Tag: innovation

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AI History Work

Flash-Frozen Cognition: Birdseye, AI, and the Future of Work

I was listening recently to a conversation between Liz Thomas, Tom Lee, and Michael Lewis — the kind of wide-ranging dialogue where a single offhand story can suddenly anchor everything that’s been swirling loosely in your mind.

Tom’s story was about the 1930s, the weight of the Great Depression, and a man named Clarence Birdseye.

Birdseye had watched the Inuit fish in the brutal cold of Labrador and noticed something the rest of the world had missed: fish frozen instantly at sub-zero temperatures tasted perfectly fresh when thawed. The ice crystals formed too quickly to rupture the cellular walls of the flesh. He took that observation home, patented the process, and introduced the world to flash freezing.

On the surface, he had simply figured out a better way to keep peas green and fish edible. What he had actually done was detonate a quiet economic bomb.

Before Birdseye, entire ecosystems of seasonal labor existed to preserve, salt, can, and rush perishable goods to market before they rotted. When flash freezing arrived, those jobs didn’t evolve — they vanished. The ice harvesters, the seasonal canners, the local preservationists all felt the sudden, biting frost of obsolescence. The cold came fast, and it was indifferent.

Yet zoom out on the timeline, and a different picture emerges entirely. Flash freezing didn’t just kill jobs — it invented new ones that nobody could have anticipated. It necessitated refrigerated trucking. It transformed the grocery store, conjuring the frozen food aisle from nothing. It reshaped the home appliance industry, making the household freezer a fixture of modern life. Most profoundly, it decoupled humanity from the harsh dictates of the harvest season, democratizing access to nutrition across geographies and income levels that had never known that kind of abundance.

The destruction was visible and immediate. The creation was invisible and slow — and vastly larger.

Listening to Tom tell this story, I couldn’t help but see our own reflection in it.

Right now, we are all hyper-focused on the ice harvesters of the cognitive economy. We look at AI — large language models, generative tools, automated reasoning — and we see the rupture. We mourn the entry-level analyst, the copywriter, the junior coder. The anxiety is real. The displacement is real. The cold is real.

But what we are struggling to visualize is the refrigerated trucking of the mind.

“AI is flash-freezing cognition. It is taking tasks that used to rot if not attended to immediately by expensive, time-consuming human effort, and preserving them in a scalable, frictionless state.”

When intelligence and execution can be flash-frozen and shipped anywhere instantly — to a first-generation entrepreneur in rural India, to a solo founder with no budget for consultants, to a teacher in a school that can’t afford specialists — what new aisles get built in the supermarket of human endeavor?

The honest answer is that we don’t know. The Inuit fishermen of Labrador couldn’t have imagined the frozen pizza aisle. The ice harvesters of the 1930s couldn’t have pictured the cold chain logistics industry that employs millions today. We are standing in their moment, watching the ice form, mourning the harvest — and almost certainly underestimating what comes next.

The true impact of AI won’t be measured in the jobs it automates. It will be measured in the industries, creative liberties, and human possibilities that emerge because we no longer have to spend all our energy just keeping the ideas from spoiling.

Questions to Consider

  1. The Invisible Creation: Flash freezing’s job creation vastly outpaced its job destruction — but only over decades. How long are we willing to hold that faith with AI, and what do we owe the people displaced in the interim?
  2. The Democratization Dividend: Birdseye’s invention ultimately made fresh nutrition available to people who never had it. Who are the equivalent beneficiaries of flash-frozen cognition — and are we building the infrastructure to actually reach them?
  3. The Harvest Season Question: We’ve always structured education, careers, and institutions around the assumption that expertise is scarce and slow to develop. What breaks — and what gets liberated — when that assumption stops being true?
  4. The Indifference Problem: The cold that killed the ice harvesters’ livelihoods was indifferent to their suffering. Is there anything about AI disruption that is meaningfully different from previous waves of technological displacement — or are we simply the latest generation to stand in that frost?

Categories
AI

The Ghost of Edison in the AI Data Center

For over a century, the story of modern electricity has been framed by the “War of the Currents.” Thomas Edison championed Direct Current (DC)—a stable, continuous flow of energy—while Nikola Tesla and George Westinghouse backed Alternating Current (AC), which could be easily stepped up in voltage to travel long distances across the grid.

Tesla won. AC became the lifeblood of the global power grid. But history has a funny way of looping back on itself. Today, as we stand on the precipice of the largest infrastructure build-out in human history—the artificial intelligence data center—Edison’s DC power is making a quiet, monumental comeback.

The catalyst? The sheer, unyielding physics of energy consumption.

The AI boom, driven by massive GPU clusters from companies like NVIDIA, is extraordinarily power-hungry. We are no longer measuring data center power in megawatts; we are measuring it in gigawatts. And when you are dealing with power at that scale, the friction of legacy architecture becomes a multi-billion-dollar bottleneck.

On X Ben Bajarin cited a recent conference discussion by an executive from power management supplier Eaton that highlighted a massive architectural shift happening right now behind the scenes:

“800-volt DC to the rack is probably one of the biggest architectural changes that are starting to be designed into data centers, and a lot of those designs are taking place right now. You know, honestly, when look at Eaton, I think that’s one of the untold stories here, is that DC power is probably one of the biggest transformational things that are going to hit the electrical industry since, quite frankly, AC electricity was around in the Edison days.”

To understand why this is revolutionary, you have to look at how a traditional data center gets its power. Power arrives from the utility grid as medium-voltage AC. It is then stepped down to low-voltage AC, sent to the server floor, converted into DC, stepped down again, and finally fed into the server rack at 54 volts.

Every time power is converted from AC to DC, or stepped down through a transformer, there is a penalty. It generates heat, and it loses energy.

“We estimate that there’s roughly about 5% electrical loss during that transition. If you could just go from DC, directly from the utility feed, all the way through the data center into the rack, that’s 5% efficiency gain that you could get.”

In the abstract, 5% sounds like a rounding error. But scale changes everything. Eaton projects that the upcoming data center build-out to support AI will require somewhere between 50 and 100 gigawatts of power.

“So on 50 gigawatts or 100 gigawatts of power generation that’s needed, that’s 5 gigawatts of power that all of a sudden just appears from the existing infrastructure. And that is really, that is really exciting.”

Five gigawatts is not a rounding error. Five gigawatts is the equivalent output of five standard nuclear reactors. It is enough energy to power millions of homes. And in this new 800-volt DC architecture, those five gigawatts aren’t created by burning more coal, building more solar panels, or splitting more atoms.

They are created purely by the removal of friction. By subtracting the unnecessary steps.

There is a profound philosophical metaphor hidden in this electrical engineering triumph. In our own lives, and in our organizations, we are obsessed with generation. When we face a deficit—a lack of time, a lack of output, a lack of revenue—our default instinct is to generate more. We try to work longer hours, hire more people, or drink more coffee.

But how much of our daily energy is lost to “conversion friction”? How much mental power evaporates when we constantly context-switch between tasks, essentially converting our mental state from AC to DC and back again? How much organizational momentum is lost translating an idea through five different layers of middle management before it reaches the “rack” where the actual work is done?

Often, the most elegant and impactful solution isn’t to generate more power. It is to look at the existing architecture of your life or business, identify the transition points that are bleeding energy as heat, and rewire the system to flow directly to the source.

The invisible architecture that shapes our digital lives is shifting. In the race to build the future of artificial intelligence, the biggest breakthrough wasn’t a new way to create energy, but a century-old method of preserving it.

Categories
AI Farming History

The Harvest and the Algorithm: What 1990s Farms Teach Us About AI

There’s a strange kind of wisdom hiding in dusty old books about agriculture.

When you’re caught in the middle of a technological revolution—and with AI, there’s no question that we are—it’s tempting to keep your eyes fixed on the horizon. But sometimes the most clarifying thing you can do is look back.

Tracy Alloway at Bloomberg recently pointed to something genuinely instructive from the past: Richard Critchfield’s 1990 book, Trees, Why Do You Wait? America’s Changing Rural Culture, which traced the collapse of the family farm as industrial agriculture swept through the Midwest.

The broad strokes are familiar. As machinery got more expensive and efficiency became everything, scale won. The 80-acre husband-and-wife operation got swallowed by the 2,000-acre neighbor with access to capital. It wasn’t complicated. It was just gravity.

But here’s the part that should make your ears prick up.

The Seed That Was Supposed to Save Everyone

In the late 1980s, agricultural biotechnology arrived with a very specific promise. The idea was almost elegant: if you could bake the magic directly into the seed, you wouldn’t need all that expensive machinery, all those sprawling acres, all that fertilizer. The playing field would tilt back toward the small farmer.

Critchfield quoted an Office of Technology Assessment report from 1986 that captured the mood of the moment:

“The Office of Technology Assessment in 1986 forecast that biotechnology in crops would be more quickly adopted by richer farmers… Others argue that the more that gets built into the seed itself, the more it means higher yields at lower cost… If it reduced farm income, it could work to the smaller farmer’s advantage. As it is with all new technology, it is hard to foresee the consequences.”

You can feel the cautious optimism in that language. Hard to foresee the consequences. The understatement of a century.

What Actually Happened

The biotech did raise yields. Nobody disputes that. What it didn’t do was leave the gains in the hands of the people doing the actual farming.

Thanks to intellectual property law, patent protections, and a level of corporate consolidation that would have seemed cartoonish if you’d predicted it in advance, the value flowed straight upstream. We didn’t get “more in the seed, less paid for inputs.” We got more in the seed, and vastly more paid for proprietary inputs. The tech giants of agriculture captured the surplus. The farmers got the risk.

Now Listen to How We Talk About AI

We are told AI will democratize expertise. That a one-person startup will be able to code like a ten-person engineering team. That a small business will generate world-class marketing copy. That this is, finally, the great leveler.

Sound familiar?

Alloway’s analysis lands hard precisely because it forces the uncomfortable question: who will actually capture this value? The ownership structure of AI looks eerily similar to the agricultural biotech boom—proprietary models, walled-off training data, and a handful of enormous tech companies positioned to act as tollbooths between everyone else and their own productivity gains.

She’s right to note that “the ultimate distribution of benefits isn’t determined by technology alone. Policy also plays a role.” That sentence is doing a lot of quiet work.

If the agricultural analogy holds, productivity gains from AI won’t naturally flow to the individual worker or the small business owner. Without a robust open-source ecosystem or some deliberate policy intervention, those gains will be captured by whoever controls the compute and the models.

Where the Analogy Might Break Down

Here’s where I think there’s room for genuine optimism—not naive optimism, but structurally grounded optimism.

You cannot open-source arable land. Reverse-engineering a patented biological seed is genuinely hard, legally risky, and practically difficult. Code and model weights are different. They’re infinitely replicable. The marginal cost of distribution is essentially zero.

The battle between closed, proprietary AI and open-source models is still very much live. That’s not nothing. AI is fundamentally more commoditizable than a physical farm, and the history of software suggests that open ecosystems have a real shot when the community is motivated enough to build them.

Who Owns the Harvest?

Technology can reshape daily workflows in months. Power structures take decades to budge, if they budge at all. The mistake would be assuming the former automatically changes the latter.

The question worth sitting with isn’t what can AI do—that list gets longer every week. The question is who decides how the productivity it unlocks gets distributed. That’s not an algorithm problem. It’s a political and economic one.

If we want the AI revolution to be a rising tide rather than another tractor paving over the family farm, we have to look past the technology itself. We have to decide, deliberately, who owns the harvest.

Questions to Ponder

On history and pattern recognition: The agricultural biotech optimists weren’t stupid—they were looking at the technology and making reasonable inferences. What does that tell us about the limits of predicting who benefits from a new technology by studying the technology itself?

On open source as a counterweight: The open-source AI movement (Llama, Mistral, DeepSeek) is often framed as a technical story. Should we be thinking about it primarily as a political economy story—a structural check on proprietary capture?

On the role of policy: Antitrust law, data ownership rights, compute access regulation—which levers, if any, seem realistic? And who has the incentive to pull them?

On the worker vs. the firm: If AI raises individual productivity, does the gain show up in wages, prices, profits, or somewhere else? What would need to be true for workers to actually keep a meaningful share?

On commoditization speed: Software and model weights can be replicated freely—but does speed matter? If proprietary models establish deep lock-in before open alternatives mature, does the theoretical commoditizability even help?

Inspired by Tracy Alloway’s analysis at Bloomberg and Richard Critchfield’s Trees, Why Do You Wait? (1990)

Categories
AI India

Intelligence as a Public Good: India’s “AI ka UPI” Revolution

There is a recurring rhythm to human progress: a breakthrough is born as a luxury, matures into a commodity, and ultimately solidifies into infrastructure.

We saw it with electricity, we saw it with the internet, and in 2016, we saw India do it with money through the Unified Payments Interface (UPI). UPI took the friction out of digital finance, transforming it from a walled garden guarded by private banks into a digital public good.

Now, it appears India is attempting to do for intelligence what they did for payments.

The global narrative around Artificial Intelligence is currently dominated at one end by massive private moats. At the other end are various open source/open weight efforts.

Silicon Valley primarily approaches AI as a capital-intensive arms race. Trillion-dollar tech players ramp huge compute, train very large models, and rent out intelligence via by the drink APIs. This intelligence is a proprietary and monetized luxury.

Enter the “AI ka UPI” initiative and the IndiaAI Mission discussed by Ashwini Vaishnaw at this week’s India AI Impact Summit.

Instead of treating AI as a product to be sold, India is architecting it as a Digital Public Infrastructure (DPI). The government is doing the heavy lifting—subsidizing the compute, curating population-scale datasets, and building foundational models.

Currently, they are making over 38,000 GPUs available to startups and researchers at around ₹65 (less than a dollar) an hour, a sheer fraction of the global cost. They are rolling out sovereign stacks like BharatGen and conversational models fluent in 22 regional languages.

“They are building an ‘orchestration layer’ for cognition.”

If a developer wants to build a voice-agent to help a rural farmer diagnose a crop disease, they don’t have to worry about the backend compute, the dataset acquisition, or paying a premium to a tech giant. They just plug into the public rails.

As I watch this unfold, I am struck by the philosophical shift it represents. We have become deeply conditioned to view AI through the lens of scarcity and subscription. But what happens when intelligence becomes a public utility?

It shifts the center of gravity of innovation. It becomes about who can solve the most acute, localized, human problems. The friction of creation drops to near zero. A bootstrapped team in a tier-two city can suddenly wield the same computational reasoning as a VC funded Silicon Valley startup.

There is also an element of sovereignty here. In the 21st century, relying on foreign infrastructure for your population’s cognitive processing seems akin to relying on a foreign nation for your electricity. True technological independence requires sovereign AI—models trained on indigenous data, reflecting local culture, nuances, and values, rather than the implicit biases of others.

The implications could be staggering. We are moving from an era where AI is an elite tool to an era where it is the invisible, ubiquitous fabric of daily life for over a billion people.

The true measure of AI’s ultimate impact won’t be found in benchmark scores on a server farm. It will be found in the quiet dignity of a citizen accessing global markets through a vernacular voice assistant, or a rural clinic predicting patient outcomes with public compute.

I look forward to following India’s AI efforts as this and other AI initiatives are more clearly defined.

Questions to consider

1. The Value of Human Capital: If artificial intelligence becomes as ubiquitous, reliable, and cheap as public electricity, what uniquely human skills will become the new premium in a hyper-automated society?

2. Cognitive Sovereignty: How will the geopolitical landscape shift when emerging economies no longer need to import their “cognitive infrastructure” and inherent cultural biases from Western tech players?

3. The Centralization of Truth: When a government builds and curates the foundational AI models for over a billion people, where is the line between providing a democratized public good and engineering a centralized cultural narrative?

What else???

Categories
AI India

The Polyglot Machine

There is a subtle but profound shift happening in the global architecture of artificial intelligence. For the past few years, the gravitational pull of the AI revolution has been overwhelmingly centralized—anchored in the server farms and venture capital boardrooms of Silicon Valley. But if you look closely at the horizon, the center of gravity is beginning to disperse.

Activity in India’s AI ecosystem is accelerating (witness this week’s India AI Impact Summit in Delhi), and it feels less like a replication of what we’ve seen in the West and more like an entirely new paradigm.

Take Sarvam AI, for example. What strikes me about their approach isn’t just the technical ambition of building foundation models, but the philosophical underpinning of why they are building them. They are focusing heavily on Indic languages. This is not a trivial detail; it is the crux of the matter.

“We often forget that language is the original operating system of human culture. It shapes how we think, how we empathize, and how we conceptualize reality.”

When the foundational models of artificial intelligence are trained overwhelmingly on English, they inadvertently inherit a distinctly Western worldview. They learn the biases, the idioms, and the cultural frameworks of a specific slice of humanity, leaving the rest of the world to interact with technology through a translation layer that often strips away nuance.

India, a nation woven together by dozens of distinct languages and thousands of dialects, presents the ultimate crucible for AI. What happens when a machine doesn’t just translate, but actually “thinks” and generates natively in Hindi, Tamil, or Bengali?

The rise of AI in India represents a push for digital and cultural sovereignty. It is a recognition that the future of technology cannot be a monolith. For AI to truly serve humanity, it must reflect the pluralism of humanity. It must understand the local context, the regional slang, and the deeply rooted cultural histories that define how people live and work.

Watching companies like Sarvam AI pick up momentum reminds me that the next great frontier in technology isn’t just about achieving higher parameters or faster compute times. It’s about representation. The models that will truly change the world won’t just be the smartest; they will be the most deeply attuned to the beautiful, noisy, and diverse chorus of the human experience.

Categories
SpaceX

A Starlink Light Show?

Earlier this morning Elon Musk commented that it’d be cool to have a “Starlink light show” for America’s 250th birthday celebration later this year.

Intrigued, I asked Grok how this might be possible? Here’s what Grok replied:

A Starlink light show, as casually suggested by Elon Musk in a recent reply on X (where a friend proposed doing one “one of these days” because it “would look cool”), would be a coordinated visual spectacle using the thousands of Starlink satellites already in low Earth orbit. Here’s how it could realistically work, based on current satellite tech, physics, and what’s been observed/discussed.

Current “Natural” Light Shows from Starlink

Starlink satellites already create impressive visual effects unintentionally:

  • Reflected sunlight — They don’t have onboard lights; what people see as bright moving dots or “trains” is sunlight glinting off their flat solar panels and metallic surfaces. Right after launch, when satellites are still clustered and low in orbit, they form a striking linear “train” of pearls across the sky (often mistaken for UFOs). Over time, as they spread out, individual ones can flare brightly when their panels catch the sun at just the right angle toward observers on the ground.
  • Flares and flashes — Astronauts like Don Pettit have captured time-exposure photos showing dozens of Starlink satellites flashing like shooting stars near orbital dawn/dusk, when sunlight hits them optimally while the ground below is dark.

These are passive reflections, not controlled emissions.

How a Deliberate, Controlled Light Show Could Be Engineered

To turn this into something intentional and artistic (e.g., patterns, shapes, messages, or synced effects for an event like the US 250th birthday in 2026), SpaceX would need to add active control. Several feasible methods exist or could be adapted:

  1. Orientation/Attitude Control for Directed Reflections
    Starlink satellites already have precise thrusters and reaction wheels to adjust orientation for communication and orbit maintenance. By tilting solar panels or the satellite body in sync, they could “aim” reflections toward specific ground areas at precise times.
  • Coordinated across many satellites, this could create moving patterns, shapes, or even simple images (like drawing with points of light).
  • Limitations: It only works when the sun is illuminating the satellites (twilight hours), and visibility depends on clear skies and viewer location. Not truly “on-demand” at midnight.
  1. Onboard LEDs or Controllable Lights
    Adding low-power LED arrays (visible from Earth) is technically possible, though current Starlink v satellites don’t have them. Power draw would be minimal compared to their solar/battery setup, and they’d only need to activate briefly for a show.
  • This would allow night-time visibility without relying on sunlight.
  • Precedents exist in other satellites (e.g., some experimental ones with flashing beacons), but it’d require new hardware on future generations.
  1. Using Existing Inter-Satellite Lasers (the “Space Lasers”)
    Starlink satellites are equipped with optical inter-satellite links (lasers) for high-speed data transfer between satellites (up to 200 Gbps per link, with thousands active). These are infrared lasers, invisible to the human eye.
  • Not directly usable for a visible show — They’re not pointed at Earth for this purpose, and IR isn’t visible.
  • A fun speculative twist: Future upgrades could include visible-wavelength lasers or diffusers, but that’s not current capability. (Some discussions joke about “space lasers” for displays, but it’s mostly the reflection method that’s practical.)
  1. Long-Exposure or Camera-Based Effects
    One creative workaround mentioned in discussions: Have people point cameras/phone apps upward for a set time while satellites flash reflections in patterns. Combined with apps syncing exposure, it could form large-scale images (e.g., flags, logos) visible only in photos/videos, not to the naked eye. This scales with the constellation’s size (over 7,000+ satellites now, heading toward tens of thousands).

Practical Challenges and Realism

  • Scale needed — At any moment, only ~50–100 Starlink satellites might be visible overhead in one spot (due to orbital geometry). A continent-wide show would require precise timing across the fleet and ideal viewing conditions.
  • Light pollution concerns — Astronomers already criticize Starlink for unintended brightness; a deliberate show could amplify backlash unless mitigated (e.g., SpaceX has added anti-reflective coatings to newer versions).
  • Feasibility today vs. future — Musk’s comment was lighthearted (“one of these days”), likely nodding to future versions with more control. Current satellites could do basic coordinated flares/reflections with software updates, but a full “spectacular” show might need hardware tweaks on next-gen birds.

In short, the simplest and most likely version would leverage sunlight reflections with synchronized satellite tilts—turning the night sky into a moving canvas of twinkling points. It’d be epic for a patriotic event, but more “string of controlled flares” than a laser-light extravaganza like a fireworks show. If SpaceX ever does it, expect announcements and tracking apps to tell you exactly when/where to look!

Quite fascinating!

Categories
AI IBM

From Picnic to Workforce: The New Scaling

In 1977, Charles and Ray Eames released a short film for IBM called Powers of Ten.

The film opens with a couple picnicking on a blanket in Chicago and zooms out—every ten seconds, the field of view increases by a factor of ten.

We move from the intimacy of a lakeside lunch to the edge of the observable universe, then plunge back down through the skin of a hand into the subatomic architecture of a carbon atom.

The subtitle was “A Film Dealing with the Relative Size of Things and the Effect of Adding a Zero.”

It was a meditation on scale, suggesting that as we add zeros to our perspective, the very nature of what we are looking at transforms.

Today, with AI, we are living through a new kind of “Powers of Ten” journey, but the zeros aren’t being added to meters; they are being added to tokens.

I recently read a reflection by Azeem Azhar where he chronicled his shift from using 1,000 AI tokens a day to nearly 100 million. In the Eames’ film, adding a zero moved you from a park bench to a city, then to a continent. In the world of Large Language Models, adding a zero moves the AI from a novelty to a tool, then to a collaborator, and eventually—at the scale of 100 million—to something resembling a “workforce.”

“At 100,000 [tokens], a collaborator. At 1 million, I was building workflows. At 10 million, processes. At nearly 100 million – something closer to a workforce.”

This shift is more than just “more of the same.” It is a phase change.

When the Eames’ camera zoomed out to $10^{24}$ meters, the Earth didn’t just look smaller; it disappeared into a texture of galaxies.

When we scale our interaction with intelligence by several orders of magnitude, the “picnic” of human cognition—the way we think, draft, and create—is no longer the center of the frame.

At the 100-million-token-day scale, we aren’t just “using” AI. We are orchestrating vast, invisible ecosystems of thought. We are seeing companies like Spotify where top developers reportedly haven’t written a line of code in months, instead directing systems that ship features while the humans review the output from their phones.

We have added so many zeros that the “relative size” of human effort has changed.

The chilling yet beautiful thing about Powers of Ten was the realization of our own insignificance in the face of the cosmos, balanced by the intricate complexity found within our own cells.

As we zoom out into the “Token-Verse,” we face a similar existential pivot. If an AI can process a hundred million tokens of “thought” in a day—a volume no human could read in a lifetime—what does it mean to be the “author” of our lives?

The answer, I suspect, lies back on the picnic blanket.

The Eameses knew that while the scale of the universe is staggering, the meaning is found in the connection between the two people on the grass.

As we add zeros to our digital capabilities, our value shifts from the production of tokens to the intention behind them.

We are no longer the builders of the cathedral; we are the ones deciding why the cathedral needs to exist at all.

We are moving from the era of the “Worker” to the era of the “Architect” or maybe just the “Witness.”

Categories
AI Programming Prompt Engineering Software Work

The Great Inversion

For twenty years, the “Developer Experience” was a war against distraction. We treated the engineer’s focus like a fragile glass sculpture. The goal was simple: maximize the number of minutes a human spent with their fingers on a keyboard.

But as Michael Bloch (@michaelxbloch) recently pointed out, that playbook is officially obsolete.

Bloch shared a story of a startup that reached a breaking point. With the introduction of Claude Code, their old way of working broke. They realized that when the machine can write code faster than a human can think it, the bottleneck is no longer “typing speed.” The bottleneck is clarity of intent.

They called a war room and emerged with a radical new rule: No coding before 10 AM.

From Peer Programming to Peer Prompting

In the old world, this would be heresy. In the new world, it is the only way to survive. The morning is for what Bloch describes as the “Peer Prompt.” Engineers sit together, not to debug, but to define the objective function.

“Agents, not engineers, now do the work. Engineers make sure the agents can do the work well.” — Michael Bloch

Agent-First Engineering Playbook

What Bloch witnessed is the clearest version of the future of engineering. Here is the core of that “Agent-First” philosophy:

  • Agents Are the Primary User: Every system and naming convention is designed for an AI agent as the primary consumer.
  • Code is Context: We optimize for agent comprehensibility. Code itself is the documentation.
  • Data is the Interface: Clean data artifacts allow agents to compose systems without being told how.
  • Maximize Utilization: The most expensive thing in the system is an agent sitting idle while it waits for a human.

Spec the Outcome, Not the Process

When you shift to an agent-led workflow, you stop writing implementation plans and start writing objective functions.

“Review the output, not the code. Don’t read every line an agent writes. Test code against the objective. If it passes, ship it.” — Michael Bloch

The Six-Month Horizon

Six months from now, there will be two kinds of engineering teams: ones that rebuilt how they work from first principles, and ones still trying to make agents fit into their old playbook.

If you haven’t had your version of the Michael Bloch “war room” yet, have the meeting. Throw out the playbook. Write the new one.

Categories
AI Work

Why IBM is Hiring Beginners

There is a pervasive anxiety humming beneath the surface of the modern workplace—a quiet, collective fear that the bottom rungs of the corporate ladder are being systematically sawed off by artificial intelligence.

The common wisdom, echoed in countless op-eds and boardroom whisperings, is that entry-level jobs are the natural prey of the Large Language Model.

The tasks of summarizing, drafting, formatting, and basic coding are easily consumed by algorithms. If a machine can execute the rote labor of a junior analyst in three seconds, why hire the junior analyst at all?

It is a seductive, mathematically appealing logic, especially in an era of tightening belts and efficiency mandates.

Consequently, we are witnessing a landscape where many tech companies are quietly, or sometimes loudly, slashing their junior roles to lean on AI.

But amidst this trend, an alternative approach emerges that feels almost rebellious in its long-term optimism.

IBM, a legacy titan that has weathered every technological revolution of the past century and where I started my career, is leaning entirely the other way. Rather than cutting, they are reportedly tripling their entry-level hiring.

Reflecting on this strategy, IBM’s chief HR officer noted:

“The companies three to five years from now that are going to be the most successful are those companies that doubled down on entry-level hiring in this environment.”

This perspective is profound because it challenges the very premise of what an entry-level employee actually is.

The prevailing, perhaps cynical, view treats a junior worker merely as a unit of basic output. If you view a beginner only as a spreadsheet compiler or a draft-writer, then yes, they appear redundant in the face of AI.

But what if we view the entry-level role not as a terminal function, but as an apprenticeship?

When we hire a beginner, we aren’t just buying their immediate, unpolished labor. We are investing in a trajectory.

We are bringing them into the fold so they can absorb the tacit knowledge of the organization—the unwritten rules, the cultural nuances, the complex, human art of navigating institutional friction.

An AI cannot learn the subtle interpersonal dynamics of a specific team, nor can it develop the intuition that comes from failing, recovering, and being mentored by a seasoned veteran.

If we automate away the entry-level, we effectively destroy the incubator for our future mid-level and senior leaders. Where will the experienced managers of 2030 come from if no one is allowed to be a beginner in 2026? You cannot suddenly parachute someone into a senior role and expect them to possess the deep, intuitive judgment that is only forged in the crucible of early-career trial and error.

The institutional memory breaks down.

IBM’s strategy recognizes a crucial reality: AI shouldn’t replace the beginner; it should accelerate them.

Imagine a junior employee who isn’t bogged down by mindless grunt work, but instead is handed the tools to instantly bypass the mundane. They can spend their foundational years analyzing, questioning, and engaging in higher-order problem-solving alongside their mentors. They transition from data-gatherers to hyper-learners.

By doubling down on human potential in an age of artificial intelligence, companies are making a strategic bet on the one asset that cannot be replicated by a server farm: the evolving, adapting, and deeply creative human mind.

The most successful organizations of the near future won’t be the ones with the fewest employees and the most algorithms; they will be the ones that used algorithms to cultivate the most formidable, deeply experienced human talent.

The ladder hasn’t been dismantled. It has merely been redesigned.

The only question is whether we have the foresight to keep inviting people to climb it.

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AI AI: Large Language Models

The Architecture of Unpredictability

There is a special understanding that comes from looking too closely at a map of a massive network or a large city. There is a point where the individual components vanish, and something else—something “other”—takes over.

Niall Ferguson captures this beautifully in The Square and the Tower:

“Large networks are complex systems which have ‘emergent properties’ – the tendency of novel structures, patterns and properties to manifest themselves in ‘phase transitions’ that are far from predictable.”

We like to believe we are the architects of our systems. We build platforms, we codify laws, and we design cities with the intent of order.

But Ferguson points out that once a network crosses a certain threshold of complexity, it enters a state of “phase transition.” It’s like water reaching 100°C; it doesn’t just get “hotter”—it becomes steam. It changes its fundamental nature.

We see this most vividly today in the trajectory of Artificial Intelligence. An LLM is, at its core, a gargantuan network of weights and probabilities. We understand the math of the individual neuron, yet we cannot fully explain how, at a certain scale, these systems begin to exhibit reasoning, humor, or theory of mind. These are not explicitly programmed “features”; they are emergent properties—the ghost that moves into the machine once the network becomes sufficiently dense.

Dario Amodei, CEO of Anthropic, describes this phenomenon through the lens of scaling:

“The thing that is so surprising about these models is that as you scale them up, they just keep getting better at things you didn’t explicitly train them to do… there’s this sense in which the model is ‘learning’ the structure of the world just by being forced to predict the next word.”

This is the “emergent property.” It is the intelligence of the beehive that no single bee possesses. It is the sudden, viral revolution that no single activist could have ignited. These properties are far from predictable because they don’t live in the nodes of the network; they live in the relationships between them.

The philosophical weight of this is humbling. It suggests that our world is governed by a structural momentum that defies linear logic.

When we find ourselves in these moments of societal or personal transition, perhaps the goal isn’t to control the outcome, but to understand the new physics of the system we’ve helped create.

We aren’t just parts of the network; we are the medium through which the unpredictable manifests.

Questions to Ponder

  • If your own consciousness is an emergent property of your neural network, where does “you” actually reside?
  • In the social networks we inhabit daily, what properties are emerging that we haven’t yet named?
  • As AI continues its phase transition, are we creating a tool, or are we witnessing the birth of a new kind of physics?