The Deep Feed

01 — Not Boring

Riding the Leopard: The Search for Meaning in a Post-Scarcity World

Why technical abundance creates a spiritual void

By Packy McCormick · 8 min read

Editor's note: As AI removes the friction of production, the primary human problem shifts from survival to significance.

The numbers coming out of the AI sector are staggering. Sierra's $15 billion raise, Anthropic's $44 billion run rate, and OpenAI's massive capital deployments suggest a world where intelligence is becoming a cheap, abundant commodity. For decades, the human struggle was defined by scarcity—the need to produce, to earn, and to secure resources. But we are approaching a threshold where the machines handle the heavy lifting of cognition and production. This raises a question that is less about economics and more about existence: once we no longer need to struggle to survive, what are we actually here to do?

The Scarcity Paradox

There is a strange inverse relationship between material wealth and spiritual stability. Viktor Frankl, writing from the horrors of a concentration camp, noted that as the struggle for survival subsides, a new question emerges: survival for what? We see this today in high-income societies where the means to live are plentiful, yet the sense of purpose is vanishing. A recent analysis of over 200 science fiction novels reveals a pattern: in post-scarcity futures, the central conflict is almost never about resources. It is about meaning. Fifty-nine per cent of these stories focus on the search for purpose, while identity follows at a distant seventeen per cent. We are building a world that solves for calories and code, but leaves the soul hungry.

The thing we’ll be left solving for is meaning.

This is the 'leopard' we are riding. Technology is a fast, dangerous animal that provides immense power but offers no direction. We have the tools to build anything, yet we lack a consensus on why we should build it. The danger is not that machines will become sentient and kill us, but that they will become so efficient at doing our work that we forget how to define our own value. When the cost of intelligence drops to near zero, the value of human intent becomes the only remaining premium.

The Post-Scarcity Shift

Shift from resource scarcity to meaning scarcity
The transition from 'how to produce' to 'why to produce'
The rising importance of human intent over technical execution

To navigate this, we must stop viewing technology as a way to escape work and start viewing it as a way to refine it. If machines handle the repetitive, the mundane, and the purely analytical, humans are pushed into the realm of the creative, the relational, and the philosophical. This is not a retreat from reality, but a move into the hardest part of being human. We are being forced to decide what matters when nothing is hard to get.

Key Takeaway

When technology solves for scarcity, the only remaining economic and personal currency is meaning.

02 — Experimental History

The Rule of Care

Why regulations fail when motivations are absent

By Adam Mastroianni · 7 min read

Editor's note: Rules are useless if the people following them don't actually value the truth they are meant to protect.

The Soviet Union's 1936 constitution was, on paper, remarkably progressive. It promised freedom of speech, assembly, and rights for all citizens. Yet, within years, the state was conducting massive purges and sending millions to gulags. The laws existed, but they were ignored. This historical reality points to a fundamental truth: rules have no power unless the people operating within the system believe they matter. You cannot legislate integrity into a person who has no interest in being honest.

The Failure of Scientific Rigour

We see this exact failure in the modern scientific community. For a decade, the 'replication crisis' has plagued research. The standard response has been to demand more rules: mandate preregistration, require larger sample sizes, and demand public data. The logic is that if we tighten the regulations, the science will become more reliable. However, these rules are frequently bypassed. In one instance, a paper designed to prove that 'rigour-enhancing practices' work was itself retracted because the authors failed to follow those very practices. They cherry-picked results and ignored their own protocols.

You can’t turn a cheat into a scientist by making a rule against cheating.

The data shows a systemic disregard for transparency. Only 45% of clinical trials post their results publicly. When researchers are asked for their data, only 17% actually provide it. The problem isn't a lack of guidelines; it's a lack of motivation. If a researcher's goal is to secure funding or prestige rather than to uncover truth, they will treat regulations as obstacles to be navigated rather than guardrails to be respected. They will find ways to 'p-hack' or manipulate variables to reach a desired conclusion, regardless of what the rules say.

Why Regulations Fail

Rules lack physical force without social consensus
Incentives often reward the outcome rather than the process
Compliance is treated as a checkbox rather than a commitment

This extends to our personal lives. Couples often try to solve relationship issues by creating 'rules'—like a mandatory daily check-in. But a rule-based interaction feels hollow. If a partner asks about your day only because a 'relationship handbook' requires it, the gesture loses its value. What people actually want is not compliance, but care. We don't want our partners to follow the rules; we want them to value our perspective enough to act without being told.

Key Takeaway

Integrity is a matter of character and motivation, not a matter of compliance with rules.

03 — Stratechery

The Deployment Era

How AI is returning to the mainframe model of enterprise

By Stratechery · 6 min read

Editor's note: The AI revolution is moving from 'chatting with bots' to 'replacing entire business processes'.

OpenAI and Google are no longer just selling models; they are selling armies of engineers. OpenAI has launched a 'Deployment Company' to help enterprises embed AI into their core operations, while Google is hiring hundreds of 'forward deployed engineers' to ensure their Cloud customers actually use their AI products. This marks a shift in the industry. The era of the 'cool demo' is ending, and the era of deep, structural implementation is beginning. These companies aren't interested in helping your employees write better emails; they are interested in re-engineering how your company functions.

Not Copilots, but Replacements

Much of the current hype focuses on 'augmentation'—the idea that AI will act as a co-pilot for human workers. This is a polite way of framing the technology, but it misses the economic reality. The real parallel for AI in the enterprise is not the modern SaaS model, but the mainframe wave of the 1970s. In that era, computing didn't just help people work; it replaced entire categories of labor. Accounting and ERP software didn't just assist bookkeepers; they automated the functions that bookkeepers performed, allowing companies to scale without increasing headcount.

Agents aren’t copilots; they are replacements.

We are seeing a new philosophy emerge: the pursuit of the bottom line through structural replacement. Private equity firms are already looking at ways to buy software companies, implement AI to handle the core workloads, and then conduct significant layoffs. This isn't about making employees more productive; it's about making the company more efficient by removing the need for those employees entirely. The 'deployment' specialists being sent into corporations are there to identify these points of friction and replace them with scalable, automated agents.

The Three Philosophies of Tech

Consumer resonance (making tools people love)
Employee augmentation (making workers faster)
Enterprise bottom-line improvement (replacing processes)

This shift will be driven from the top down. Unlike the internet revolution, which required individual users to change their habits, the AI deployment wave is being orchestrated by executives. They don't need the rank-and-file to embrace the technology; they only need to decide to take the plunge. The result will be a fundamental rethinking of business processes that hasn't occurred since the introduction of the mainframe.

Key Takeaway

The true goal of enterprise AI is not to help humans work, but to allow companies to function without them.

04 — The Marginalian

The Alchemy of Despair

Audre Lorde and the necessity of feeling the pain

By Maria Popova · 5 min read

Editor's note: To find strength, one must first refuse to look away from their own suffering.

Albert Camus once wrote that there is no love of life without despair of life. This is not a contradiction, but a requirement. In the autumn of 1978, the poet and activist Audre Lorde was confronted with a terminal diagnosis. She found herself in a state of 'molten despair,' facing the existential shock of her own mortality. For many, the instinct is to resist this feeling—to numb it, to ignore it, or to pretend it isn't there. But Lorde discovered that resistance only leads to internal destruction.

Through, Not Around

Lorde's philosophy was one of direct engagement. She realized that if she could look at her life and her death without flinching, there would be nothing left to fear. She argued that despair must be allowed to flow through a person. To resist it is to ensure it 'detonates' inside, shattering the self and damaging everyone around them. By accepting the pain, she was able to channel it into her work, transforming a period of physical decline into a period of immense creative and political power.

I do not have to win in order to know my dreams are valid, I only have to believe in a process of which I am a part.

This approach redefines the purpose of work. For Lorde, work was not merely a way to achieve a goal or a status; it was a lifeline. It was the mechanism through which she processed her losses and maintained her connection to the world. Her work became a way of recognizing the existence of love even in the face of death. In this sense, work is not an escape from despair, but a way of giving voice and name to the struggle against it.

Lorde's Framework for Resilience

Acknowledge the despair rather than resisting it
Use work as a tool for processing and connection
Recognize yourself as part of a larger continuum of struggle

Ultimately, Lorde teaches us that battling despair does not mean ignoring the darkness of the world. It means teaching, surviving, and fighting with the most important resource available: oneself. It is about finding joy in the battle itself, rather than waiting for a victory that may never come. It is the recognition that our individual lives, our loves, and our works are part of a much larger, ongoing effort to reclaim power and meaning.

Key Takeaway

Despair is not an obstacle to be avoided, but a force to be channeled into purposeful action.

05 — Simon Willison

The Unethical Guide to AI Survival

The dark comedy of corporate automation

By Mo Bitar · 3 min read

Editor's note: A satirical look at how to navigate the anxiety of the automation age.

There is a growing, palpable anxiety in the modern workplace: the fear that one's role is being 'automated out of existence.' While most career advice focuses on upskilling and staying relevant, a new, darkly comedic strategy is emerging in the social media consciousness. It is a strategy of performative automation—using the language of the very technology that threatens you to secure your own position.

The 'Ralph Loop' Strategy

The concept, as articulated by Mo Bitar, involves weaponising the buzzwords of the C-suite. If a CEO hears the word 'automation,' they don't hear a threat to the workforce; they hear a promise of efficiency. The strategy suggests that instead of resisting automation, one should aggressively advocate for it—specifically through vague, high-sounding terms like 'Ralph Looping.' The goal is to create an aura of technical mastery that is impossible to verify, thereby securing promotions and equity before the reality of the technology catches up.

Nothing arouses the slumbering capitalists than the mention of automation.

This satire highlights a deeper truth about the current corporate climate. The speed of AI development has created a gap between what leadership understands and what is actually possible. In this gap, people can thrive by performing competence. It is a cynical response to a cynical environment, where the ability to speak the language of 'efficiency' is often more valuable than the ability to actually perform the work being automated.

Tactics of Performative Automation

Constant mention of automation in all meetings
Using specific, invented jargon to sound advanced
Publicly 'tagging' colleagues as successfully automated

While clearly a joke, the 'unethical guide' reflects the absurdity of the current moment. We are in a period where the tools are moving faster than the people who manage them, creating a landscape where performance often takes precedence over substance. It is a survival mechanism for a world that is increasingly obsessed with the appearance of progress at the expense of actual stability.

Key Takeaway

In an era of rapid technological change, the ability to speak the language of efficiency can be as powerful as the efficiency itself.

06 — Simon Willison

The Data Integrity Gap

What happens when the tools for exploration fail

By Simon Willison · 2 min read

Editor's note: A technical look at the bugs and race conditions that haunt data exploration.

In the world of data science, the tools used to explore and publish information must be as reliable as the data itself. The recent release of Datasette 1.0a29 highlights the constant battle against the invisible errors that plague complex software. Even in a tool designed for clarity, bugs can emerge that obscure the very information the user is trying to find, such as table headers disappearing when a table is empty.

The Ghost in the Machine

One of the most difficult challenges in software development is the 'race condition'—a bug that only appears when multiple processes happen in a specific, unpredictable order. In the latest version of Datasette, a particularly 'gnarly' bug was discovered where a database connection would close while a query was still executing in another thread. This type of error is notoriously difficult to replicate because it is non-deterministic; it doesn't happen every time, only when the timing is exactly wrong.

The bug was gnarly.

Solving such issues often requires a level of technological assistance that mirrors the very problems we discuss in the enterprise. In this case, the developer used a high-level AI (Codex CLI with GPT-5.5) to generate a minimal Docker environment that could reliably recreate the bug. This is a perfect example of the new workflow: using advanced models not to replace the engineer, but to handle the tedious, highly specific task of debugging the engineer's own work.

Key Updates in Datasette 1.0a29

Improved visibility for empty tables
Mobile Safari bug fixes for column actions
Resolution of critical race condition segfaults

As we move toward a world of automated data analysis, the reliability of these foundational tools becomes even more critical. If the tools we use to explore the truth are themselves prone to silent, race-condition-driven errors, the entire structure of data-driven decision-making begins to wobble. The work of fixing these 'gnarly' bugs is the unglamorous but essential foundation of the digital age.

Key Takeaway

Reliable data exploration requires constant vigilance against the subtle, non-deterministic errors of complex software.