AI Doesn’t Have a Model Problem
It Has a Product Problem. Why Products Are Falling Behind Models?
In the fast-evolving world of artificial intelligence, models are advancing at breakneck speed. Capabilities leap forward every few weeks, but products? Not so much.
On August 14, 2025, Madhu Guru, Product Leader for Gemini at Google, put it cleanly: “AI has a product problem. Not a model problem. Models are making capability leaps every few weeks but AI-native product innovation hasn’t kept up. Most products are forcing AI into existing UX patterns rather than rethinking an AI-native experience from first principles.” (X (formerly Twitter))
This article lays out the evidence, the why, the historical echo, and a concrete example of what an AI-first product actually looks like in practice.
1) The facts: models are racing, products are walking
Organizational AI use hit an inflection in 2024: 78% of surveyed organizations reported using AI, up from 55% in 2023. That same dataset shows use of generative AI in at least one function jumped from 33% to 71% in a year. (Stanford HAI)
Inference got radically cheaper: the cost to query a model performing at GPT-3.5 level fell from about $20 per million tokens in Nov 2022 to $0.07 by Oct 2024, a 280x drop. At the hardware level, costs declined roughly 30% per year while energy efficiency improved about 40% per year. (Stanford HAI)
Adoption is not the same as impact: S&P Global finds the share of companies abandoning most AI initiatives before production jumped from 17% to 42% year over year. (S&P Global, CIO Dive)
Consumer behavior underscores the gap: nearly 2 billion people use AI, yet direct consumer spend sits near 12 billion dollars. That is a large usage base with thin wallets. (Menlo Ventures)
Net: capabilities and cost curves are screaming ahead, yet a large share of projects still stall before they matter to users.
2) Why the gap exists
Probabilistic UX is hard. People do not trust opaque systems that sometimes fail. Trust requires visible provenance, confidence, and override controls, not disclaimers.
Org sequencing is backwards. Many teams start with model selection and only later look for jobs to be done, when it should be the reverse.
Evidence on productivity is real but bounded. Controlled studies show large gains on defined tasks, not a blanket “10x.” GitHub Copilot experiments show roughly 55% faster task completion on coding challenges. A large field experiment with BCG consultants found 12% more tasks completed, 25% faster, with higher quality on suitable tasks. (arXiv, Harvard Business School)
3) A useful historical parallel
In early mobile, teams crammed websites onto small screens. The breakthrough came when apps fused sensors, real time logistics, and payments to solve a new job end to end. AI is at a similar hinge: the win will come from fusing context ingestion, prediction, action primitives, and trust surfaces into a new interaction model, not from sprinkling autocomplete into old menus.
4) AI-first products that already show the pattern
ChatGPT — conversation as the primary interface for knowledge work.
Midjourney — text to image as a native creative medium.
NotebookLM — grounded research workflow across your sources.
Cursor — coding environment rewritten around in-editor agents.
Each of these is impossible or painfully inefficient without AI as the core engine, not as an add-on.
5) A concrete AI-first wedge: FlowSync
Who it serves
Remote managers of 5 to 20 people using Slack and Google Workspace who lose hours in scattered signals.
Job to be done
Identify, explain, and act on the 3 to 10 work signals that matter each day across chat, mail, docs, and tasks, in under 3 minutes per hour.
What makes it AI-first
Continuous context scanning of Slack, Gmail, Calendar, Docs.
Signal detection for urgency, dependency risk, and sentiment shift.
Predictive escalation that warns before deadlines slip.
Conversational control: “What will derail Friday’s release?” yields evidence-linked answers and one-tap actions.
Why it is not just another inbox
Provenance by default: every alert shows sources, features used, and a confidence score.
Counterfactuals: when confidence is low, FlowSync shows what it would do and asks for confirmation.
Action primitives: schedule, assign, draft, or escalate, always with human approval.
Guardrails: least-privilege scopes, on-device or tenant-contained embeddings where possible, red-teamed prompts for hallucinated escalations.
Differentiation vs incumbents
Microsoft 365 Copilot: strong inside Microsoft tools, weaker across mixed Slack-Google stacks. FlowSync is cross-suite by design.
Slack AI: great summaries inside Slack, limited cross-tool causal linking. FlowSync fuses chat, mail, calendar, docs into one risk graph.
Superhuman triage: fast email, but not multi-channel signals and predictive escalation.
What FlowSync must prove in a 6-week beta
Triage time reduced 30% per week for target users.
Precision of “urgent” flags at 0.85 or higher, false positives under 0.15.
Missed-deadline rate down 20% in instrumented projects.
At least 5 high-confidence actions per user per day with user approval.
Unit economics from day one
Small model first, large model only when confidence drops.
Budget caps per user per day tied to alert volume.
Cost per analyzed message and per accepted action tracked in product analytics.
6) Closing the gap: what builders need to do now
Start from pains, not models. Write the single job to be done, then prove it with time-to-value and error costs.
Design for probabilistic behavior. Expose why the system acted, how sure it is, and how to correct it.
Exploit AI’s unique strengths. Real time context, generative action sequences, and adaptive workflows are the point.
Measure with falsifiable targets. Pre-register precision, recall, and time saved. Publish holdout results.
Build for inclusion. UNCTAD’s 2025 guidance is unambiguous: people-centered AI requires skills, data access, and accountability so the benefits do not concentrate in a narrow slice of firms or regions. Design your product so it is understandable, controllable, and accessible. (UN Trade and Development (UNCTAD))
7) Appendix: sources worth anchoring to
Stanford AI Index 2025 for adoption, cost collapse, and hardware trends. (Stanford HAI)
S&P Global on project abandonment rates. (S&P Global, CIO Dive)
Menlo Ventures on consumer AI spend. (Menlo Ventures)
Controlled productivity studies for task-level gains. (arXiv, Harvard Business School)
What this means, finally?
If 2023 and 2024 were about capability leaps and cost collapse, 2025 should be about building products that make probabilistic systems trustworthy and useful for specific jobs.
The winners will not bolt AI onto old workflows.
They will ship AI-native experiences that show their work, earn trust, and deliver measurable outcomes.