing data, healthcare diagnostics, supply chain heuristics, or even risk-scoring for cybersecurity.
These models won’t need 175B parameters or $100 million training budgets: they will be laser-focused and context-aware and deployable with privacy and compliance in mind. Most importantly, they will produce tailored outcomes that align tightly with organizational goals.
The outcome is similar to containerized microservices: small, purpose-built components operating near the edge, orchestrated intelligently, and monitored comprehensively. It is a back-to-the-future moment.
All the lessons from Distributed Computing …. Again
Remember the CAP theorem? Service meshes? Sidecars? The elegance of Kubernetes versus the chaos of homegrown container orchestration? Those learnings are not just relevant; they are essential again.
In our race to AI products, we forgot a key principle: AI systems are distributed systems.
Orchestration, communication, and coordination: these core tenets of distributed computing will define the next wave of AI infrastructure. Agent-to-agent communication, memory systems, vector stores, and real-time feedback loops need the same rigor we once applied to pub/sub models, API gateways, and distributed consensus.
Even non-functional requirements like security, latency, availability, and throughput have not disappeared. They’ve just been rebranded. Latency in LLMs is much a performance metric as disk IOPS in a storage array. Prompt injection is the new SQL injection. Trust boundaries, zero-trust networks, and data provenance are the new compliance battlegrounds.
Why This Matters
Many of us, in our excitement to create generative experiences, often overlook the fact that AI didn’t emerge overnight. It was enabled by cloud computing: GPUs, abundant storage, and scalable compute. Cloud computing itself is built on decades of distributed systems theory. AI will need to relearn those lessons fast.
The next generation of AI-native products won’t just be prompt-driven interfaces. They will be multi-agent architectures , orchestrated workflows, self-healing pipelines, and secure data provenance.
To build them, we will need to remember everything we learned from the cloud and not treat AI as magic but as the next logical abstraction layer.
Final thought
AI isn’t breaking computing rules; it’s reminding us why we made them. If you were there when cloud transformed the enterprise, welcome back. We’re just getting started.
When Grok-4 and ChatGPT launched, headlines praised their high scores on benchmarks like Massive Multitask Language Understanding (MMLU), better pass rates on HumanEval, and improved reasoning on GSM8k. Impressive? Yes! However, as a product leader, I worry we are focusing on the wrong things.
Benchmarks are similar to academic entrance exams; they assess readiness but not real-world results. Customers, teams, and industries operate in the complex reality of delivering software, treating patients, securing systems, or managing supply chains. Focusing only on benchmarks may lead to models that perform well in tests but struggle in real-life situations.
Overfitting to the Test
The danger here is overfitting. Models are trained to optimize benchmark scores, yet they perform poorly on actual outcomes. We have seen it in other industries: students who test well but cannot apply knowledge, or autonomous systems that perform perfectly in simulation but fail in the field.
AI is at risk of repeating the same mistake if we confuse benchmark leadership with product leadership.
The Case for Human-in-the-Loop
Human oversight is not an optional safety net. It is the core of effective AI deployment. Whether it is a software engineer reviewing AI-generated code, a security analyst validating an alert, or a doctor confirming a recommendation, humans provide context, judgment, and accountability that machines can’t.
My blog last week about Toyota and automation offers a useful analogy. In its factories, even the robots can pull the andon cord. The andon cord is a mechanism to stop the assembly line if something seems off. The point of the matter is not to distrust automation; it is about embedding responsibility and oversight into the system itself. AI needs its own version of the andon cord.
From Monoliths to Meshes
Patterns that we thought we solved with distributed computing seem to be new again. The industry has chased monolithic, general-purpose models: bigger, denser, and more universal. But in practice, most enterprises need something different:
Small, specialized models tuned for their domain context (finance, healthcare, manufacturing)
These models collaborate, distribute tasks, and pool their strengths in mesh architectures.
The retrieval and orchestration layers provide grounding, context, and control.
The mesh model is both more sustainable and more aligned with enterprise outcomes. It reduces compute costs, improves transparency, and accelerates adaptation to new regulations or customer needs.
The Real Benchmark: Outcomes
As product leaders, our job isn’t to chase leaderboard scores; it is to deliver outcomes that matter.
Did the security breach get prevented?
Did the patient get safer diagnosis?
Did the software deploy without incident?
The future of AI will belong not to the biggest models, but to the smartest systems:
Those designed around human oversight
Specialized collaboration,
Outcome-driven measurement
Benchmarks are transient. Trust, reliability, and impact will endure!
There is an old Latin phrase: Quis custodiet ipsos custodes? Simply put: Who watches the watchmen?
It was a question of power and oversight. If those entrusted with guarding society become corrupt, who ensures they are accountable? In today’s world, that same question applies not to presidents and law enforcement but to algorithms, automation, and artificial intelligence, especially in the case of agentic AI.
The Rise of the Automated Watchers
Modern systems are too vast and complex for humans to monitor alone. These complexities range from
Microservices sprawl across Kubernetes clusters, spawning thousands of interactions per second.
Observability tools like Datadog, New Relic, and OpenTelemetry stream terabytes of logs, traces, and metrics to surface anomalies.
AI guardrails in platforms like LangChain, GuardrailsAI, and Azure’s Responsible AI toolkits catch unsafe or biased model outputs before they get to customers.
These systems watch everything: performance, security, compliance, and fairness. They are our first line of defense against outages, breaches, and reputational risk.
This idea came to me when I was writing a program for my robot using ROS2: What happens when the watcher itself fails, drifts, or is compromised?
The Accountability Gap
We assume watchers are infallible, but history says otherwise:
A metrics pipeline silently dropped alerts during a network partition, and no one noticed until the customer SLA was breached.
An intrusion detection system was itself bypassed in a supply chain attack, leaving a false sense of security
An AI safety layer failed to catch adversarial prompts, exposing users to harmful outputs or expose a company’s sensitive data
In each case, the system built to guarantee trust became the single point of failure.The absence of alerts was misread as the absence of problems.
This is the accountability gap:Who verifies the automated verifier?
Lessons from Toyota: Jidoka and the Andon Cord
Early in my career, I had the privilege of working with Toyota as a customer, and my counterpart shared a history lesson with me. The auto industry wrestled with this decades ago. Toyota, the pioneer of lean manufacturing, introduced robots to improve efficiency. But they quickly discovered a hard truth: robots can make the same mistake perfectly, at scale.
Every incorrect weld resulted from a robotic arm’s miscalibration. If a sensor failed, the defect affected thousands of cars. Automation didn’t correct errors; if it didn’t, it made them worse.
Toyota’s solution was jidoka: “automation with a human touch.” Rather than relying solely on machines, they included human oversight in the process:
The Andon Cord: Any worker could pull a literal cord to stop the entire assembly line if a defect was spotted.
Layered Verification: Human inspectors and visual systems checked robotic output continuously.
Kaizen (Continuous Improvement): Every failure was treated as a learning loop, improving both robots and oversight systems.
The lesson is timeless: automation increases both efficiency and risk. A single defect in a manual process is localized; a defect in an automated process is systemic.
The software world is no different. Observability dashboards are our Andon cords. SREs are our jidoka. And post-incident reviews are our kaizen.
Strategies for Watching the Watcher
Just as Toyota built layered accountability into its manufacturing system, we need to design resilience into our agentic AI systems. Four key strategies stand out:
Meta-Monitoring for Microservices
Observability tools should watch each other, not just the services.
Example: Prometheus scrapes are validated by synthetic transactions running through the service mesh, the digital equivalent of a second inspector checking a robot’s welds.
Audits for Observability
Periodic “reality checks” involve comparing raw logs and traces against dashboards.
Independent tools like Honeycomb validating a Datadog pipeline are today’s equivalent of a Toyota team double-checking machine outputs.
Guardrails for Guardrails in AI
Safety layers need redundancy: pre-training filters, real-time classifiers, and post-response moderation.
Think of this as multiple Andon cords for LLMs such as OpenAI’s Evals, Anthropic’s Constitutional AI, and Microsoft’s Responsible AI dashboards, which can all act as independent cords waiting to be pulled.
Human-in-the-Loop Escalation (Digital Jidoka)
Automation can reduce noise, but critical thresholds must escalate to humans.
Just as Toyota trusted line workers to stop the factory floor, we need to empower SREs, red teams, and ethics boards as the final circuit breaker.
Why It Matters
My experience with Toyota taught me, and Toyota taught the world, that automation doesn’t eliminate human judgment; it amplifies the need for it. The philosophy of jidoka, the practice of pulling the Andon cord, and the discipline of kaizen created not just efficient factories, but resilient ones.
Agentic AI needs the same mindset:
Jidoka: Design automation with human judgment built in.
Andon Cord: Give humans the power to halt systems when trust is in doubt.
Kaizen: Treat every monitoring failure as a learning loop, not a one-time solution.
Juvenal’s warning still holds: unchecked power, whether in presidents, robots, or algorithms, breeds complacency.
👉 The real question for software leaders is this: will we embed jidoka for Agentic AI systems, or will we continue to trust the watchers blindly until they fail at scale?
The future of resilient software, trustworthy AI, and reliable observability depends on whether we pull the cord in time.
As artificial intelligence and automation evolve, we are entering a multi-agentic world. Multi-agentic implies a distributed environment where autonomous software agents, APIs, machine learning models, and human users act in concert. Identity is no longer a technical detail; it is a core requirement for system integrity, trust, and control.
Agents today write code, deploy infrastructure, triage support tickets, summarize meetings, and in some cases make decisions. How do you know who made the decision and what liability your organization faces when agents collaborate, orchestrate, and reason?
You can’t coordinate a system that you cannot trust, and trust starts with a core capability: identity!
Identity is not for users only
Single sign-on, multi-factor authentication, and directory sync are basic requirements for user identity. With hundreds or even thousands of non-human agents like retrieval bots, security assessments, AI code reviewers, and autonomous workflows active at any given time, these measures are essential.
In large organizations, understanding the workflow, accountability, and permissible actions is crucial. Identity plays a fundamental role in these situations.
The issue affects everyone. It represents a change in control systems. Identity isn’t just about who logs in; it’s about making secure, transparent decisions and taking actions on a large scale.
Why is identity core to multi-agentic systems?
Identity is crucial to multi-agent systems. Here are six important reasons why.
🔐 Trust and Authentication
In a decentralized agent ecosystem, it’s important for us to understand who or what we are interacting with.
Is this code review coming from an approved AI agent or a spoofed script? Is it a rogue bot or an authorized user that initiates the workflow?
We can’t rely on IP addresses or client secrets anymore. We need signed, verifiable agent identities that persist across time and context.
🧾 Auditability and Accountability
Without identity, there’s no provenance.
When agents approve purchases, modify infrastructure, or triage incidents, it is essential to maintain a complete and tamper-proof record of the actions taken:
Who acted
On whose behalf
Under what authorization
With what outcome
This isn’t just good practice. It is essential for security, compliance, and debugging in enterprise systems.
Remember the old Abbott and Costello routine on “Who’s on first?” Who knew that comedy routine was so prescient!
👥 3. Delegation and Agent Chaining
In human teams, we delegate work to others. In multi-agent systems, delegation becomes the norm.
For example:
A user asks their assistant to generate a report
The assistant calls a forecasting agent
The forecasting agent queries a data governance agent to ensure compliance
At every step, the original identity and permissions need to be preserved. We must know who initiated the action and whether each agent is authorized to act on their behalf.
🔐 4. Fine-Grained Authorization
Multi-agent systems don’t work if every agent has God mode.
Each agent needs just enough access to do its job and no more.
This means:
Identity-linked roles and scopes
Time-boxed or task-limited permissions
Attribute-based access policies that adjust dynamically
Without strong identity-linked authorization, we’re just building smarter ways to breach ourselves.
🧠 5. Personalization and Adaptation
Good agents don’t just act; they learn.
But learning requires context:
What team is this user on?
What systems do they interact with?
What are their preferences?
Identity is the gateway to this context. It allows agents to personalize their behavior, become more useful over time, and avoid making dumb, default assumptions.
🌐 6. Interoperability Across Ecosystems
As agents start to collaborate across platforms, e.g., your Jira assistant talking to a GitHub bot or a Salesforce AI; the need for interoperable identity becomes critical.
That’s where standards like OIDC, SCIM, and even emerging ideas like Decentralized Identity (DID) and Verifiable Credentials (VCs) come into play.
Imagine an agent from Microsoft Graph collaborating with one from Atlassian Forge is only possible if identity flows freely but securely between them.
What Happens When You Don’t Prioritize Identity?
Without a strong identity layer, you get:
Shadow agents making untraceable decisions
Permission creep where every agent can do everything
Cross-system silos that break orchestration
Unverifiable outputs from unknown actors
It’s a recipe for disaster in regulated, security-sensitive, or high-trust environments.
Identity Is the New Control Plane
In human organizations, identity governs org charts, responsibilities, and roles.
In a multi-agent system, identity governs logic, execution, and autonomy.
Observability Tied to Identity – Logs, metrics, and decision trees with clear attribution
Policy-as-Code for Access – Role and attribute-based access enforcement
Cross-Domain Trust – Federated or decentralized identity for external agent collaboration
Closing Thoughts
We’re moving toward a world where autonomous agents are as common as microservices—and far more powerful. But without a robust identity layer, these systems will be fragile, opaque, and untrustworthy.
As a product leader, I see it clearly: identity is no longer a backend feature. It’s an architectural foundation for the next generation of intelligent systems.
If you’re building for the future, start by asking:
“Can I trust the agents in my system?”
And then:
“Can I prove it?”
In a multi-agent world, identity serves as the foundation for all trust.
Let’s connect: If you’re working on agent frameworks, trust layers, or identity models, I’d love to trade notes. The agentic future is here, let’s build it right.
There are many stories about AI taking over human jobs. Each time technology advances, like with steam power, assembly lines, or automation, people worry about losing their jobs. AI, especially large language models, has brought back those fears. However, it’s crucial to recognize that while AI offers many benefits, it also brings big challenges. At a deeper level, deductive reasoning continues to be a lasting strength of humans.
The Three Types Reasoning and AI has challenges
To understand why, let us start with the basics of human reasoning:
Reasoning Type
Description
Example
AI Proficiency
Deductive
From general rules to specific conclusions
All planets orbit stars. Earth is a planet → Earth orbits the Sun
❌ Weak (needs Symbolic systems)
Inductive
From specific observations to general rules
Earth, Mars, and Jupiter orbit the Sun → All planets orbit stars
✔️ Strong (Pattern Learning)
Abductive
Best explanation given incomplete data
The ground is wet → It probably rained
✔️ Strong (Probabilistic modeling)
AI excels at inductive and abductive reasoning because its architecture is probabilistic and data-driven. But deductive reasoning, which underpins scientific discovery, legal frameworks, and mathematical proofs, remains deeply challenging for AI.
Why deductive Reasoning is Hard for AI
them based on training data. That’s fundamentally different from how humans deduce facts from axioms.
Key Limitations of AI in Deductive Reasoning:
Non-determinism: Outputs vary even with the same input due to probabilistic sampling.
No grounding: LLMs lack a symbolic understanding of truth or causality.
Computational complexity: Symbolic logic engines require significant memory and computational resources, making them unsuitable for the current transformer-first AI infrastructure.
In essence, LLMs can mimic deduction, but they cannot construct or verify deductive truths unless tightly coupled with external logic engines.
Historical Parallel: Kepler and the limits of today’s AI
Consider how Johannes Kepler derived the laws of planetary motion. He didn’t just observe planets; he deduced laws from data, noticing elliptical orbits and harmonic relationships others overlooked.
Today’s AI could ingest the same data, classify it, and perhaps fit a regression curve. But could it infer a universal law from physical patterns?
AI cannot infer a universal law from physical patterns without external symbolic tools, and it cannot do so instinctively either.
This is the crux: humans don’t just learn from labeled data; we synthesize, infer, and challenge. These are traits AI lacks.
The path to Artificial General Intelligence (AGI) requires Symbolic Intelligence
To transition from Narrow AI to General AI (AGI), our models must establish a connection between statistical learning and symbolic logic.
Emerging models that might enable deductive AGI:
Symbolic Logic Engines: e.g., SAT solvers, Prolog, Z3 – already used in theorem proving.
These frameworks begin to touch the nuance humans process instinctively. But they remain research-heavy andhighly compute-intensive, limiting their real-world scalability today.
AI is a tool: It raises the floor and the roof
Yes, AI will eliminate certain types of entry-level cognitive work, much like robots replaced repetitive tasks on factory floors. But just as factory workers evolved into process engineers, robot maintenance technicians, and quality optimization experts, so too will today’s workforce evolve to supervise, audit, and extend intelligent systems.
The issue is not about job loss but job transformation.
Raising the floor: Automating routine tasks, freeing humans from grunt work.
Involves the creation of new domains, such as reasoning over AI outputs, validating symbolic inferences, or designing new logic-based systems.
Just as programming evolved from assembly to C++ to Rust, AI evolves the way we interact with computation. But it doesn’t replace our capacity to reason. It extends it.
The real jobs of the future: Observation, Inference, and oversight
As AI improves, our role will change to:
Monitoring outputs for bias, hallucination, and logical consistency
Observing systems and inferring gaps in their logic
Scaling knowledge across domains that require deductive precision
Securing systems where probabilistic behavior may lead to unpredictable or adversarial outcomes
These are not “basic tasks.” They’re deeply human responsibilities.
Conclusion: Our future is not post-human, it is Post-redundancy
AI won’t replace us; it will make us more essential. By handling repetitive tasks, we can concentrate on our unique ability, the capacity to think critically.
Deductive reasoning is more than a method; it’s a way of thinking. It has supported scientific advancements, philosophical ideas, and legal systems. Even in the age of AI, it remains our greatest competitive advantage.
Artificial intelligence (AI) dominates headlines and boardroom conversations. From chatbots to copilots, AI feels everywhere. But if we apply Geoffrey Moore’s classic “Technology Adoption Lifecycle,” we see a different story: despite the hype, AI still sits with Innovators and Early Adopters. The Early Majority, the pragmatic users who drive true mainstream adoption, remain cautious. Why? They demand trust, and trust in AI hinges on three pillars: security, privacy, and transparency.
Security First: The Foundation of Trust
AI changes the security landscape. Traditional software already faces a barrage of attacks, but AI introduces new risks. Imagine an AI agent with the power to automate tasks across a business. If attackers exploit a vulnerability or misconfiguration, the consequences could be catastrophic: privilege escalation, data exfiltration, or even manipulation of business decisions.
Security must come first. Enterprises, especially in regulated industries, will not trust AI until it proves resilient against both old and new attack vectors. AI systems must defend against prompt injection, adversarial attacks, and unauthorized data access. Companies need robust controls, continuous monitoring, and clear incident response plans.
Pros of investing in AI security
Cons and challenges
Reduces the risk of breaches and attacks
Security investments can slow down deployment and innovation
Builds trust with enterprise and regulated customers
Increased complexity and cost
Protects against new AI-specific threats
Overly restrictive controls may limit AI’s capabilities
Extra security measures can introduce friction for end users
The bottom line: Without strong security, AI will never cross the chasm to the Early Majority.
Privacy: The Competitive Edge
Organizations and individuals hold deep concerns about privacy. Companies hesitate to use proprietary data to train public models, fearing they’ll lose their competitive edge. Consider a manufacturer with unique processes or a retailer with exclusive customer insight; these are valuable assets, not mere inputs for public AI models.
On the personal side, AI blurs the boundaries of privacy. In the past, searching Google for symptoms allowed you to maintain a certain sense of anonymity. Now, if you share health information with an AI chatbot, that data might reinforce the model’s learning. Suddenly, your private details could influence future predictions, raising the specter of data misuse, just as search engines and social platforms have long monetized our data.
AI must respect privacy. Curated, local, or federated models that do not leak sensitive information will win trust. Privacy-preserving techniques, such as differential privacy, data minimization, and on-device processing, will become essential.
Pros of prioritizing privacy
Cons and trade-offs
Protects user and organizational data
Inadequate data may reduce the accuracy of the model.
Preserves Competitive Advantage
Limits the scope of AI learning and generalizing.
Reduces the risk of regulatory penalties
Can complicate data management and integration
Builds user trust and willingness to adopt
Increased privacy controls may require more resources to implement
If we want the Early Majority to embrace AI, we must treat privacy as a feature, not an afterthought.
Transparency: The Art of Questioning
AI models, particularly large language models, function as opaque entities. They generate answers by calculating probabilities based on weights, biases, and vast training data. As users, we risk outsourcing our thinking to these systems unless we demand transparency.
Transparency empowers users. When AI provides clear reasoning or explanations, we can evaluate, question, and challenge its outputs. This art of questioning keeps us in control and prevents blind trust in machine-generated answers. But transparency has its limits. Too much openness can reveal proprietary methods or make it easier for bad actors to manipulate the system. We must strike a balance: enough transparency to foster trust and accountability, but not so much that we expose the system to new risks.
Pros of transparency
Cons and risks
Increases user trust and understanding
May expose proprietary methods or intellectual property
Facilitates regulatory compliance and auditing
Could be exploited by adversaries to game the system
Encourages responsible and ethical AI use
Can overwhelm users with too much information
Enables better debugging and error correction
May slow down model deployment if explanations are required
How Curated Models will shine!
The next wave of AI adoption will not come from bigger models or more data alone. It will come from curated, secure, and privacy-preserving AI systems. Whether in software or manufacturing supply chains, organizations want to protect their unique value. They will not willingly use their competitive advantage to train public models. Curated models, trained on carefully selected, private, or domain-specific data, offer a path forward. These models can deliver high performance while respecting privacy and security requirements. They also provide clearer transparency, as their scope and training are well defined
Build Trust: The path to Early Majority
To win over the Early Majority, the AI community should: • Focus on strong security to combat threats • Make privacy integral to design, not an add-on • Ensure transparency so users can understand AI decisions We also need to educate users: AI is a tool, not a prophet. When an AI provides answers, we should continue asking questions. Does the reasoning add up? Can we follow the logic? Only then can we use AI wisely and with confidence.
Conclusion
AI is close to becoming widely used. The Early Majority is looking for evidence that AI systems are safe, private, and clear. By focusing on these aspects now, we can make the leap and create a strong base for long-lasting, responsible innovation.
AI-assisted software development is growing, and as someone who enjoys empowering people with technology, I find this trend exciting. It involves creating or changing software using clear ideas, natural language prompts, or basic frameworks. It’s quick, impressive, and quite freeing.
And yet, when you look at the bigger picture, something feels off.
Statistical patterns vs grounded in secure practices
While vibe coding is a useful tool for sharing ideas, creating prototypes, exploring, and onboarding, it has weaknesses that technical leaders and engineering teams must address. The foundational models trained on Python, TypeScript, and other programming languages often learn from vast amounts of public code, which may not always represent secure or maintainable engineering practices. Some patterns they derive are simply statistical trends and lack a basis in solid software design principles, such as secure design and zero trust. This dependence on potentially flawed data can lead to misunderstandings about best practices, causing developers to adopt insecure or inefficient coding habits without realizing it. As technology changes quickly, relying on outdated or poorly written examples can stifle innovation and weaken the integrity of software projects.
The illusion of safety in noise reduction
Auto-complete features and noise reduction methods in AI coding depend on making patterns in the training data look smoother instead of being based on proven engineering principles. The purpose of these coding solutions is to mitigate friction in the realization of ideas rather than to impose constraints. An unfortunate consequence of this approach is the semblance of correctness: the code appears polished, and the functions seemingly operate as intended; yet, the foundational logic may be flawed, insecure, or incompatible with operational requirements. I draw upon my experience with large enterprises in guiding them toward low-code solutions, and this was a common concern expressed by many of them.
Is the code maintainable?
Although it is improving, vibe-coded software still lacks explainability and rationale. During service outages, particularly when the outage cascades across microservices, third-party dependencies, or cloud infrastructure, it is essential to have more than just syntactically correct code.
You need to have context, contracts, and traceability. Code that is “vibe-coded” into existence often fails the test of operational readiness. Without proper guardrails, you end up with something far worse than legacy software—there, I said it! Legacy software is an example of live software that no one understands and gets really hard to decompose and do anything meaningful.
We are already seeing early signs of this in open-source projects where AI-generated code has proliferated. There are repositories brimming with redundant logic, ambiguous abstractions, and fragile dependencies. In some cases, contributors can’t explain why a block of code exists or what might break if it changes.
Secure Coding and Zero Trust as guardrails are non-negotiable
Now, I am not saying we need to reject AI-generated code; in fact, far from it. The solution is to ground it in the enterprise secure coding principles and zero trust architectures. These should serve as rails, not brakes, on this new mode of development. Enterprises must invest in tooling, policy, and culture that elevate contextual understanding, threat modeling, and least-privilege execution.
The promise of agentic development is real. We will get to a future where intelligent systems reason about business intent, architectural constraints, and security posture before generating code. But we are not there yet. Until then, vibe coding without governance is a fast lane to spaghetti code. Code that looks modern but behaves like legacy.
Let us celebrate the creativity this new medium offers, but let us not confuse vibes with validation!
The modern business landscape has witnessed a shift in how companies approach growth, with two methodologies emerging as dominant forces: Product-Led Growth (PLG) and Customer-Led Growth (CLG). PLG drives rapid initial expansion through product virality and self-service models, while CLG prioritizes deep customer understanding and outcome-focused innovation for sustainable growth. PLG organizations often face a single intense growth phase, while CLG organizations build competitive advantages through superior product-market fit and innovation. Recognizing that a hybrid model—leveraging PLG for market penetration and transitioning to CLG for sustainability—offers a compelling path forward, companies understand how these approaches influence key behaviors in product management and impact measurement.
Understanding Product-Led Growth: The Product as the Primary Driver
Product-Led Growth is a strategy where the product is the main way to attract, engage, and keep customers. In PLG companies, the product itself serves as the key sales and marketing tool, as seen with Slack and Dropbox, which let users enjoy immediate benefits through free trials or freemium options. The core idea of PLG is that when customers see real value from the product, moving to a paid plan feels natural and easy.
The PLG model is based on key principles that set it apart from traditional growth strategies. Companies using PLG focus on self-service onboarding, reduce human involvement in sales, and depend on the product’s natural appeal to encourage growth. This requires products to be easy to use so that users can quickly understand and benefit from them, with sharing features integrated into the experience. The strategy believes that happy users will become advocates, creating a referral cycle that leads to ongoing customer growth.
However, the effectiveness of PLG is highly dependent on product characteristics and market conditions. Research indicates that PLG works best for products that are simple enough for users to understand immediately and where sharing is naturally integrated into the user experience. Products like Calendly, which enables easy calendar scheduling, represent ideal candidates for PLG implementation due to their straightforward value propositions and built-in sharing mechanisms.
The Merits of Product-Led Growth: Speed and Efficiency
Product-Led Growth offers several compelling advantages that have driven its widespread adoption across the software industry. The most significant benefit is the dramatically reduced customer acquisition cost (CAC) that results from the product’s ability to sell itself. When executed effectively, PLG can eliminate the need for extensive sales teams and expensive marketing campaigns, as satisfied users become organic advocates who drive word-of-mouth growth. This cost efficiency is particularly valuable for startups and companies operating with limited resources.
The speed of PLG implementation represents another crucial advantage. Companies can achieve faster time-to-value for customers by providing products that deliver immediate, tangible benefits without requiring extensive onboarding or training. This rapid value delivery accelerates customer adoption and can result in shorter sales cycles compared to traditional sales-led approaches. The self-service nature of PLG also allows companies to scale their user base without proportionally increasing their support infrastructure, creating opportunities for rapid growth.
PLG also generates valuable data insights that inform product development and business strategy. By tracking user behavior and product usage patterns, companies can gain direct insights into customer preferences and needs. This data-driven approach enables more informed decision-making and allows companies to optimize their products based on actual user behavior rather than assumptions. The continuous feedback loop created by PLG can accelerate product iteration and improvement cycles.
The Limitations and Drawbacks of Product-Led Growth
Despite its initial appeal, Product-Led Growth faces significant limitations that become apparent as companies mature and market conditions evolve. One of the most critical challenges is that PLG typically experiences only one sustained growth phase before requiring substantial strategic pivots. As companies grow and their products gain complexity, the self-serve model often hits a plateau where users can no longer achieve advanced outcomes without additional support and guidance. This limitation forces PLG organizations into a continuous cycle of launching new products and ramping up features to maintain growth momentum.
The requirement for near-perfect product experiences from launch represents another significant challenge for PLG organizations. Unlike sales-led models where customer feedback can gradually shape product development, PLG demands a high degree of polish and intuitive design from the beginning. This pressure can lead to rushed product launches or oversimplified solutions that fail to address complex customer needs adequately. The emphasis on immediate usability often comes at the expense of depth and sophistication in functionality.
PLG also struggles with products that require significant customization or serve complex use cases. Research shows that as products move along the complexity spectrum, user proficiency can only keep pace with additional human support, leading to inevitable revenue plateaus. Companies like Salesforce and Segment, which offer robust and multifaceted solutions, often require more hands-on approaches to effectively onboard and support users. This limitation means that PLG is not universally applicable and may actually hinder growth for companies with sophisticated product offerings.
Furthermore, PLG can limit a company’s ability to reach certain customer segments and may create dependencies on specific user behaviors that may not scale across diverse markets. The reliance on viral growth and word-of-mouth can be unpredictable and may not provide the consistent, scalable growth needed for long-term business success.
Understanding Customer-Led Growth: Outcomes Over Features
Customer-Led Growth represents a fundamentally different approach that prioritizes deep customer understanding and outcome-focused innovation over product features and viral mechanisms. CLG organizations place customer insights at the center of all business decisions, from product development to service delivery, ensuring that every initiative directly contributes to customer success and value creation. This approach recognizes that sustainable growth comes from consistently delivering superior customer experiences rather than relying on product virality alone.
The CLG methodology involves continuously gathering customer feedback, analyzing customer behavior patterns, and adapting offerings to meet evolving customer needs. Unlike PLG, which assumes that a great product will naturally drive growth, CLG actively seeks to understand what customers are trying to achieve and builds solutions that enable those outcomes. This approach requires organizations to develop sophisticated customer research capabilities and create feedback loops that inform strategic decision-making across all business functions.
CLG organizations focus on qualifying and quantifying the value delivered to customers, ensuring that business success is directly tied to customer success. This alignment creates a symbiotic relationship where customer achievements drive business growth, leading to higher retention rates, increased customer lifetime value, and more sustainable revenue streams. The approach also emphasizes building strong customer relationships and fostering brand advocacy through consistent value delivery.
The Competitive Advantages of Customer-Led Growth
Customer-Led Growth offers several distinctive advantages that address many of the limitations inherent in Product-Led Growth models. Most importantly, CLG organizations achieve superior product-market fit by continuously aligning their offerings with actual customer needs and desired outcomes. This deep alignment results from the systematic collection and analysis of customer insights, ensuring that product development efforts focus on capabilities that directly contribute to customer success rather than feature accumulation for its own sake.
Contrary to common assumptions, CLG organizations are not slower than their PLG counterparts in achieving growth. The customer-centric approach enables faster identification of market opportunities and more targeted innovation efforts, leading to higher success rates for new initiatives. By focusing on customer outcomes rather than product features, CLG organizations can make more strategic decisions about resource allocation and avoid the feature bloat that often plagues PLG companies seeking to maintain growth momentum.
CLG drives innovation that lifts entire industries rather than just individual companies. By deeply understanding customer challenges and working to solve fundamental problems, CLG organizations often develop breakthrough solutions that set new industry standards. This type of innovation creates competitive moats that are difficult for competitors to replicate, as they are based on deep customer relationships and understanding rather than easily copied product features.
Perhaps most importantly, CLG enables organizations to scale securely without compromising foundational capabilities. Unlike PLG organizations that may sacrifice security, reliability, or customization capabilities in pursuit of viral growth, CLG organizations can build robust, enterprise-grade solutions that meet sophisticated customer requirements. This approach supports long-term customer relationships and enables expansion into enterprise markets without requiring fundamental architectural changes.
CLG also provides more predictable and sustainable revenue growth. By focusing on customer success and retention, CLG organizations typically achieve higher customer lifetime values and more stable revenue streams. The emphasis on customer outcomes creates natural expansion opportunities as successful customers seek to achieve additional goals or scale their usage of proven solutions.
The Emerging Hybrid Model: Combining Strengths
Recognition is growing that neither PLG nor CLG represents a universally superior approach, and that the most effective strategy often involves combining elements of both methodologies. The hybrid model acknowledges that different stages of the customer journey and different types of products may benefit from different growth approaches. This evolution represents a maturation of growth strategy thinking, moving beyond one-size-fits-all solutions toward more nuanced, context-specific approaches.
The hybrid model typically begins with PLG principles to drive initial customer acquisition and early-stage engagement. The product serves as the primary vehicle for attracting users and demonstrating initial value, leveraging the cost efficiency and scalability benefits of the PLG approach. This initial phase allows companies to reach broad audiences and identify potential customers who experience meaningful value from the product. The self-service nature of PLG enables rapid market penetration and user base growth.
However, the hybrid model transitions to CLG principles as customers seek to achieve more sophisticated outcomes or as their usage scales. This transition recognizes that complex customer needs often require human support, customization, and deeper relationships to achieve success. The shift to CLG enables companies to capture greater value from their customer relationships while providing the level of support and guidance necessary for customer success in complex scenarios.
The hybrid approach is particularly effective for companies seeking to move upmarket or expand into enterprise segments. Initial PLG success can demonstrate product value and build market credibility, while the transition to CLG enables the company to address the more sophisticated requirements of enterprise customers. This progression allows companies to grow their customer base efficiently while building the capabilities necessary to serve high-value customer segments.
Implementation of the hybrid model requires careful orchestration of sales, marketing, customer success, and product teams to ensure smooth transitions between growth approaches. Companies must develop the capability to identify when customers are ready for more sophisticated support and ensure that the transition enhances rather than disrupts the customer experience. Success with the hybrid model often depends on having robust customer segmentation and the organizational flexibility to deliver different types of experiences to different customer categories.
Product Management Practices in PLG vs. CLG Environments
The divergence between Product-Led Growth (PLG) and Customer-Led Growth (CLG) extends to fundamental shifts in product management methodologies, requiring distinct strategic approaches, collaboration models, and success metrics. Product managers operating in these environments face radically different priorities that influence every stage of the product lifecycle, from discovery to delivery.
Strategic Focus and Prioritization
In PLG organizations, product managers prioritize feature development that drives organic adoption and virality, often optimizing for metrics like activation rate and time-to-value (TTV) . The product roadmap focuses on removing friction points in the user journey while embedding growth loops directly into the product architecture. For example, Calendly’s product team redefined their activation metric from a single meeting scheduled to five meetings booked within a specific timeframe, ensuring users formed lasting habits with the tool . This data-driven approach requires continuous A/B testing of onboarding flows and feature placements to maximize self-service conversion rates. CLG product managers, conversely, prioritize outcome-aligned capability development over viral features. Their roadmaps emerge from deep analysis of customer-reported challenges and desired business impacts, with features validated against specific customer success metrics before development begins . At companies like Arm & Hammer, product teams used direct customer feedback about unconventional product uses to develop entirely new product lines, demonstrating CLG’s focus on solving articulated customer needs . This approach demands rigorous customer journey mapping and value quantification at every development stage.
Customer Engagement and Feedback Mechanisms
PLG product teams leverage in-product behavioral analytics as their primary feedback mechanism, using tools like heatmaps and feature adoption rates to infer customer needs . When Dropbox observed users struggling with file-sharing workflows through analytics, they prioritized interface simplifications that reduced support tickets by 23% while increasing referral rates . These teams often deprioritize direct customer interviews in favor of scalable data collection methods, though leading organizations like HubSpot combine usage data with automated NPS surveys triggered by specific user actions . CLG organizations institutionalize structured voice-of-customer programs, with product managers spending 30-40% of their time conducting ethnographic research and co-creation workshops . SAP’s UX Engineering team, for instance, embedded product managers in customer operations for weeks to observe real-world ERP usage patterns, leading to workflow redesigns that reduced task completion times by 37% . This hands-on approach extends to beta testing protocols where customers validate solution prototypes against predefined outcome metrics before full development .
Metrics and Success Measurement
The metric portfolio diverges sharply between approaches. PLG teams track product-qualified leads (PQLs), viral coefficient scores, and free-to-paid conversion rates as north star metrics . Heap’s product team famously optimized their knowledge base engagement metrics, discovering that users who viewed three help articles within their first week showed 68% higher retention rates . These metrics feed into growth accounting models that prioritize features likely to move acquisition and activation metrics. CLG organizations measure success through customer-qualified outcomes (CQOs) like ROI realization timelines and solution adoption depth . A CLG product manager at Wildbit redesigned their onboarding process to track how quickly customers achieved their first “value milestone,” correlating 90-day retention improvements of 22% with teams hitting that milestone within 14 days . Enterprise CLG teams often implement outcome scorecards that quantify both quantitative metrics (e.g., process efficiency gains) and qualitative feedback (e.g., stakeholder satisfaction scores) .
Cross-Functional Collaboration
PLG product managers function as growth architects, working closely with growth engineers and UX designers to embed viral mechanics into product experiences . At Slack, product teams collaborate with marketing to create shareable content templates that users can personalize and distribute, directly tying feature development to acquisition goals . This model requires tight integration with data science teams to model the impact of feature changes on viral coefficient and CAC payback periods . CLG product managers operate as customer success enablers, maintaining direct relationships with customer success managers (CSMs) and implementation partners . Salesforce’s CLG teams conduct quarterly business reviews with customers, where product managers present roadmap items directly tied to resolving challenges identified in CSM reports . This approach demands alignment with professional services teams to ensure product capabilities support customized implementation scenarios without creating technical debt .
Adapting to Hybrid Growth Models
Forward-thinking organizations are training product managers in bimodal capability development, enabling them to shift between PLG and CLG paradigms based on product lifecycle stages . Early-stage products might emphasize PLG metrics like PQL velocity, while mature products serving enterprise clients adopt CLG’s outcome-based roadmaps. QuotaPath’s hybrid model provides a blueprint, with product managers using separate pipelines for self-service users (tracking feature adoption) and sales-assisted enterprise clients (tracking outcome achievement timelines) . This duality requires product managers to master both behavioral analytics tools and customer value realization frameworks. The most successful hybrid product managers implement stage-gated development processes where features must demonstrate both PLG-style adoption potential and CLG-aligned outcome impact . A European SaaS company achieved 140% net revenue retention by requiring all new features to pass three gates:
1) User engagement projections from ML models
2) Pilot customer outcome achievement rates
3) Sales team viability assessments for upselling
This approach prevents feature bloat while ensuring alignment with both end-user needs and customer business objectives.
Conclusion
The evolution from single-approach growth strategies to hybrid models reflects the increasing sophistication of both customer expectations and business strategy thinking. While Product-Led Growth offers compelling advantages in terms of initial customer acquisition and cost efficiency, its limitations become apparent as companies seek sustained growth and market expansion. Customer-Led Growth provides the foundation for long-term competitive advantage through superior customer relationships and outcome-focused innovation, but may require different capabilities and approaches for initial market penetration.
The emerging hybrid model represents the most promising path forward for most organizations, combining the efficiency and scalability of PLG with the depth and sustainability of CLG. This approach recognizes that different customers, products, and market situations may require different growth strategies, and that the most successful companies will be those that can effectively orchestrate multiple approaches based on context and customer needs.
Success in implementing either pure or hybrid growth strategies requires careful assessment of product characteristics, customer needs, market dynamics, and organizational capabilities. Companies must resist the temptation to adopt growth strategies simply because they are popular or have worked for other organizations, instead focusing on approaches that align with their unique circumstances and strategic objectives. The future belongs to organizations that can master both product-driven efficiency and customer-driven innovation, creating growth engines that are both scalable and sustainable.
I have started blogging again, and it feels great to be back! It’s an exciting time to jump in, especially with all the developments in AI (Artificial Intelligence). I am really excited because I believe that during our lives, we will be able to find a cure for cancer and tackle climate change globally with AI.
So, what makes AI systems, like chatbots, recommendation tools, or even autonomous vehicles work? The answer is layers. What do I mean by layers? To make AI work, there are many layers involved. These layers are the hidden heroes behind all the amazing things we can achieve with Artificial Intelligence. Let’s explore what makes them brilliant!
1. The Infrastructure Layer
The infrastructure layer is essential for any AI system, providing the computing power and storage to manage large data and complex tasks. Think of it as the oven and tools needed for baking a cake. Key components include cloud platforms, GPUs, and powerful servers. Without this layer, AI systems lack the strength to operate. Besides the main infrastructure, there are also important aspects like security, compliance, identity, scaling, and backup and recovery timelines.
2. The Data Layer
Data is the raw ingredient for AI—like the flour, sugar, and eggs for your cake. The data layer involves collecting, storing, and processing data. It ensures that the data is clean, organized, and accessible for further use. Databases, data lakes, and data pipelines play a crucial role in this layer, ensuring your AI system has a steady supply of high-quality “ingredients.”
3. The Model Layer
Moving on, the model layer is where the real magic happens. This layer involves training and fine-tuning AI models to perform specific tasks, such as recognizing images, understanding speech, or predicting trends. Think of this as mixing and baking your ingredients into a delicious cake. Machine learning algorithms and frameworks like TensorFlow or PyTorch are the key tools in this layer.
4. The Orchestration Layer
This is the conductor of the AI symphony. The orchestration layer ensures that all the other layers work in harmony. It manages workflows, integrates components, and ensures scalability and efficiency. Imagine this as the recipe book and timer that guide you through the baking process. Without orchestration, the entire system can become chaotic and inefficient.
5. The Application Layer
Finally, the application layer is where AI meets the real world. This is the beautifully decorated cake that everyone gets to enjoy. It includes user interfaces, APIs, and AI-powered applications, such as chatbots, recommendation systems, or autonomous vehicles. This layer ensures that the end-user can interact with and benefit from the AI system effortlessly.
Conclusion
In summary, the layers of AI work together like a well-baked cake, with the infrastructure, data, model, orchestration, and application layers playing their distinct roles. As the orchestration layer brings harmony to the entire stack, it ensures that all components collaborate seamlessly to deliver intelligent and efficient solutions. Understanding these layers is the first step toward appreciating the brilliance behind AI systems!
In the realm of product management, “value” is often discussed but rarely defined with the clarity it deserves. At its core, value is the ability to improve and impact the current reality of a customer. It’s not about the prettiness of your deliverables or the meticulousness of your documentation—it’s about creating tangible outcomes that transform how customers experience the world.
The Difference Between Menus and Meals
Imagine dining at a restaurant. The beautifully designed menu teases with promises of delightful dishes. However, if the meal served fails to satisfy or create a memorable experience, the menu’s allure becomes meaningless. Similarly, in product management, gorgeous specifications and polished PowerPoint decks are the menus. They outline possibilities and articulate plans, but they aren’t the meals. The real substance comes from the actual impact these plans have on a customer’s life.
Menus (Outputs): These are the deliverables—specifications, designs, presentations—that provide clarity on what is intended.
Meals (Outcomes): These are the actual results. They are the improvements, the efficiency gains, and the enhanced experiences that customers enjoy.
If we focus solely on outputs without paying attention to the outcomes, we risk missing the point entirely. Our job is not to create beautiful documents for their own sake, but to drive meaningful change.
From Outputs to Outcomes
Many product managers fall into the trap of equating activity with progress. They become enamored with the creation of extensive documents and polished slides, believing that these outputs signal success. However, without a direct line of sight to the outcomes—the real, positive shifts in customer reality—our efforts may end up being impressive on paper but ineffective in practice.
To truly deliver value, we must:
Prioritize Impact: Ask yourself, “How will this feature or specification change the way a customer works, feels, or succeeds?”
Measure Outcomes: Track metrics that matter, such as customer satisfaction, engagement, and long-term business benefits. It’s these numbers that truly validate our efforts.
Stay Outcome-Oriented: Regularly reassess and pivot based on feedback. Clinging to a document that once promised a breakthrough but no longer aligns with customer needs is a surefire way to lose relevance.
The Role of Clarity in Driving Action
Clarity is the bridge between vision and execution. Wonderful documents and detailed specifications are valuable tools only because they provide clarity. They help align teams, set expectations, and serve as a reference point during the tumultuous journey of product development. But the ultimate goal isn’t to maintain clarity for its own sake—it’s to empower teams to take decisive, informed actions that bring about a new reality for customers.
Consider these strategies to ensure clarity translates into action:
Define Clear Objectives: Start every project with a clear statement of the intended impact. What does success look like from the customer’s perspective?
Communicate Transparently: Ensure that every stakeholder understands not just what is being built, but why it matters. This shared vision drives collaboration and innovation.
Iterate and Improve: Use clarity as a starting point, but always remain open to refining your approach. The landscape changes, and so do customer needs. Iterative feedback loops ensure that clarity evolves into meaningful progress.
Creating a New Reality
Ultimately, value is measured by the transformation it brings about. As product managers, our mission is to turn ideas into actions that reshape customer experiences. It’s about moving from theoretical blueprints to real-world results. Every beautiful specification or compelling slide should be viewed through the lens of the impact it can drive. When we focus on outcomes rather than just outputs, we empower our teams to create products that not only meet expectations but exceed them.
In conclusion, value isn’t just an abstract concept or a metric on a dashboard—it’s the very essence of meaningful innovation. By shifting our focus from outputs to outcomes, and by harnessing the power of clarity to drive action, we can truly make a difference in our customers’ lives. And that, in the end, is what real value is all about.
