2026 Is AI’s “Show Me the Money” Year

Advanced | January 10, 2026

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The New AI Question: “Cool Demo… But Does It Make Money?”

2025 was the year of breathtaking AI demos. But 2026 is shaping up to be the year bosses and investors ask a tougher question: what’s the return? In an Axios roundup of predictions, experts said the AI “model maker race” will keep speeding up—but companies will face growing pressure to prove AI return on investment instead of just showing off better benchmarks. (Axios)

Why “Being the Best Model” Isn’t Enough

Axios argues that success will hinge less on having the “best” model and more on timing and execution—knowing when the tech is mature enough to deploy and how to integrate it into real organizations without burning money (or credibility). Box CEO Aaron Levie told Axios that the race is constant, while Harvey’s Winston Weinberg joked that good AI shouldn’t need long prompts—if you have to explain everything, the product isn’t truly smart yet. (Axios)

Agents Are Coming… But 2026 Could Be Messy

A lot of people believe the next leap isn’t chatbots—it’s agents, meaning AI tools that can actually do tasks across apps and systems. OpenAI’s Fidji Simo told Axios that soon “answering questions” will be the least useful thing AI can do, because assistants will become proactive and handle more work for us. But not everyone expects a smooth rollout: Slack’s Ryan Gavin predicted “the year of the lonely agent,” where companies launch tons of agents that mostly sit idle—like unused software licenses. (Axios)

The Real Problem: AI Has to Plug Into “Deterministic” Systems

Here’s the business reality: companies don’t just want creativity—they want reliability. Axios reports that Square’s Willem Avé expects firms to connect agents to deterministic systems (systems that behave predictably) to reduce the “randomness” that can come from AI outputs. In plain English: businesses want AI that doesn’t hallucinate in a spreadsheet or freestyle in a compliance report. (Axios)

AI Return on Investment Meets a Cost Reality

The pressure to show AI return on investment is happening at the same time that AI infrastructure is getting expensive. A Reuters analysis warns investors are underpricing a major risk for 2026: AI-driven inflation, fueled by massive data center spending and rising costs for energy and advanced chips. Reuters notes that hyperscalers like Microsoft, Meta, and Alphabet are in a multi-trillion-dollar data center buildout—and strategists warn tighter money (if inflation rises) could hit tech valuations and profits. (Reuters)

What It Means for Busy Professionals

If you’re a manager, the new “AI skill” isn’t knowing every tool—it’s knowing how to pick use cases that pay. RBC Capital Markets predicts enterprise AI adoption will hit an inflection point in 2026: moving beyond pilots into measurable ROI. But RBC also warns that AI can pressure software margins in the near term because inference costs and development costs can rise before the payoff arrives. (RBC Capital Markets)

Vocabulary (10)

Return on investment (ROI) (noun) – the measurable benefit you get compared to what you spend.
Example: “The CFO asked for clear ROI before approving the AI budget.”
Benchmark (noun) – a standard test used to compare performance.
Example: “The model scored well on benchmarks, but customers still wanted real results.”
Deploy (verb) – to roll out and use something in real life (not just testing).
Example: “They deployed the AI tool across the whole customer support team.”
Integrate (verb) – to connect something into an existing system or workflow.
Example: “The challenge is integrating AI into messy, real-world processes.”
Agent (noun) – an AI system that can take actions and complete tasks.
Example: “The company built an agent that drafts emails and schedules meetings.”
Deterministic (adjective) – predictable; produces consistent results.
Example: “They connected the agent to deterministic rules to reduce errors.”
Inference cost (noun) – the cost of running an AI model to get outputs.
Example: “High inference costs can squeeze software margins.”
Hyperscaler (noun) – a giant cloud company that builds huge data centers.
Example: “Hyperscalers are spending billions on AI infrastructure.”
Valuation (noun) – how much investors think a company is worth.
Example: “If rates rise, tech valuations could drop.”
Inflection point (noun) – a turning point where growth or change accelerates.
Example: “RBC says 2026 could be an inflection point for enterprise AI.”

Discussion Questions (About the Article)

Why does Axios say 2026 is the “show me the money” year for AI?
What’s the difference between a great AI demo and a profitable AI product?
Why do businesses want agents connected to deterministic systems?
What does “the year of the lonely agent” mean in a workplace context?
How could inflation or higher interest rates change AI investment plans?

Discussion Questions (About the Topic)

Where do you think AI can create the fastest ROI in your industry?
What jobs or workflows in your company are structured enough for AI today?
What risks worry you most: cost, privacy, mistakes, or job disruption?
How should leaders measure AI success: speed, quality, cost savings, revenue, or something else?
If your boss said “Use AI more,” what would you do first on Monday morning?

Related Idiom or Phrase

“Put your money where your mouth is” – prove you’re serious with real action (or real results), not just talk.

Example: “In 2026, AI teams will have to put their money where their mouth is by showing measurable ROI.”

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Sources (for reference)

This article was inspired by Axios.