The Invisible Cost of Going “AI-First”: Why Your Company Might Be All Hype and No Substance
When companies declare they’re AI-first, it often sounds like a bold, visionary move. But according to Siqi Chen, co-founder and CEO of Runway, there’s a growing risk that many organizations are becoming AI-first in name only — performing innovation, without much real AI happening on the ground. (Venturebeat)
Here’s what’s really going on — and how companies can avoid becoming poster children for AI-washing.
The Illusion of Innovation
Chen argues that real change rarely comes from top-down mandates or PowerPoint decks. Instead, meaningful innovation often begins with people tinkering quietly in the margins:
- Someone stays late to write a simple script that automates a manual process.
- A developer uses a large language model (LLM) to fix a bug at 2 a.m.
- A workflow changes because one person shared a useful AI trick on Slack — and others followed.
These grassroots moments are powerful because they grow organically. But when leadership swoops in and mandates “AI adoption by Q3,” the magic of that curiosity-driven experimentation often falls apart. (Venturebeat)
The “Great Reversal”: From Curiosity to Compliance
According to Chen, the shift is subtle but damaging:
- A competitor announces flashy AI features — say, onboarding automation or support bots that claim “40% efficiency gains.” (Venturebeat)
- Your CEO calls an emergency meeting. Suddenly, every team is pressured to come up with an “AI initiative.” (Venturebeat)
- What once felt like experimentation becomes a performance: “We need something that looks like AI by Friday.” (Venturebeat)
This kind of top-down pressure can backfire. It turns innovation into a checkbox exercise — building AI for the appearance of innovation, not the substance.
Two Types of Leaders — and Their Impact
Chen identifies two contrasting leadership styles:
- The Curious Leader: Builds in public. Shares prototypes (even buggy ones), talks openly about failures, invites feedback. These leaders model learning — and encourage a culture of experimentation. (Venturebeat)
- The Directive Leader: Issues AI mandates via Slack. Pressures compliance. Creates resentment instead of inspiration. (Venturebeat)
The former builds real momentum. The latter builds theater.
Where AI Actually Works — and Where It Doesn’t (Yet)
Chen calls out a few places where AI adoption often delivers genuine value:
- Customer support: LLMs can handle Tier-1 tickets, interpret intent, suggest replies, and escalate complexity. (Venturebeat)
- Code assistance: At odd hours, AI can feel like a tireless teammate helping you write or debug — saving hours over time. (Venturebeat)
But for more ambitious promises — like “fully automated forecasting” or “AI-driven ops teams” — the gap between hype and reality often shows when pilot projects begin. (Venturebeat)
How to Build Real AI Momentum
Chen recommends three practices to ground AI efforts in genuine value:
- Model what you mean Leaders should prototype publicly. It’s less about polished presentations and more about showing real experiments — warts and all. (Venturebeat)
- Listen to the edges The people quietly experimenting (your curious developers, power users) often have the most valuable insights. Trust them. (Venturebeat)
- Create permission, not pressure Instead of commanding every team to “do AI,” foster a safe space for experimentation — let people try, fail, learn, and iterate. (Venturebeat)
Why This Matters
In six months, the external signals may be flashy — board decks full of AI dashboards, shiny contracts, and hires with “AI” in their titles. But Chen warns: real transformation happens in the quiet corners, through small experiments that compound over time. (Venturebeat)
The companies that will truly win in the AI era are not necessarily the ones who announce AI-first fastest. They’re the ones who stay curious, build patiently, and let real usage emerge organically.
Glossary
- AI-first: A business strategy where AI is treated as central to a company’s operating model, not just an add-on. (Forbes)
- AI washing: The practice of overstating or misrepresenting how much a company actually uses AI, often for marketing or strategic optics. (en.wikipedia.org)
- Large Language Model (LLM): A type of AI — like GPT — trained on massive text datasets, used for generating text, summarization, code assistance, and more.
Conclusion
Going “AI-first” doesn’t guarantee real innovation. Without a culture that supports experimentation — and leaders who walk the talk — AI initiatives risk becoming superficial, performative, and disconnected from actual value. The companies that stand out will be those that stay curious, test quietly, and nurture real adoption over time.
Source: VentureBeat — How to avoid becoming an “AI-first” company with zero real AI usage (Venturebeat)
