The Next Input — Issue #176

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⚡ The Briefing — 60 sec

• AI Nobel prize-winning discovery could happen within two years, says Anthropic co-founder And the Nobel Prize goes to… Claude?! Jack Clark thinks AI systems could contribute to Nobel-level scientific discoveries within a couple of years, which sounds insane right up until one actually does it.

• OpenAI barrels toward IPO that may happen in September IPO from OpenAI? A lot of people will want in on this one. Once this thing hits public markets the AI conversation gets even more tied to quarterly expectations, growth pressure, and shareholder theatre.

• Australian CEO says machines won’t ‘ping HR’ like workers do CEO is basically like “I mean machines won’t ping HR so…” and honestly that tells you exactly why so many workers are uneasy about this whole thing.

🛠️ The Playbook — The AI Incentive Engine

Mission
Design AI workflows that align incentives between leadership, workers, customers, and the systems themselves before the whole thing turns into extraction theatre.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Better adoption, lower internal resistance, and AI systems that improve work instead of quietly turning every metric into a pressure cooker.

0) Why This Matters

AI is becoming powerful enough to collide directly with incentives.

One side of the conversation is extraordinary upside: Nobel-level discoveries, scientific breakthroughs, and systems capable of accelerating research far beyond normal human pace.

The other side is financialisation: IPO pressure, investor expectations, and a future where AI companies stop behaving like research labs and start behaving like quarterly-report machines.

And then there’s the worker reality. The “machines don’t complain” framing says the quiet part out loud: some organisations see AI primarily as a way to reduce friction from humans, not improve outcomes for humans.

That is where things break.

Because if AI:

• only benefits shareholders

• only increases worker pressure

• only removes bargaining power

…then resistance is inevitable.

The play is:

• align incentives before scaling workflows

• make productivity gains visible and fair

• ensure humans still benefit from the systems they help operate

1) Architecture

2) Workflow

1. Identify workflows where AI changes the relationship between workers, managers, and output.

2. Define what productivity gains are expected and who benefits from them.

3. Add AI assistance or automation carefully instead of dropping it into every task at once.

4. Track worker pressure alongside speed and throughput metrics.

5. Create a feedback loop where staff can identify where AI is helping versus creating stress.

6. Adjust the workflow if gains are only increasing output expectations without improving work quality or conditions.

3) Example Prompts

Incentive Alignment Prompt

You are reviewing an AI-assisted workflow for incentive alignment.

For the workflow below:
- identify who benefits from productivity gains
- identify who absorbs additional pressure
- identify where incentives become misaligned
- suggest one fairer operating model

Workflow:
[insert workflow here]

Worker Impact Prompt

You are assessing the worker impact of an AI rollout.

Check:
- whether workload actually decreases
- whether output expectations quietly increase
- whether humans retain meaningful judgment
- whether the workflow improves or worsens morale

Return 4 bullet points only.

Leadership Reality Check Prompt

You are reviewing an executive AI strategy.

Identify:
- whether the strategy focuses on outcomes or labour reduction
- whether worker concerns are being ignored
- whether the rollout is sustainable
- the top 3 risks if leadership gets this wrong

Scientific Breakthrough Prompt

You are reviewing a frontier AI capability claim.

Assess:
- what the capability could realistically change
- what is likely hype
- what industries may be affected first
- what operators should pay attention to now

Return concise bullet points.

4) Guardrails

• Do not let productivity gains become permanent overload.

• Track morale and pressure alongside output.

• Keep humans involved in judgment-heavy workflows.

• Avoid measuring workers purely against machine-level throughput.

• Ensure AI benefits are visible to teams, not just leadership.

• Separate scientific potential from workplace reality.

5) Pilot Rollout — 3 hours

1. Pick one workflow where AI is expected to improve productivity.

2. Define what success looks like for both leadership and workers.

3. Introduce AI assistance into a narrow part of the process first.

4. Track time saved, workload changes, and worker sentiment.

5. Review whether expectations increased alongside productivity.

6. Adjust the workflow before scaling further.

6) Metrics

• Time saved per workflow

• Worker pressure score

• Human correction rate

• Productivity gain distribution

• Employee sentiment

• Workflow adoption rate

• Retention impact

Pro Tip: If AI only improves life for the spreadsheet, people will eventually notice.

🎯 The Arsenal — Tools & Platforms

• Claude / GPT · increasingly capable systems that may genuinely shift scientific and research workflows · https://www.anthropic.com · https://openai.com

• Airtable · map workflows, incentives, and operational pressure points · https://www.airtable.com

• Google Sheets · lightweight tracking for productivity gains versus workload increases · https://workspace.google.com/products/sheets/

• Retrospective surveys · the fastest way to spot whether AI is helping or quietly poisoning morale

• Governance frameworks · because “machines don’t complain” is not an operating model, it’s a warning sign

Copy-paste prompt block:

You are helping me build an AI Incentive Engine.

For the workflow below:
1. identify who benefits from AI productivity gains
2. identify who absorbs additional pressure
3. identify where incentives become misaligned
4. identify where humans must retain judgment
5. identify the top 5 organisational risks
6. propose a fairer workflow design
7. define success metrics

Workflow:
[insert workflow here]

Return the answer in markdown with sections for:
- Workflow summary
- Incentive map
- Worker impact
- Leadership impact
- Risks
- Fairer workflow design
- Metrics

💡 Free Office Hours

If your organisation is trying to use AI without turning the workplace into a pressure-cooker disguised as innovation, I run free office hours to help design workflows that actually hold up for humans too.

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🕹️ Game Over

The machines may not complain. The humans eventually will.

— Aaron Automating the boring. Amplifying the brilliant.