Light and Shade: Creativity at AI's Crossroads

Light and Shade: exploring creativity's AI conundrum

AI keeps moving. Creative work is shifting with it. Light and Shade looks at the tension points creatives feel every day: the upside of new capability, and the downside of lost control.

Back in early 2022, an ECD used a basic AI model to sketch people's "dream careers" on Twitter. It felt like a playful side project, a quirky endpoint. Two years later, AI sits inside the tools we use daily. It helps some, threatens others, and leaves many unsure what comes next.

What's changed for creatives

• Access: skills that once took years now sit behind prompts and sliders.
• Pressure: clients expect more variations, faster, for less.
• Control: questions grow about credit, consent, data, and where this all leads.

The five issues at the center

• Jobs: The ladder for juniors looks broken.
• Ownership: Copyright, credit, and consent collide with training data.
• Environment: AI's true footprint is hard to pin down.
• Productivity: Speed promises often turn into new admin loops.
• Homogenisation: Default prompts, default outcomes, generic work.

Jobs: if the first rung is gone, build new ones

Entry-level roles are thinning. In their place: unpaid gigs, cold DMs, and portfolio lottery. That "empty void" is real, but it's not immovable.

• Pair AI with craft: show the thinking, process, and taste behind outputs.
• Make micro-apprenticeships: short paid trials with clear deliverables and feedback.
• Publish playbooks: document systems, prompts, and decisions for transparent growth.
• Mentors: trade one-off portfolio reviews for 4-6 week sprints tied to live work.

Ownership: credit, consent, and contracts

Training data, copyright, and attribution are colliding. Legal norms are catching up, and your agreements should too.

• Add "no model training" clauses to contracts and NDAs.
• Record provenance: keep prompt logs, source references, and model versions.
• Check guidance from the U.S. Copyright Office on AI-generated works.
• Agree on credit: define who gets named for prompts, curation, and final edit.

Environment: make energy a design constraint

The footprint varies by model, workflow, and scale. Treat cost and carbon like any other spec.

• Prefer smaller models and batch runs for exploration.
• Cache results, reuse assets, and reduce needless rerenders.
• Ask vendors for energy and water data; compare options via the IEA's data center insights.

Productivity: the paradox of "faster"

AI speeds up drafts, then adds rounds of "one more option." More choices, more revisions, more scope creep. The fix is intent and constraints.

• Set a decision cap: agree on the number of iterations before you start.
• Timebox exploration: 90 minutes to find a direction, then switch to craft.
• Charge for expanded scope: new prompts and styles equal new budget.
• Use AI for breadth early; save polish for human judgment late.

Homogenisation: avoid the great sameness

Defaults produce default work. If your inputs look like everyone else's, your outputs will too.

• Feed your own source sets: moodboards from field photos, scans, physical textures.
• Write style constraints: "No glossy gradients. Emulate risograph misregistration."
• Force divergence: generate with three different models, then cross-breed elements.
• Blend analog steps: print, distress, rescan, and reimport to break the sheen.

How this series was built

Light and Shade is grounded in interviews with founders, technologists, researchers, educators