What AI actually looks like in 2027, minus the hype

Forecasting AI is a good way to look silly in eighteen months. So here are predictions with reasoning attached, so you can at least see where we went wrong.

Agents stop being demos and start being boring

The current agent experience is: impressive for four steps, then it books the wrong flight. What changes is not intelligence, it is scaffolding — checkpoints, verification steps, and the humility to stop and ask.

By 2027 the successful agent products will be narrow and unglamorous: reconcile these invoices, triage this inbox, keep this dataset clean. Not "digital employee." The demos will be less exciting and the software will be much more used.

Small models eat an enormous share of actual usage

Frontier models get the headlines; small models get the volume. Most real tasks — classification, extraction, summarisation, routing — do not need the biggest model available, and running something small and local is cheaper, faster and private.

The practical consequence: more AI will run on your own device, and you will not know or care which model answered.

Verification becomes a product category

Right now the burden of checking output is entirely on you. That is untenable at scale, and it is where the next real product wedge is: tools whose whole job is telling you which parts of a generated output are load-bearing and which are guesses. Citations, confidence signalling, and diffs against source material — sold as features, not afterthoughts.

The price war nobody is pricing in

Inference costs have fallen relentlessly and there is no sign of a floor. That reshapes what is buildable: features that are economically absurd today (run the model over every document, every night, speculatively) become routine. Assume the cost of a model call trends toward the cost of a database query, and build accordingly.

Context windows stop being the headline

The million-token race is close to done as a marketing lever, because capacity was never the bottleneck — attention across capacity was. Expect the conversation to shift to retrieval quality and memory that persists usefully between sessions, which is a much harder and much more valuable problem.

What does not change

• Hallucination does not get solved. It gets managed, mitigated, and better-signposted. Anyone promising elimination is selling.
• The bottleneck stays human. Knowing what to ask for and recognising a good answer remains the scarce skill.
• Consolidation continues. Fewer foundation labs, more application companies on top of them.

What this means for you, practically

If you are building: bet on things that get cheaper, not on things that require the model to stop making mistakes. Design for verification from day one — it will be a selling point, not overhead.

If you are choosing tools: do not over-commit. The gap between the leading platforms keeps closing and reopening every few months. Stay portable, keep your prompts and data in formats you can move, and re-evaluate twice a year rather than picking a religion. Our platform deep-dives are kept current for exactly that reason.

If you are just trying to keep up: ignore benchmark announcements almost entirely. Watch what changes in the products you personally use. That signal is slower, quieter and far more accurate.

The clock keeps ticking. The hype cycle does not have to set your watch.