Practical & Pragmatic AI Principles for AI Autumn

Originally published on LinkedIn.

What do you need to consider to prepare your organisation for AI Autumn and avoid AI winter? This article is more directly aimed at Chief / Enterprise Architects and adjacent roles.

This is the second part of my reflection on the Royal Society's 75th anniversary of the Turing Test event (2 October 2025). Part 1 covered what happened at the event - the dismantling of AI hype, the evidence of scaling limitations, and the documented harms being ignored in the rush to deploy.

Your Role: The Long-Term Voice of Reason

You're uniquely positioned (as an Architect / Tech Strategist) to influence business strategy, challenge vendor claims, and ensure system resilience. Whilst procurement chases the next shiny thing and leadership responds to market pressure, you're thinking in architectural timescales - five, ten, fifteen years out.

That perspective matters now more than ever. The correction seems likely. Your job is ensuring your organisation protects genuine value when it arrives and that you've built systems on what actually works rather than vendor promises.

  • Demand evidence over enthusiasm

  • Design for reality over brochures

  • Build systems that augment capability without degrading people's capabilities

  • Protect genuine value when the bubble deflates

The scaling wall appears real. The economics look challenging. The question is whether we'll have an autumn (harvesting what works, dropping what doesn't) or a winter (wholesale rejection losing genuine value).

Recap of AI Autumn

AI Autumn is my suggested next phase - where dead leaves (failed experiments) drop, we harvest what works (scale prototypes to production) , and we prepare for what's next (the air coming out the bubble?). For more on this see the last article.

Six Principles for AI Strategy

1. Map Reality, Not Marketing

Current AI excels at content transformation with good data. That's its strength. Systems tend to fail at causal reasoning, long-term planning, novel scenarios, and grounded truthfulness.

  • Match use cases to actual capabilities, not vendor claims

  • If you need robust planning or handling unexpected scenarios, current systems will disappoint you

  • Stop procurement favouring "general purpose" platforms

  • Demand proof in your specific context, not synthetic benchmarks

2. Protect Your Data Assets

Model collapse is documented. Systems trained on AI-generated content progressively deteriorate. The internet is contaminated. Clean, human-generated, domain-specific data becomes increasingly valuable.

  • Don't build strategies assuming infinite model improvement

  • Plan for degradation

  • Treat proprietary data sources as strategic assets

  • Question where training data comes from

3. Invest in Specialisation

AlphaFold revolutionised protein folding. LLM's haven't replicated that success across complex highly regulated domains. Evidence suggests the best results come from highly specialised, modular systems, not jacks-of-all-trades. Consider the full range of AI techniques - symbolic reasoning, expert systems, statistical models, neural networks - rather than over-indexing on generative AI.

  • Stop procurement favouring "one model to rule them all"

  • Invest in domain-specific models where ROI is demonstrable

  • Consider hybrid architectures combining different AI techniques (not just transformers)

  • Match the AI approach to the problem (Gen AI for content transformation, expert systems for rules-based decisions, statistical models for prediction)

  • Challenge vendors claiming general intelligence

4. Challenge AGI Rhetoric

Keeping AGI "always in the future, never now" lets companies evade transparency, ignore current harms, and justify massive investment. But do the economics actually work? Bain projects an $800B revenue shortfall by 2030.

  • Reframe business cases around measurable outcomes

  • Don't accept AGI-adjacent promises

  • Resist inevitability narratives

  • Remember you have agency

5. Build Resilience

Consider second-order effects. Are you building dependency or capability? What happens when AI fails? Will junior staff ever learn to think critically?

  • Design systems that can function when AI fails

  • Consider "chaos anti-AI monkey" days to test resilience

  • Question whether you're augmenting capability or creating atrophy

  • Map workforce skills against AI dependency

6. Make Externalities Visible

AI systems have environmental and social costs that rarely appear in business cases. Training large models consumes vast energy. Deployment at scale affects employment, widens inequality, and can enable surveillance. These aren't abstract future concerns - they're current costs being externalised.

  • Include environmental impact in technology assessments

  • Consider social impact alongside efficiency gains (job displacement, skill degradation, inequality)

  • Question whose costs you're externalising and whether that's acceptable

  • Ask who benefits and who bears the costs

  • Make these trade-offs explicit in decision-making rather than pretending they don't exist

The Workslop Problem

Gary Marcus referenced the term "workslop" for AI-generated content that looks acceptable but lacks substance. One result of this is confirmation fatigue where human review becomes meaningless theatre. We need to watch out for:

  • Automation overload (systems designed for human use overwhelmed by agentic AI)

  • Confirmation fatigue (reviewers rubber-stamping because everything looks plausible)

  • Capability degradation (staff never developing expertise because AI does it)

What to Stop

Brute-force approaches:

  • "Scaling is all you need" - but have GPT-5 and Llama-4 delivered on their hyped expectations?

  • Mass deployment without evidence

  • Procurement favouring general-purpose platforms

AGI theatre:

  • Business cases built on AGI promises

  • Inevitability narratives that remove agency

  • Vendor claims about reasoning or intelligence without proof

Ignoring externalities:

  • Business cases that ignore environmental costs

  • Deployment decisions that externalise social harms

  • Technology assessments that only count benefits, not costs

What to Start

Systematic evidence:

  • Carefully managed testing in real-world like contexts where you'll deploy

  • Demand demonstrable ROI in your domain or experiment to validate claims

  • Build evaluation frameworks that test actual use cases

Strategic investment:

  • Domain-specific tools with proven outcomes

  • Various types of AI matched to the problem (Gen AI for content transformation, expert systems for rules, statistical models for prediction)

  • Protect data assets as model collapse progresses

  • Multimodal integration only where it adds genuine value

  • Don't over-index on Gen AI whilst ignoring other proven techniques

Resilience planning:

  • Maintain ability to function when AI fails

  • Differentiate research from deployment

  • Consider workforce capability alongside system capability

  • Build in human expertise development, not just AI augmentation

Externalities accounting:

  • Include environmental impact in technology decisions

  • Make social costs visible in business cases

  • Question who benefits and who bears the costs

  • Consider whether you're solving problems or shifting them elsewhere

What would you add to this? Keen to hear your thoughts.

Originally published on LinkedIn.