Many people today type prompts like “argue from the point of view of Shakespeare” or “write this like Shakespeare,” as if a few stylistic flourishes could summon his mind on demand. In most cases, the time would be far better spent simply reading Shakespeare: engaging slowly with his language, structure, and characters instead of asking a system trained on billions of tokens to improvise a pastiche in a few seconds. What these prompts usually produce is a pleasant imitation—Elizabethan seasoning on modern thoughts—rather than an encounter with the uncomfortable depth, ambiguity, and moral seriousness that make Shakespeare worth returning to.
Creating a serious “digital avatar” of Shakespeare would require much more than pouring his collected works into a language model. It would also mean situating those plays and poems in the world that formed him: his sources, his rivals, his collaborators, and his critics—voices that pushed, challenged, and sharpened his own. Shakespeare wrote in a crowded, competitive theatre ecosystem; contemporaries like Christopher Marlowe, Ben Jonson, Thomas Middleton, John Webster, and John Fletcher were experimenting with blank verse, satire, revenge tragedy, and dark moral ambiguity at the same time, and their work clearly influenced the texture and themes of his plays. Even a contemporary like Robert Greene—who dismissed Shakespeare as an “upstart crow”—is part of the context that shaped how we see him and how he responded to his age. To approximate that in a model, you would need not only Shakespeare’s texts, but also these surrounding voices and histories, woven into a careful domain model rather than a flat mash-up.
Precisely because any AI “Shakespeare” is at best a compressed, context-stripped echo, there is enormous value in going back to the plays themselves and learning directly from them. Reading Shakespeare forces us to slow down, puzzle over syntax, inhabit multiple perspectives, and accept that not all questions about love, power, and identity have neat answers. The plays become mirrors: we find ourselves in Hamlet’s hesitation, Macbeth’s ambition, Viola’s disguise—patterns that algorithms can mimic linguistically but cannot live through on our behalf.
Take King Lear, which turns on a shocking lack of self-knowledge and awareness. Lear misreads his daughters, mistakes flattery for love, disowns the only child who truly cares for him, and divides his kingdom on the basis of ego and spectacle rather than truth. His refusal to see himself clearly—his temper, vanity, and dependence on public displays of loyalty—unleashes chaos: civil war, the destruction of his realm, and ultimately his own death and that of Cordelia. The play shows with brutal clarity how a leader’s blindness to their own character can ruin not only their private life, but an entire political order. No generated “Shakespearean-style” monologue can substitute for the experience of walking with Lear through the storm, watching him lose everything, and recognising how dangerous self-deception can be in our own lives and institutions.
Last week, I drew on King Lear to mentor a cohort of 20 leaders from a pharmaceutical company, using the play to explore self-awareness and stakeholder management. I didn’t need to “prompt” my hypotheses—I simply returned to my first experience of seeing the tragedy performed on stage. I was 15 years old. No AI could ever replace or erase that lived encounter.