Perspectives tech experts might miss

"Knowledge can be a double-edged sword when it comes to creativity. While knowledge in a domain facilitates action and work in the domain, it can also constrain from thinking outside the domain." - Manu Kapur After WWII, Ferruccio Lamborghini turned deserted war machines into tractors and built a thriving business. Lamborghini was also a passionate Ferrari owner. However, after experiencing repeated problems with Ferrari clutches, he believed he had a way to improve them. He requested a meeting with Enzo Ferrari. The meeting didn’t go well. Enzo dismissed him with a sneer: “You stick to making tractors, and I’ll make cars." It was the classic expert’s blind spot. Ferrari assumed his mastery of racing cars put him beyond challenge. But overconfidence and expertise is a fragile shield. From that clash of perspectives was born one of the world’s most iconic automotive brands. Sometimes it takes an outsider to see what an insider cannot—or will not. In my workshops, I share the fascinating work of Dr Trafton Drew. In 2013, he asked 24 expert radiologists to examine CT scans for signs of lung cancer. Without their knowledge, he inserted the image of a gorilla — forty-eight times larger than a tumour — into one of the scans. Over 80% of the radiologists missed it, even though eye-tracking showed many had looked directly at the gorilla. Their expertise had tuned their vision so precisely to detect small, round nodules that something incongruous, even something as large as a gorilla, was invisible. As Drew put it, “Part of the reason radiologists are so good at what they do is that they are very good at narrowly focusing their attention. The cost is that they’re subject to missing other things, even really obvious large things like a gorilla.” Expertise sharpens vision — but it can also narrow it. Just as radiologists miss gorillas when focused on nodules, so too experts miss the future when locked into their theories. As for experts, “The average expert was roughly as accurate as a dart-throwing chimpanzee”. - Philip Tetlock When we become experts, our brains adapt to do certain tasks effortlessly. The tradeoff is that by strengthening some connections, we weaken others. What makes us skilled also makes us narrow. Japanese psychologists Giyoo Hatano and Kayoko Inagaki captured this trade-off in their distinction between routine expertise and adaptive expertise. Routine experts thrive in stable, predictable environments. They are precise and efficient, but brittle when the context shifts. Adaptive experts, in contrast, can take knowledge into new domains. Their edge comes not from efficiency alone but from flexibility — forged through variation, struggle, and even failure.

Reflecting on 30+ Years as an IT Educator—and What It Means in the Age of AI Many people don’t know this, but my career as an educator began in the 1980s, not out of academic aspiration, but practicality: I became an adjunct professor to fund the down payment for my first home. Decades later, what’s changed—and what hasn’t? The technologies we teach and the platforms we use for learning are nearly unrecognizable compared to where we started. But one thing remains constant: the fundamentals of how we learn and absorb knowledge, especially in the dynamic world of IT, have not changed. Today, I want to share three recommendations for IT professionals seeking to skill up in AI and leverage it to advance their careers: 1️⃣ Be Cautious with Over-Specialized Training I see a lot of people diving deep into specific tools, platforms, or software—chasing certifications or skills that might only be relevant for a year or two. While there’s value in hands-on know-how, don’t let short-term gains distract you from the bigger picture. Too many IT pros know how to “run AI on AWS” but haven’t developed a solid grasp of broader architectural or organizational contexts. The danger? Becoming proficient in transient skills, but missing the larger, strategic implications. 2️⃣ General Principles First, Details Second If there’s one thing decades as a technologist and educator have taught me, it’s this: focus on the foundational principles before getting lost in the weeds. Architecture matters more than memorizing every function or command. Learn to map business challenges to objective solutions, and from there, build the right technology stack. True architects—those who can connect dots from business to tech—are in short supply, and the market is hungry for that kind of insight. 3️⃣ Cultivate Healthy Skepticism Every new tech wave comes with a massive dose of hype, and AI is no different. The most effective professionals are those who view new technologies with a critical eye: questioning both the promise and the pitfalls. Too many “all in” adopters miss hidden costs, risks, and complexities. Real mastery means not only knowing when to deploy new tech, but also when not to. Sometimes, the best decision is not to follow the crowd. In summary: the future belongs to those who blend a strategic grasp of technology with critical thinking and a commitment to lifelong learning. Don’t chase the shiny objects—build the foundational skills that stand the test of time. #AI #ITCareers #Architecture #TechLeadership #ContinuousLearning

“Who moved my cheese?” Now, an entire generation in tech is searching for the answer—in real life. Over the last two weekends, my phone has been ringing more than usual. The callers? Seasoned IT professionals—15 to 25 years of solid delivery roles—many now blindsided by AI’s tidal wave. Once considered ‘critical’ by titles alone, their actual skills faded under the pressure of dashboards, client calls, and treating people as mere resources. The recent TCS layoff announcement, affecting over 12,000 employees (mostly mid- to senior-level professionals), is not a blip, but a new norm for India’s $283 billion tech sector. The bellwether party is over—now comes the reckoning. - The world’s cheese moved while good people stayed still. - AI didn’t just change jobs—it redefined what’s valued: learning, adaptability, and self-leadership. - Panic won’t bridge the gap. But perspective—and a good coach—can. Key Takeaways 1. Pause the panic. Grieve what’s lost, but don’t miss what’s possible. 2. Audit (really audit) your strengths—beyond job titles. 3. Build the new: upskill in AI, double down on uniquely human abilities. 4. Seek a coach: clarify purpose, regain confidence, map skills to new growth paths. 5. Don’t do this alone. Peer learning beats silent struggle. If your professional “cheese” has vanished, what’s the one skill or perspective you wish you’d grown sooner? Share below—your story might guide someone else, today. “When uncertainty is the new certainty, reinvention isn’t an option—it’s self-respect in action.” Follow Me Sudhakar Reddy G. for more Insights on Leadership and Board Functions. Coach. Mirror. Professional Inner Voice Translator. “Like a silent conch in the storm — true coaching calms, awakens, and guides from within.”

Every once in a while, a new-age startup disrupts the established giants (even when the giant has great management talent). Why does this happen? Often, it’s the "Innovator’s Dilemma" at play. Incumbents get tripped by the following - 1. They don’t invest in products outside of what core customers want. 2. They shy away from areas where immediate revenue/profits aren’t clear. Let’s break this down into three blind spots that incumbents often overlook: 1️⃣ Missing the Next Generation of Users:
  Sometimes, the disruption isn’t in the product itself but in how it is delivered. Incumbents in B2C fintech focused on higher returns and relationship-building, which were rightly the key differentiators. But younger users wanted sleek, effortless UX and hassle-free investing. Startups in neobanking and investment tech are winning by prioritising customer experience over traditional metrics. UX alone may not be a moat, but over time, it’s hard to compete with products that users love to engage with daily. 2️⃣ Missing a Key Trend:
The rise of developer-first products is a classic example. Legacy B2B tools were designed for CXOs or procurement heads. But as developers became scarce and influential, they started adopting tools that empowered them. Platforms like Airtable, Slack, and Postman thrived because they catered directly to the people doing the work—not just decision-makers. 3️⃣ Missing Human Behaviour Insights:
 Missing on the understanding of how people think or act. Social platforms like Instagram and Facebook succeeded by tapping into dopamine-driven behaviours like instant gratification. Features like infinite scrolling and autoplay, once dismissed as trivial and lacking utility, became core to user engagement. Legacy companies often miss these insights, refusing to see them as value drivers. So, how can companies avoid these pitfalls?
 ✔️ Paying attention to non-customers and what they want - even if you don’t build for them.
 ✔️ Build an advisory board with diverse perspectives—different generations, geographies, and genders. ✔️ Run experiments continuously, even for ideas dismissed as “fads.” You never know what will take off! 
 Have you seen companies fall behind because they missed a key trend or customer need? Please share them in the comments. #startups #disruption

CES content has dominated my LinkedIn feed this week. Here's what it reveals about the next revenue challenge for companies in growth mode. CES always showcases what tech companies think the future looks like. This year: AI agents, spatial computing, personalized everything. But here's what the tech demos consistently miss: The future isn't just about what technology can do. It's about who trusts it enough to use it. And trust doesn't distribute evenly across demographics. Research consistently shows that multicultural audiences, particularly Black and Hispanic communities, adopt and interact with new technology differently than white audiences. Not slower. Not less sophisticated. Differently. Multicultural consumers are more likely to: Trust recommendations from community voices over algorithms. Adopt mobile-first solutions before desktop. Value human connection even in automated experiences. Prioritize cultural relevance over technical novelty. I believe that this creates a strategic dilemma for companies. If you are investing heavily in AI-driven personalization, automated content creation, and algorithmic recommendations, but your fastest-growing consumer groups are in markets like Houston, Dallas, Atlanta, and Philadelphia, it is time to reevaluate your marketing strategy. Consumers trust local personalities and community voices more than they trust machines. You're optimizing for yesterday's audience, not tomorrow's revenue. The companies that will win in the next five years will have more than just the best AI infrastructure. They'll be the ones that understand where technology amplifies human trust, and where it erodes it. This is not a question CES answers. It's cultural intelligence translated into competitive strategy. And right now, most companies pouring millions into AI aren't asking it. The tech is impressive. But without cultural strategy, it's optimization in the wrong direction.

Why we need to think like a non-technical person I’ve seen this line in many job posts: “Someone who can deal with non-technical stakeholders.” At first, it sounds simple. But it’s one of the hardest and most valuable skills in tech. Most business owners aren’t developers. They’re experts in their own field, finance, logistics, health, real estate and they turn to us to bring their ideas to life through software. They know what they want to solve, not how to code it. And that’s where we often go wrong. We start talking in our own language, APIs, scalability, latency, and lose them halfway. Thinking like a non-technical person doesn’t mean knowing less. It means knowing how to translate. How to take what a client says, “I want to see my customers’ progress easily” and turn it into a technical process, maybe a dashboard with visual analytics. How to explain a delay not as “the API throwing issue,” but as “we’re waiting for another system to send us data.” It’s about bridging worlds. When you can explain complex things simply, you build trust. When you understand their perspective, you build better products. Here are a few ways to practice it:  • Ask more “why” than “how.” It helps uncover the real business goal.  • Use examples from their world. Not yours.  • Speak in outcomes, not features. “You’ll save time,” not “We’ll add a cron job.” Technology only works when people understand it. So next time, before you explain, pause and ask: “Would this make sense to someone who doesn’t code?” Great engineers don’t just build software → they connect understanding. 🔁 Let’s bridge the gap! Repost to spread the message between tech and non-tech worlds.

🔥 Most academics make this mistake. I sure did. And it took me over 10 years to realize it: 🙈 Coming from the science and engineering side of academia, I shied away from business topics like return on investment and insurance reimbursement codes, as well as subjective (harder-to-quantify) outcomes like user comfort and preference. 🚧 But this impedes true innovation, tech translation, and societal impact. It's a form of tunnel vision. And bias. ❌ Some of these topics—especially those related to business and money—seemed off-limits in academia, and they are still taboo in many academic circles today. 🤔 In retrospect, avoiding these other topics as a grad student and early career faculty member was a mistake. If we want our science and technology to move beyond journal publications and have a societal impact, then we need to be curious about and open to these other areas too. 👉🏽 We don't have to be experts in each area. But sometimes, in academia, I feel like we actively avoid business and practical considerations. Or maybe peer reviewers just beat it out of us. 🤷🏾♂️ 💡 Here's my advice, especially to early career researchers and wearable tech developers in academia (and beyond): 1️⃣ If you are designing a clinical device, spend more time learning about reimbursement codes (not just the technical novelty) and thinking about how it might impact your R&D directions. 2️⃣ If you are developing tech, learn more about the industrial and financial considerations. These may impact your design or research priorities far more than you realize. 3️⃣ If you're developing tech that will be worn or used by a person, then get to know the anticipated users (and other stakeholders), and their subjective preferences, needs, and concerns. It's amazing how much user comfort and usability can shape technical design requirements and what you prioritize in your scientific research. 💯 Academic research has a lot to offer, but it's also surprisingly easy to become siloed in the academic bubble, overemphasize academic or technology perspectives, and overlook other foundational factors that we should understand as researchers and engineers. 🚀 So, get out of the bubble. Get out of your comfort zone. Get to know the users, perspectives, and constraints beyond your specific scientific or engineering discipline. You'll be glad you did!! 🍻 Cheers!

𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗵𝗲𝗮𝗿 "𝗔𝗜 𝗶𝗻𝗵𝗲𝗿𝗶𝘁𝘀 𝗵𝘂𝗺𝗮𝗻 𝗯𝗶𝗮𝘀𝗲𝘀," 𝘆𝗼𝘂'𝗿𝗲 𝗼𝗻𝗹𝘆 𝗵𝗲𝗮𝗿𝗶𝗻𝗴 𝗵𝗮𝗹𝗳 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆. In fact, some of AI's most powerful applications come precisely from its ability to transcend human biases and preconceptions. Yes, there are legitimate concerns about bias in large language models trained on human-written text. But by focusing exclusively on these concerns, we risk developing our own dangerous bias: seeing all AI through the lens of LLMs and their limitations. The spectacular emergence of LLM-based applications has dominated recent discussions about AI. While these tools are impressive, they've created an unintended consequence: many people now think of AI solely in terms of LLMs and their characteristics – including their tendency to reflect human biases. In my 2009 book, 𝘛𝘩𝘦 𝘎𝘦𝘯𝘪𝘦 𝘪𝘯 𝘵𝘩𝘦 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 (https://buff.ly/3E56M4b), I explored how different types of AI could assist invention precisely by operating 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 human preconceptions. Consider these contrasts: • 𝗟𝗟𝗠𝘀 learn from human-generated content and thus inherits human biases, requiring careful guardrails. • 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗿𝘆 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 explore solution spaces free from human preconceptions, often finding superior solutions that human experts might have dismissed. • 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 trained on sensor data rather than human text can discover patterns that transcend human perceptual limitations. Real-world examples demonstrate this diversity's power: • NASA's ST5 spacecraft antenna, designed by evolutionary algorithms, looked like a twisted paper clip – defying traditional engineering aesthetics while outperforming conventional designs. • DeepMind's AlphaGo discovered Go strategies that violated centuries of human tactical assumptions, leading to revolutionary new approaches to the game. 𝗧𝗵𝗲 𝗱𝗮𝗻𝗴𝗲𝗿 𝗼𝗳 𝗲𝗾𝘂𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 is that it might lead us to: • Overlook powerful non-LLM AI approaches that could be better suited for certain problems • Miss opportunities to combine different AI approaches in innovative ways • 𝗨𝗻𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝗶𝗹𝘆 𝗹𝗶𝗺𝗶𝘁 𝗼𝘂𝗿 𝗶𝗺𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗮𝘁 𝗔𝗜 𝗰𝗮𝗻 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 The future of invention lies in understanding the unique strengths and limitations of each type of AI. We might use evolutionary algorithms to discover novel solutions and LLMs to explain them to humans. But we can only realize this potential if we resist letting today's most visible AI technology define our entire conception of what AI is and can be. 𝗦𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝘁𝗵𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝘀 𝗽𝗿𝗲𝗰𝗶𝘀𝗲𝗹𝘆 𝗶𝘁𝘀 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝗯𝗼𝘅. What's your experience with non-LLM forms of AI? Have you seen examples where different AI approaches complemented each other in unexpected ways? #ai #innovation

In the field of software engineering, learning is a continuous journey. Every architectural decision, design pattern, and tool selection comes with tradeoffs that only become clearer with experience. However, I have noticed a growing trend where professionals with only a few years of experience confidently share absolute statements about best practices, technologies, and methodologies. While knowledge sharing is essential for the growth of our industry, it is equally important to recognize the limitations of our experience. A perspective formed in the early stages of one’s career is often subject to change as exposure to complex systems, large scale applications, and real world constraints increases. What may seem like the best approach today might later be recognized as suboptimal due to unforeseen challenges. For example, a junior engineer might advocate for microservices as the "only scalable solution" believing that monolithic architectures are outdated. However, with more experience, they may encounter scenarios where monolithic designs provide significant advantages in terms of simplicity, deployment speed, and maintainability. With time, they might revise their stance, acknowledging that the choice between monoliths and microservices depends on business needs, team expertise, and scalability requirements rather than a universal rule. As professionals, we must approach technical discussions with humility and an awareness of our evolving understanding. Instead of making definitive claims, we should encourage open dialogue, acknowledge tradeoffs, and embrace the possibility that our views will mature over time. Experience reshapes our perspectives, and looking back, many of us may disagree with what we once firmly believed. Let us continue to share knowledge responsibly, fostering a culture where learning from each other includes recognizing the nuances and complexities of our ever-evolving industry. #SoftwareEngineering #ContinuousLearning #TechEthics #KnowledgeSharing

In today's business landscape, there's an interesting disconnect in how we perceive data engineering. Often relegated to the "machine room" of the company, data engineers are viewed as background supporters, delivering data for insights. This perception, which began with software engineers, has carried over to data professionals, despite the increasingly central role of data in business operations. However, this view doesn't align with reality. Data engineers are at the core of any data-driven company, working with its lifeblood - data. These highly educated professionals often have a comprehensive understanding of the company that surpasses individual departments. Yet, their breadth of knowledge isn't always utilized effectively, as they're frequently brought in at the end of processes, after key decisions have been made. This approach may be missing a significant opportunity. Involving data engineers early in ideation and strategic planning could yield valuable insights and more robust solutions. Their unique perspective, combining technical knowledge with a broad understanding of the business, can be invaluable in shaping strategies and solving complex problems. As businesses continue to evolve in this data-driven era, perhaps it's time to reconsider the role of data engineers. By recognizing their potential as strategic partners rather than just technical support, companies might unlock new levels of innovation and efficiency. It's a shift in perspective that could yield significant benefits in our increasingly data-centric world.