Here's what caught my eye in the world of AI this week:

1. The Worst Career Advice Ever Given

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   Every technological shift follows the same pattern: automation lowers barriers, increases accessibility, and expands demand. AI-assisted coding is no different.

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   Some say programming will disappear as AI advances. That’s a fundamental misunderstanding of how technology works. History continually shows that when tools improve, more people adopt them—not fewer.

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   Andrew Ng captures this perfectly: “Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you.” AI will transform the role of engineers, but it won’t replace the need for experts who can architect, refine, and deploy reliable AI solutions.

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   The winners in this AI revolution won’t be those who sit back and watch— it’ll be those who learn how to command the machines.

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   “Dear friends,

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   Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful!

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   In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment.

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   As coding becomes easier, more people should code, not fewer!

   Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step. (By the way, to learn more about AI assisted coding, check out our video-only short course, “Build Apps with Windsurf’s AI Coding Agents.”)

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   I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively.

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   One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is the best way to do that.

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   When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result.

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   Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools continue to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do.

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   Keep building!

   Andrew”

Quote of the week

2. Mastery isn’t about knowing all the answers—it’s about shaping them. AI is accelerating change, making yesterday’s skills obsolete and today’s expertise temporary. The future belongs to those who think like master craftsmen, capable of evolving with each challenge, adapting their tools, and redefining what’s possible.

3. The Leading Generative AI Companies

The AI Investment Surge Has Only Just Begun

The AI revolution isn’t slowing down—it’s just getting started. The generative AI market rocketed from $191 million in 2022 to $25.6 billion in 2024, a staggering 134x growth in just two years. And this is just the opening chapter.

The Future of AI Spending: A Prediction

By 2030, we won’t be talking about billions—we’ll be talking about trillions. Why? Because AI is no longer an experimental technology; it’s becoming the backbone of business, infrastructure, and competitive advantage. Companies are not just adopting AI; they are restructuring entire industries around it.

• AI Infrastructure Boom: The data center GPU market soared to $125 billion in 2024, with NVIDIA holding 92% market dominance. But the hunger for compute power will only grow as AI models become more complex, requiring exponential increases in high-performance computing.

• The Cloud AI Arms Race: Microsoft and AWS are cementing themselves as AI powerhouses, leading in foundation models and model management platforms. Meanwhile, AI services are rapidly expanding, with Accenture and Deloitte spearheading a fragmented but booming market.

• Enterprise AI Is Just Getting Started: Walmart’s CEO, Doug McMillon, put it best—AI is fundamentally reshaping how businesses engage with customers, employees, and operations. Companies are no longer just experimenting; they’re embedding AI into every facet of their organizations.

What Comes Next?

The next wave of AI investment will dwarf today’s numbers. The demand for AI-native infrastructure, services, and applications will create trillion-dollar markets, while enterprises that fail to integrate AI into their core strategies will be left behind.

The bottom line? The AI gold rush is far from over. If you think the spending has peaked, you haven’t seen anything yet.

4. AI Quick Bytes: Ethical Leadership in the Age of AI

🚀 As AI reshapes industries, ethics isn’t a side note—it’s the North Star. We can automate tasks, optimize decisions, and even generate creative work, but one thing we cannot outsource to AI? Ethical judgment.

Ethical dilemmas aren’t about black-and-white answers—they’re about understanding how to think through complexity. Yet, most business ethics frameworks ignore a key factor: relationships. In a world driven by AI, balancing principle-centered and relational ethics is more critical than ever.

Two Core Approaches to Ethics

🔹 Principle-Centered Ethics – Morality is based on rules and duties.
🔹 Relational Ethics – Morality is shaped by context and human connections.

📌 Principle-Centered Ethics:
1️⃣ Utilitarianism – The greatest good for the greatest number. (But do we sacrifice the few for the many?)
2️⃣ Deontology – Duty-based ethics. (Does duty override compassion?)
3️⃣ Virtue Ethics – Character-driven morality. (How do we consistently embody all virtues?)

📌 Relational Ethics:
4️⃣ Aesthetics – Finding beauty in work and life. (Does this distract from hard ethical decisions?)
5️⃣ Ethics of Care – Compassion and empathy. (How do we balance care for others with self-care?)
6️⃣ Ethics of Hospitality – Welcoming and sheltering others. (Where do we draw the line between generosity and self-protection?)

The Leadership Edge: Meta-Capabilities

To lead ethically in the AI era, leaders must develop:
🧠 Meta-awareness – Seeing the bigger picture.
🤔 Meta-cognition – Thinking about thinking.
📚 Meta-learning – Adapting to new challenges.

Why It Matters

AI will keep evolving, but ethical decision-making remains a human responsibility. Wise leaders don’t just follow rules—they balance principles, relationships, and critical thinking to lead with integrity.

⚡ AI won’t replace ethical leadership. But ethical leadership will define how we use AI.

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5. AI is Complex—Your Roadmap Shouldn’t Be

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   AI transformation isn’t about complexity; it’s about clarity. A well-structured product roadmap translates vision into action, ensuring your team isn’t just experimenting with AI but implementing it effectively. The best roadmaps decompose big ideas into small, executable steps—because progress happens one breakthrough at a time.

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   This is a fantastic deep dive into product roadmaps! If you're looking for a quick summary of the key takeaways from Ben Yoskovitz's survey and insights, here’s a Byte-Sized Breakdown😀 

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   8 Quick Bytes on Product Roadmaps

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   1️⃣ Roadmaps Are More Strategic Than Execution-Focused

   • The best roadmaps tell a story and align with company objectives.

   • Execution is important, but consistency in delivering builds trust.

   • If a roadmap lacks a clear "why," stakeholders lose confidence.

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   2️⃣ Short-Term Planning is King (Especially for Startups)

   • Most roadmaps focus on 3-12 months, with startups favoring shorter timelines.

   • Big companies lock in 1-year plans, often leaving little room for flexibility.

   • Monthly updates are common—because 💩 happens.

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   3️⃣ Roadmaps Often Miss the “Why”

   • Many teams document "what" and "when" but not "why" decisions were made.

   • This creates misalignment, especially in large companies.

   • Solution? Tie the roadmap directly to business models and strategic goals.

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   4️⃣ What’s on the Roadmap?

   • 50% of teams include product vision (but…shouldn’t that be higher?).

   • Most roadmaps are time-based over goal-based, though a mix works best.

   • Only 50% include success metrics, raising questions about measuring impact.

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   5️⃣ Prioritization is Messy (And Sometimes Political 😬)

   • Less than half of teams use formal prioritization frameworks.

   • RICE and OKRs are the most common methods.

   • Too often, prioritization aligns with PMs’ career goals, not company impact.

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   6️⃣ The CEO’s Opinion Still Holds Weight

   • Business objectives rank highest in roadmap decisions—above customer input.

   • Exec buy-in is key, but balancing bottom-up ideas with top-down alignment is tricky.

   • Too many stakeholders = roadmap chaos.

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   7️⃣ AI is Slowly Creeping into Roadmaps

   • Most teams aren’t using AI directly for roadmaps.

   • Some use AI for research, insights, and documentation.

   • But fully AI-driven roadmap planning? Not quite there yet.

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   8️⃣ Communication is the Biggest Roadmap Challenge

   • Roadmaps = alignment tools, but keeping stakeholders informed is tough.

   • Top communication methods:
     ✅ Quarterly executive presentations
     ✅ Monthly team updates
     ✅ Notion/Confluence as a “source of truth”
     ✅ Roadmap visualization tools (Jira, Linear, Miro)

   • The biggest mistake? Not involving engineers early enough.

   🚀 Action Steps for Product Leaders:

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   ✅ Make your roadmap strategic—tie every item to a clear outcome.
   ✅ Kill the backlog every so often—if it's important, it’ll resurface.
   ✅ Improve roadmap communication—different stakeholders need different levels of detail.
   ✅ Experiment with AI—even if it’s just for gathering insights or summarizing feedback.
   ✅ Align early & often—especially with engineering, sales, and execs.

   Product roadmaps are messy but essential—so if yours isn’t working, fix the process before fixing the roadmap. 💡

6. Sunday Funnies 🤣 .

7. Simplicity in Strategy

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   One of my favorite quotes comes from Mark Twain:

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   "I did not have time to write a short letter, so I wrote a long one."

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   The best work in strategy, design, and execution follows the same principle—simple, intuitive, and elegant. But achieving simplicity is the hardest task of all. It requires distillation, discipline, and a relentless focus on what truly matters. Think of the Google search bar—effortless in appearance, yet backed by one of the most sophisticated algorithms ever built.

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   There is a fine balance between clarity and over-intellectualizing. Too much complexity, and you lose people. Too little, and you risk oversimplification. That’s why I’ve always been drawn to powerful visuals like the Strategy House. In one slide, it aligns an entire company around a shared vision and execution plan.

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   That’s not just structure. That’s strategy. And when done well—it’s nothing short of magic.

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8.  Agentic Retrieval - Augmented Generation: A Survey On Agentic RAG

AI Technologies are morphing together taking the best of each to create a better whole. In computing, a single byte comprises 8 bits—each essential for encoding information efficiently and precisely. Similarly, the Agentic Retrieval-Augmented Generation (Agentic RAG) framework significantly enhances traditional Large Language Models (LLMs) by integrating autonomous AI agents into the retrieval-generation pipeline. Traditional Retrieval-Augmented Generation (RAG) combined the generative power of LLMs with external data retrieval but faced limitations in static workflow structures and difficulty adapting to complex, real-time scenarios.

Agentic RAG addresses these limitations by embedding intelligent, autonomous agents into the retrieval-generation pipeline. These agents employ advanced techniques such as reflection, planning, tool use, and multi-agent collaboration, thereby enabling dynamic retrieval strategies, iterative refinement, and adaptive problem-solving. This results in highly flexible, context-aware, and scalable systems capable of multi-step reasoning and handling diverse and complex tasks.

Analogy: Just as a byte is composed of 8 distinct bits that collectively encode complex information, Agentic RAG integrates distinct but interconnected components—reflection, planning, multi-agent collaboration, and dynamic adaptability—to produce sophisticated, precise, and real-time contextual responses.

8 Bits of Key Highlights:

1. Dynamic Adaptability: Agents dynamically select retrieval and reasoning strategies based on query complexity, ensuring accurate and timely responses.

2. Enhanced Multi-Step Reasoning: Excels at iterative, multi-hop reasoning and contextually nuanced query resolution.

3. Autonomous Decision-Making: Employs agents capable of independent evaluation and management of information retrieval, optimizing relevance and accuracy.

4. Multi-Agent Collaboration: Incorporates specialized agents working collaboratively, efficiently distributing complex tasks for improved scalability.

5. Iterative Refinement and Correction: Implements self-correcting mechanisms where agents iteratively evaluate and enhance retrieval results, ensuring high-quality outputs.

6. Strategic Planning and Tool Use: Agents strategically utilize external tools and APIs, significantly expanding their capabilities beyond mere text generation.

7. Hierarchical Agent Structures: Uses hierarchical systems, enabling strategic prioritization and structured orchestration to efficiently address multi-level complexities.

8. Broad Industry Applications: Demonstrates effectiveness across multiple sectors, including healthcare diagnostics, financial analytics, customer support automation, educational technologies, and legal workflows.

Bakers Bits

State Maintenance Across Workflows: Maintains contextual understanding and document state, enhancing coherence and relevance across complex tasks.

Comprehensive Tools and Frameworks: Supported by robust tools and frameworks (LangChain, Hugging Face, LlamaIndex), facilitating rapid development and deployment of versatile, scalable agentic solutions.

Agentic RAG’s architecture is like the eight bits of a byte—individually functional yet collectively powerful—enabling complex real-world problem-solving with precision, context-awareness, and scalability.

Until next time, take it one bit at a time!

Rob

Thank you for scrolling all the way to the end! As a bonus check out Brij Kishore Pandey Agentic AI, which allows AI models to learn and execute tasks autonomously. He provides a strategic roadmap for understanding this new technology, covering topics from foundational AI concepts to real-world applications. Mastering Agentic AI will be essential for engineers and developers in the future.

P.S.

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