In this episode of the Alchemist Influencer Series, Ravi sits down veteran technologist Marcelo Calbucci, drawing on three decades of experience at Microsoft and Amazon to cut through the AI hype. Marcelo offers a grounded, contrarian take on what AI is actually doing to jobs, productivity, and enterprise teams, separating economic reality from fear-driven headlines. He explains where AI truly shines, where human judgment must stay firmly in control, why software engineering roles are likely to grow rather than shrink, and which functions are genuinely at risk. For founders, this conversation is a clear guide to building in an AI-augmented world without losing strategic thinking along the way.

The Influencer Series is an intimate, invite-only gathering of influential, good-energy leaders. The intent is to have fun, high-impact, “dinner table” conversations with people you don't know but should. The Influencer Series has connected over 4,000 participants and 15,000 influencers in our community over the last decade.

These roundtable conversations provide a space for prominent VC funds, corporate leaders, start-up founders, academics, and other influencers to explore new ideas through an authentic and connective experience.

Influencer Series: The Contrarian AI Insights Founders Need to Hear From Veteran Technologist Marcelo Calbucci.

Mainstream media continues to push dramatic narratives about AI's immediate, transformative business impact, creating confusion about its real capabilities and limitations. This hype cycle has led to unrealistic expectations and fear around job displacement.

In this article, we explore Marcelo Calbucci's evidence-based perspective on AI adoption, drawing from their three decades of technology development and leadership roles at Microsoft and Amazon.

Key takeaways

• Economic challenges stem mostly from post-COVID factors, not AI displacement, with most enterprise AI pilots failing to deliver promised results.

• AI excels at tactical execution but founders should maintain human control over strategic thinking and critical business decisions.

• Software engineering jobs will likely double as AI amplifies productivity, competition, and rather than replacing developers.

• Administrative roles face disruption while relationship-based positions in sales, and physical trades remain secure against AI replacement.

Economic Reality vs. AI Job Displacement Fears

The narrative connecting recent layoffs, hiring freezes, and graduate employment challenges directly to AI adoption doesn't hold up under scrutiny. We've been experiencing an economic moment that began with the pandemic, and the job market is still working through those aftershocks.

Let's look at what the research actually tells us. A recent study revealed that 95% of AI pilots in enterprise environments aren't delivering on their promises. Another found that AI simply hasn't replaced jobs yet—at least not in the widespread manner that many predicted.

What's actually happening inside engineering organizations with teams of 100-plus developers? About one-third have fully embraced AI tools in their workflows. Another third is actively experimenting, trying to figure out where these technologies fit. The final third? They've rejected AI entirely and continue working as they always have.

Those who have adopted AI are seeing real benefits, but not the ones dominating headlines. Teams are clearing more items from their backlogs. They're shipping products faster. Engineers are getting more done. But they're not getting fired because of it—a reality check that contradicts much of the current AI narrative.

The AI Hype Cycle and Its Historical Patterns

Anyone who's been in technology for more than a few years recognizes this pattern. We collectively convince ourselves that this time is different—that this particular innovation'll transform everything immediately.

It happened with the PC. Then the internet. Cloud computing had its moment. Mobile sparked the same frenzy. Each time, we overestimated what would happen in two years and underestimated what would unfold over twenty.

AI isn't exempt from this cycle. We're so deep in the hype phase that gaining objective perspective has become nearly impossible. The technology'll absolutely transform how they work and live, but probably not in the timeline or manner currently dominating conference keynotes, venture capital pitch decks, and venture capital pitch decks.

Strategic Thinking and Human Judgment in AI

AI's linguistic capabilities create a dangerous temptation: outsourcing strategic thinking to systems that excel at appearing knowledgeable. Ask an AI to develop your marketing plan, architect your software, or outline your business strategy, and you'll get a convincing response. Whether that response is actually good remains an entirely different question.

The danger lies in how persuasive these systems sound. When AI generates strategic content, it presents ideas with an authoritative tone that makes critical evaluation difficult. You lose the ability to effectively critique the output because you didn't develop the underlying thinking yourself.

There's genuine value in tactical applications. SEO optimization, resume formatting, answering straightforward questions with clear parameters—AI handles these tasks beautifully. But strategy requires something different.

The process of wrestling with strategic questions, exploring possibilities, and developing understanding through effort builds capabilities that no shortcut can replace. Instead of developing your strategic thinking, outsourcing that work to AI results in losing both the output quality and the mental development that comes from the process.

Effective AI Implementation: Strategies for Success

The most productive relationship with AI inverts the typical approach. Instead of asking AI to build your strategy, build your strategy first and then ask AI what's wrong with it.

This shift transforms AI from a crutch into a sparring partner. Come to the conversation with formed ideas and request criticism. Ask it to identify blind spots. Challenge it to find weaknesses in your reasoning. AI excels at this type of analytical work.

What you're avoiding is confirmation bias. Don't use AI to validate decisions you've already made, to rubber-stamp directions you're already committed to pursuing, or to stress-test your thinking and surface considerations they might have missed.

Reality-testing through AI creates a feedback loop that strengthens both human judgment and the quality of AI interactions. You maintain ownership of strategic direction while leveraging computational power to examine your ideas from angles you might not naturally consider.

Exploring Collaborative AI Systems

Every current AI tool operates in what we might call single-player mode. You open ChatGPT or Claude, work through your prompts, receive your responses, and that entire interaction remains private and isolated.

In reality, we haven't figured out how to make AI a team sport yet. Imagine a Notion document or Google Sheet where an AI agent participates alongside human collaborators, where everyone benefits from shared interactions and accumulated context.

Strategy represents an ideal application for this collaborative approach. Teams already struggle to develop and refine organizational direction together. Adding AI as an active participant in those conversations—visible to everyone, responding to collective input—could fundamentally change how strategic planning happens.

The invention will likely emerge from startups. That's where genuine innovation typically originates. But adoption at scale? That's where Microsoft and Google hold significant advantages through their existing enterprise platforms, distribution channels, and partnerships.

Visit Alchemist Accelerator at alchemistaccelerator.com to explore how emerging founders are addressing these collaborative AI opportunities within our portfolio.

Cross-Functional Integration and the Next Software Frontier

Figma demonstrated something important: that design tools could extend beyond pure design into adjacent functions like product management, and engineering collaboration. That boundary-crossing created enormous value.

Something will emerge that glues together the entire software development pipeline. Whether Figma evolves to become that platform or whether an entirely new company builds it from scratch remains an open question.

These integrated platforms will reduce the friction between specialized domains while preserving the expertise and judgment that each function brings. The coordination overhead that currently consumes so much time—transferring information from one system to another, translating between different tools, and maintaining consistency across platforms—represents pure waste that technology should eliminate.

The winning approach might come from an established player expanding their beachhead. It might come from a new entrant designed specifically for cross-functional collaboration. It might come from an orchestration layer that connects existing tools. We genuinely don't know, which makes it an interesting space to watch.

The Challenge of Expanding the Software Engineering Workforce

Here's a genuinely contrarian position: we're going to need significantly more software engineers over the next decade, potentially double the current workforce.

Microsoft conducted research years ago examining how engineers actually spend their time. Less than 30% went to writing code. The rest? Architecture decisions. Collaborating with product managers and designers to determine what's possible. Debugging. Code review. Understanding business requirements and translating them into technical approaches.

AI handles the coding portion beautifully. The experience of building with AI assistance is genuinely transformative—the most enjoyable coding experience many engineers have ever had. But AI still makes mistakes. It requires steering, correction, and oversight. The review process remains human-intensive.

What happens as AI makes coding faster? Organizations ship more features. They address more items from their backlog. They move faster on product development. Competition intensifies because everyone gains these productivity advantages simultaneously.

That competitive dynamic creates demand for more engineers who can effectively direct AI tools toward business objectives. The wave rises for everyone, and companies need human judgment to navigate the resulting complexity.

Look back twenty years. People predicted we'd reached peak demand for software engineers—everyone had a PC, what more could we possibly need? Then cloud computing emerged. SaaS transformed business software. Mobile created entirely new categories. The industry expanded to employ tens of millions of engineers globally.

Job Security in the AI Age and the Roles Facing Disruption

Not all roles face the same AI future. Some functions have natural ceilings on demand that AI'll ruthlessly expose.
Project management, particularly coordination-heavy flavors, faces significant disruption. When 90% of a role involves moving information from one place to another—reading documents, processing data, and generating reports—AI can automate most of that work. We'll need perhaps one-tenth the project managers currently employed.

Administrative support functions across HR, IT, legal, procurement, accounting, and finance face similar pressure. These departments have demand ceilings, and as AI improves at handling their core functions, organizations'll need fewer people in these roles.

What's safe? Anything built on human relationships. Sales and business development require interpersonal connections that AI can't replicate. Therapy, despite AI's growing presence in mental health applications,'ll continue needing human practitioners.

Physical trades offer perhaps the strongest job security. Plumbers, electricians, and roofers—anyone working in brick-and-mortar environments faces minimal AI displacement risk. The digital economy's expansion only highlights the enduring value of skilled manual labor.

Adapting to Thrive in the AI-Augmented Future

The founders who'll succeed in this environment recognize AI as an amplifier rather than a replacement. They focus on opportunities where technology enhances human judgment instead of attempting to bypass it entirely.

Calbucci's advice for founders navigating this landscape distills to six words: "We're entering an adaptable era." That framing captures something essential—flexibility matters more than prediction and continuous learning trumps rigid expertise. The competitive advantage belongs to those who can adjust their thinking as both AI capabilities and market dynamics evolve.

What we're experiencing isn't fundamentally different from previous technological shifts, but it does require the same adaptability that characterized successful responses to the internet, mobile, and cloud computing. The companies that thrive will be those that maintain clear-eyed assessments of what AI can and cannot do, that preserve human judgment where it matters most, and that remain flexible enough to adjust as this technology matures beyond its current hype cycle into genuine, sustained transformation.

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