The essay “Your brain does not process information and it is not a computer” by Robert Epstein argues that the dominant information‑processing (IP) metaphor for human cognition is a misleading and ultimately futile analogy. Epstein begins by observing that, despite intensive research, scientists will never discover a literal copy of Beethoven’s Fifth Symphony, words, pictures, or any other environmental stimulus stored in the brain. He stresses that while the brain is certainly not empty, it does not contain the kinds of discrete data structures—memories, representations, algorithms, or symbolic registers—that characterize digital computers.

He contrasts the newborn’s innate capacities (reflexes, basic perceptual biases, and powerful learning mechanisms) with the absence of any pre‑installed ‘software’, ‘data’, or ‘hardware‑like’ components that would allow it to operate as an information processor. The argument proceeds to a brief tutorial on how computers truly work: information is encoded as bits, organized into bytes, stored in physical memory, retrieved, copied, and transformed according to explicit programs. Human cognition, by contrast, lacks such encoding, storage, and retrieval mechanisms. The brain does not hold symbolic representations of a dollar bill, a poem, or a melody that can be fetched from a memory register; instead, experience changes the brain’s structure in a way that enables future performance without the need for “retrieval”.

Epstein traces the historical lineage of metaphors for intelligence over the past two millennia: clay‑infused spirits, hydraulic humours, mechanical automata, electrical/chemical analogies, and finally the computer metaphor that emerged after the 1940s. Each metaphor reflected the most advanced technology of its era, but all were eventually superseded. He points out that the modern IP view—the idea that the brain processes symbols like a computer—originated with early cognitive scientists such as George Miller, who applied information theory to the mind, and was cemented by works like John von Neumann’s The Computer and the Brain (1958). Since then, billions of dollars and thousands of researchers have pursued a framework that assumes the brain is an information processor, producing a massive literature that seldom questions its basic premise.

To illustrate the inadequacy of the IP model, Epstein describes a classroom exercise where a student draws a dollar bill first from memory and then with the bill present. The memory‑based drawing is poor, despite the student having seen the bill countless times. This demonstrates that the brain does not store a precise visual “representation” that can be retrieved; rather, exposure to the bill altered the brain’s dynamics, making the student better able to reproduce it when the stimulus is present. He argues that memory is not a retrieval of stored data but a re‑enactment of prior experience, and that even the notion of “memory stored in individual neurons” is untenable—functional neuroimaging shows distributed, often massive, networks engaged during recall.

Epstein then outlines an alternative, “anti‑representational” or embodied cognition perspective. Experience shapes the brain in orderly ways, allowing us to perform tasks (sing a song, recite a poem, catch a baseball) without invoking internal symbolic models. The baseball example from McBeath et al. (1995) shows that a player catches a fly ball by maintaining a simple optical relationship with the ball rather than calculating trajectories via internal representations. This view aligns with scholars such as Anthony Chemero, who reject computational accounts and emphasize direct organism‑world interaction.

The essay warns that clinging to the IP metaphor not only misguides scientific research but also fuels speculative futurist claims—e.g., Ray Kurzweil, Stephen Hawking, and Randal Koene’s predictions of mind uploading and digital immortality. Since no “software” or memory banks exist in the brain, such scenarios are fundamentally impossible. Moreover, the unique, history‑dependent changes each brain undergoes mean that even identical experiences produce distinct neural configurations. This “uniqueness problem”, illustrated by Frederic Bartlett’s work on memory distortion, underscores the impossibility of a universal brain‑computer mapping.

Epstein highlights the practical consequences of the metaphor’s dominance: massive projects like the EU’s Human Brain Project, which promised a full‑brain simulation by 2023, have floundered, exposing how the IP assumption can lead to unrealistic expectations and waste of resources. He concludes by urging a shift away from the entrenched computational metaphor toward a more faithful understanding of the brain as a dynamic, embodied system that changes through interaction with its environment. The call to “hit the DELETE key” is a metaphorical plea to discard the outdated information‑processing view and to pursue neuroscience free of its intellectual baggage.

Overall, the essay challenges the foundational assumptions of contemporary cognitive neuroscience, argues for an embodied, anti‑representational framework, and cautions against the hype surrounding brain‑computer convergence.

Over the past decade, the software engineering industry experienced an unprecedented period of prosperity and privilege, characterized by lavish perks, generous compensation, and near-guaranteed job security. This era was fundamentally driven by an economic environment of near-zero interest rates that allowed companies to operate with unlimited access to cheap capital. During this time, profitability was secondary to growth metrics, user acquisition, and company valuation, leading organizations to compete aggressively for engineering talent through extensive benefits and high salaries.

The landscape has dramatically shifted since 2023, with interest rates rising to approximately 5%, fundamentally altering corporate incentives. Companies now prioritize profitability and efficiency over unchecked growth, leading to a wave of layoffs and a cultural transformation in how engineers are valued and treated. What was once considered essential—open-source contributions, developer experience initiatives, and experimental projects—is now being defunded in favor of focused execution on core business priorities.

This transformation represents more than just economic adjustment; it's a fundamental realignment between company interests and individual engineer interests. The previous decade created an illusion where companies appeared to share engineers' values and priorities, but this was largely a mirage created by abundant capital and the need to attract talent. The current reality is that companies are now driven by executive leadership's specific strategic objectives rather than broad engineering enthusiasm.

Engineers face difficult choices in this new environment. Those who continue pursuing projects or values misaligned with company priorities risk being perceived as ineffective or unreliable, making them vulnerable to layoffs. The shift requires engineers to either adapt to the new reality or accept potential career consequences. While this transition has been painful and represents a loss of privilege, it also brings a certain clarity and authenticity to the profession. The relationship between engineers and companies has returned to a more straightforward dynamic: value creation leads to rewards, while lack of value leads to consequences.

The fundamental truth emerging from this shift is that software engineering, like any profession, operates on clear economic principles. Success now depends on understanding and aligning with company objectives rather than expecting companies to accommodate individual preferences. This realignment, while challenging, may ultimately lead to a more sustainable and realistic industry where both companies and engineers operate with clearer expectations and mutual understanding.

The IndieWeb is a people-focused alternative to the “corporate web”.

The AT Protocol (atproto) is an Authenticated Transfer Protocol where user data is signed, enabling broadcast across services.

A Personal Data Server (PDS) hosts user data repos and signing keys, assigning handles and DIDs.

An AppView is an application in the Atmosphere, aggregating data from repos via PDSes.

A Relay aggregates data repos from PDSes, providing a stream of change events for AppViews.

Lexicon is a schema language for describing data records and APIs, similar to JSON-Schema and OpenAPI.

A data repo is a public dataset representing a user, containing JSON records and blobs, identified by a DID.

DIDs (Decentralized Identifiers) are universally unique identifiers for data repos, supporting did:web and did:plc.

the happy path: on human agency and AI interfaces

eric zelikman