How do the questions change?

Second, for AI to become truly mainstream, it must augment our abilities and be explainable.

The real challenge begins where the prototype ends.

The data sources were not as comprehensive as initially thought, and the data quality varies significantly.

The real challenge begins where the prototype ends.

The real challenge begins where the prototype ends.

Stochasticity is a wonderful engine for creativity, but a terrible companion when you need to return to a known state.

The role of a product manager (PM) in ML research teams is not just beneficial but essential.

Not a PM. Not a TPM. Not a researcher or a VP.

It's about setting the right context, fine-tuning the parameters, and iterating with purpose.

With roles. With responsibilities. With interfaces. With expectations. With management.

Together = L10/L11 research in half the time

But the human underneath the suit is no longer being replenished.

Together = L10/L11 research in half the time

“Exponential” remains a metaphor, not an established curve.

verification is the mechanism that turns a recommendation into something you can execute without fear.

Neither automatically establishes that the proposition captures our intention, the tests cover every important situation, or the outcome is one we should want.

our propensity to fall in love with reflections of our own ideals, beliefs, and values.

But the human underneath the suit is no longer being replenished.

The true danger is not that AI replaces originality, but that culture stops supplying it.

The founder acts as a lossy filter — compressing, distorting, sometimes removing the warnings embedded in the original texts.

A mind capable of revising its world, not just storing it.

An AI mathematician must search across representation spaces, not just within them.

An AI mathematician must search across representation spaces, not just within them.

In decision systems, the analog is verification under constraints:

In both cases, a robust internal model helps decide when to stay local and optimize incrementally, and when to leap to a new representation or strategy.

In decision systems, the analog is verification under constraints:

The data itself was the moat.

Calmness with controllability. Presence with accountability.

But the human underneath the suit is no longer being replenished.

The question is how much understanding remains recoverable, by whom, and at what cost.

Second, for AI to become truly mainstream, it must augment our abilities and be explainable.

By providing alternatives (not optimality) and assisting us in making more objective and informed decisions,

By providing alternatives (not optimality) and assisting us in making more objective and informed decisions,

The data sources were not as comprehensive as initially thought, and the data quality varies significantly.

Stochasticity is a wonderful engine for creativity, but a terrible companion when you need to return to a known state.

In decision systems, the analog is verification under constraints: feasibility, invariants, safety envelopes, “this will not break when the world is adversarial.”

It's about setting the right context, fine-tuning the parameters, and iterating with purpose.

The role of a product manager (PM) in ML research teams is not just beneficial but essential.

That’s the bottleneck. Not compute. Not capability. But cohesion.

A manager’s job is to create an environment of success, by continuously tuning temperature and pressure to fit the moment.

A manager’s job is to create an environment of success, by continuously tuning temperature and pressure to fit the moment.

If we design them like coached sports—sandboxes, reps, verification habits, psychological safety—more people will build the tinkering muscle without losing the narrative and social strengths that keep systems humane.

If we design them like coached sports—sandboxes, reps, verification habits, psychological safety—more people will build the tinkering muscle without losing the narrative and social strengths that keep systems humane.

With roles. With responsibilities. With interfaces. With expectations. With management.

In the world of AGI, affordances are the tasks. But signifiers? They’re the invisible glue.

A mind capable of revising its world, not just storing it.

An AI mathematician must search across representation spaces, not just within them.

In both cases, a robust internal model helps decide when to stay local and optimize incrementally, and when to leap to a new representation or strategy.

The data itself was the moat.

LLMs are not replacing Google; they’re becoming something else entirely. An advisor. A confidant. A digital friend.

LLMs are not replacing Google; they’re becoming something else entirely. An advisor. A confidant. A digital friend.

Instead, it would be a graph of agents — autonomous entities specialized in language, vision, reasoning, coding, and emotional inference.

Instead, it would be a graph of agents — autonomous entities specialized in language, vision, reasoning, coding, and emotional inference.

Calmness with controllability. Presence with accountability.

Calmness with controllability. Presence with accountability.

This isn’t a fight over AI features. It’s a fight over who controls the loop.

When it finds inefficiencies, it generates and tests new code — small adjustments at first, larger reorganizations later.

In that world, the OS doesn’t just run applications. It adapts.

You bring the analogies. You bring the conceptual leaps. You bring the cross-domain mental models. You decide where to dig — and how deep.

In that world, humans don’t write code. We define intent. We set constraints. We conduct.

In that world, humans don’t write code. We define intent. We set constraints. We conduct.

With roles. With responsibilities. With interfaces. With expectations. With management.

In that world, humans don’t write code. We define intent. We set constraints. We conduct.

The question is how much understanding remains recoverable, by whom, and at what cost.

In decision systems, the analog is verification under constraints: feasibility, invariants, safety envelopes, “this will not break when the world is adversarial.”

AI designs a faster chip. That chip trains a better AI. That AI designs an even faster chip.

Alex, standing before a sprawling whiteboard, sketches the architecture of a new application destined to transform social networking. The challenge? To develop, test, and refine this complex software in a fraction of the time traditionally required. The solution? A synergistic partnership with two advanced AI systems: one, named CodeGen, specializes in rapid software development; the other, ArchiReview, excels in code analysis and optimization.

Together = L10/L11 research in half the time

With roles. With responsibilities. With interfaces. With expectations. With management.

For research, that means making consequential alternatives, failed experiments, assumptions and verification evidence available alongside the result.

Let’s imagine a fictional device called TimeLoop and craft a speculative interview around it. Ten years from now, in the bustling streets of a city where digital innovation intertwines seamlessly with daily life, I find Jordan, an early adopter of TimeLoop. Here is the interview I could have conducted...

The true danger is not that AI replaces originality, but that culture stops supplying it.

The founder acts as a lossy filter — compressing, distorting, sometimes removing the warnings embedded in the original texts.

three Neural Banditos were arguing about why machines never have new ideas.

three Neural Banditos were arguing about why machines never have new ideas.

Insight is not one mechanism. Insight emerges at the intersection of multiple modes of intelligence.

He was prediction error wearing a cowboy hat.

Mathematics begins where the gradient points nowhere obvious.

Needs to know when to push the update… and when to leave the damn weights alone.

verification feedback loops that tighten or loosen exploration.

There ain’t no learning if nothing is in focus.

Transformers began this work, but mathematics demands something sharper: attention must become semantic, not just lexical.

Intelligence isn’t made of neurons. It’s made of balance.

This meta-controller keeps the whole system in a productive regime – not too hot, not too cold.

Like a monk copying manuscripts in a tower of mirrors. Knows everything and discovers nothing.

In the last decade, we learned how to make models that perform mathematics — but not yet models that invent mathematics.

A way for the model to shift its own learning conditions — be bold or cautious depending on the story the world is telling.

In that world, the OS doesn’t just run applications. It adapts.

This week I share a white paper on the power of hashtag#automation in an hashtag#ai era.

But what if we could decouple speed from ecological impact? I coach my team so that our work begins with a premise: Sustainable logistics isn’t merely about reducing emissions but reimagining the pathways through which goods travel across the globe.

Investors need to see real, sustainable use cases that justify their investments.

The real challenge begins where the prototype ends.

Users who have mastered Photoshop expect a high degree of control over their work.

Stochasticity is a wonderful engine for creativity, but a terrible companion when you need to return to a known state.

It's about setting the right context, fine-tuning the parameters, and iterating with purpose.

Think of AI as a junior team member—learning, assisting, and growing with feedback.

humans must curate and interpret AI-generated content with structure, analogies, and, most importantly, taste.

You bring the analogies. You bring the conceptual leaps. You bring the cross-domain mental models. You decide where to dig — and how deep.

In the world of AGI, affordances are the tasks. But signifiers? They’re the invisible glue.

Define shared affordance languages across research, engineering, and product. Let discovery, iteration, and deployment speak the same dialect.

The paradox of personalization challenges us to balance the desire for tailored experiences with the need for privacy and control. As AI becomes adept at reading our digital footprints, the line between helpful personalization and intrusive surveillance blurs. It’s a dance on the tightrope of consent, where each step must be measured and mindful.

Calmness with controllability. Presence with accountability.

The development of hardware such as GPUs (Graphics Processing Units) has been critical in this phase. Originally designed for gaming, these devices excel at handling the matrix and vector operations that are central to AI processes. Google’s introduction of TPUs (Tensor Processing Units) bespoke the specialized hardware demands of AI’s computational needs, showcasing how technological innovations continue to drive forward the capabilities of AI systems.

AI designs a faster chip. That chip trains a better AI. That AI designs an even faster chip.

By providing alternatives (not optimality) and assisting us in making more objective and informed decisions,

Me: “So, in essence, the role of AI in logistics is evolving from seeking perfection to empowering decision-making. How do you see this impacting your daily operations?”

“The journey from the tactile satisfaction of a touchscreen to the emotional resonance of AI doesn’t just chart technological progress—it narrates a deepening relationship between humans and machines.”

Calmness with controllability. Presence with accountability.

humans must curate and interpret AI-generated content with structure, analogies, and, most importantly, taste.

In an age awash with data and digital creations, the essence of human creativity transforms, embracing a role reminiscent of an artistic director in a grand theater of innovation. This paradigm shift is not merely a response to the information deluge but a strategic realignment, positioning human ingenuity at the helm of the creative process and guiding the immense potential of artificial intelligence to areas where it can serve us best.

The effective use of AI, then, hinges on embracing its imperfections and understanding its mechanics. It requires recognizing that the stochastic nature of LLMs, their tendency to explore and ‘hallucinate,’ is not a flaw to be mitigated but a feature to be leveraged. This shift in perspective transforms AI from a mere tool for productivity into a collaborator in the truest sense, capable of enhancing human creativity in ways previously unimaginable.

Users who have mastered Photoshop expect a high degree of control over their work.

Yet, this integration is not without its hurdles. The challenge lies not in the capability of the AI but in our approach to managing and mentoring these digital counterparts. It requires a shift in perspective, a recognition that the AI’s journey from novice to expert is contingent on our willingness to invest time, effort, and, most importantly, patience. The path to fully realizing the potential of AI in the workplace is paved with the same principles that guide effective human leadership: clear communication, consistent feedback, and a commitment to mutual growth.

With roles. With responsibilities. With interfaces. With expectations. With management.

our propensity to fall in love with reflections of our own ideals, beliefs, and values.

Their laughter fills the room, a testament to the joy and connection the Memory Weaver has brought into their lives. It’s a vivid demonstration of how, when technology is designed with a deep understanding of human emotions and needs, it can transcend its circuitry and code to become an integral, cherished part of our lives.

“The personalization algorithms, designed to tailor content to our individual tastes and preferences, can inadvertently confine us within echo chambers.”

someone who controls the algorithms does not just have influence; they hold a lever on perception itself.

“The personalization algorithms, designed to tailor content to our individual tastes and preferences, can inadvertently confine us within echo chambers.”

Instead, it would be a graph of agents — autonomous entities specialized in language, vision, reasoning, coding, and emotional inference.

As we delve deeper into the odyssey of artificial intelligence and its stewards, we encounter a critical juncture: the evolution from minimum viable products (MVPs) to fully realized AI solutions. This phase of the journey is marked not by the quest for technological supremacy, but by the pursuit of meaningful, sustained engagement with the end-users. It’s a narrative about transcending the traditional paradigms of product development to embrace a more collaborative, iterative approach that places the user at the center of the innovation process.

The real challenge begins where the prototype ends.

This groundbreaking game owes its existence to the vision of a product manager, Jordan, who found inspiration in the rich tapestry of science fiction literature. Jordan was captivated by the genre’s ability to stretch the imagination, to envision worlds where technology-enhanced human experience in deeply personal ways. It was this emotional resonance, the thrill of exploration, and the personal connection to the narrative that Jordan sought to bring into the gaming world.

The founder acts as a lossy filter — compressing, distorting, sometimes removing the warnings embedded in the original texts.

But as the seasons changed, so too did the narrative. The problem wasn’t with what AgriAI was doing; it was with what it wasn’t. In pursuit of efficiency, AgriAI had recommended the most water-efficient crop possible: almonds. Almonds everywhere. What initially seemed like a masterstroke in sustainability soon revealed a comedic twist. Local ecosystems began to suffer. Bees, tired from pollinating endless almond orchards, went on strike. Bird populations, confused by the lack of variety in their diet, started emigrating en masse, seeking culinary diversity elsewhere.

Neither automatically establishes that the proposition captures our intention, the tests cover every important situation, or the outcome is one we should want.

Another often overlooked aspect of logistics is address verification. Incorrect or incomplete addresses are more than just a minor inconvenience—they result in failed deliveries that waste time, increase costs, and elevate carbon emissions due to repeated delivery attempts. Here, AI provides a sophisticated solution. By employing machine learning techniques and generative AI approaches, our GeoEngine© system at Quincus can validate and correct addresses before packages ever hit the road. This seemingly simple step is a cornerstone of efficient logistics, ensuring that goods are delivered on the first attempt.

Stochasticity is a wonderful engine for creativity, but a terrible companion when you need to return to a known state.

But what if we could decouple speed from ecological impact? I coach my team so that our work begins with a premise: Sustainable logistics isn’t merely about reducing emissions but reimagining the pathways through which goods travel across the globe.

In both cases, a robust internal model helps decide when to stay local and optimize incrementally, and when to leap to a new representation or strategy.

The question is how much understanding remains recoverable, by whom, and at what cost.

But an answer can also reveal territory we did not know existed.

An AI mathematician must search across representation spaces, not just within them.

I would call it reverse mathematical analysis: begin with the result and investigate the new ways of thinking it makes possible.

But the human underneath the suit is no longer being replenished.

The machine's speed can create more occasions to ask, test, explain and revise—provided we organize the work that way.

Think of AI as a junior team member—learning, assisting, and growing with feedback.

“Co-author” describes that intellectual partnership here, not a transfer of human accountability to a machine.

Dive deeper into personalized education, where AI transcends the role of a mere tool for dispensing information, becoming a mentor that understands each student’s unique learning style, pace, and challenges. Picture a learning platform that curates content not just based on academic requirements but on what inspires and engages the student, turning education from a chore into a journey of discovery. Such AI doesn’t just impart knowledge; it ignites curiosity and fosters a love for learning that lasts a lifetime.

A university should be part of a network through which knowledge can travel: across institutions, countries, generations and disciplines.

Distinguishing between vision-market fit and product-market fit is crucial for success in B2B environments. Startups must focus on data market fit to ensure their products deliver on their promises. By emphasizing feasibility, viability, and iterative development, startups can navigate the complexities of B2B markets and build successful, ML-driven products.

Users who have mastered Photoshop expect a high degree of control over their work.

The question is how much understanding remains recoverable, by whom, and at what cost.

At the heart of this exploration is a paradox that defines our digital existence: the personalization algorithms, designed to tailor content to our individual tastes and preferences, can inadvertently confine us within echo chambers. These digital silos, reinforced by algorithmic feedback loops, narrow our exposure to a diversity of ideas, experiences, and perspectives. The consequence is a digital landscape where serendipity and the intellectual growth born from encountering the unexpected are endangered species.

In that world, humans don’t write code. We define intent. We set constraints. We conduct.

Let the machine optimize the encoding. We still need a shared Rosetta Stone for the parts of the work through which we direct, challenge and learn.

Imagination makes the guess. Proof earns the right to trust it.

There is an important difference between not understanding how a machine generated a proof and not being able to establish whether the proof is valid.

There’s a playful yet insightful examination of how delegating mundane tasks to AI necessitates a radical overhaul of how we train and develop human talent within our organizations.

If AI performs some tasks through which juniors used to learn, the answer is not to prohibit the tasks from being automated. It is to design the next apprenticeship.