By John Sviokla
The biggest unrecognized challenge we face as business leaders in the age of AI is our concept of time. Silicon intelligence lives in nano-seconds and transfers knowledge instantaneously and universally available to any access point. New hybrid organizations that symbiotically mesh silicon and human intelligence operate on what I’m calling AI Time. I conjecture that OpenAI is such an organization because how else could they release a four-front strategy on a global scale to hundreds of millions of daily users. No one in the long history of technology has pulled off innovation at this scale and speed. In a single launch window, OpenAI:
- Open-sourced capable small models for anyone to run and adapt.
- Advanced its top frontier model to new performance levels.
- Delivered to both consumer and enterprise audiences at massive scale.
- Offered its product to the federal government for $1/year for the first year.
The result: a portfolio approach that reshapes the competitive landscape and gives leaders—from Fortune 100 CEOs to startup founders—an important set of tools for using AI across every layer of the business. Paul Baier’s recent Forbes Column has written about some of the implications for AI and business leaders. In this short piece I make the case for rethinking your organization and your concept of time.
What GPT-5 Brings to the Table
The headline improvements are clear: better reasoning, faster responses, fewer hallucinations, stronger coding, and new multimodal capabilities. Reviewers like Wired called GPT-5 OpenAI’s “most advanced” model yet, while The Verge emphasized its “significant technological leap” and more reliable output.
Two changes stand out for everyday use:
- Auto-routing: the system automatically picks the right model for the task, removing the need for users to manually select engines.
- Safe completions: instead of blanket refusals, GPT-5 aims to provide helpful, bounded answers—a shift directly aimed at building trust, especially in enterprise contexts.
The launch wasn’t flawless. The Guardian collected early user reports of spelling mistakes, geography slip-ups, and brittle reasoning in edge cases. But that’s normal when shipping to hundreds of millions of users—each bug surfaces faster, and each fix rolls out at global scale.
The Numbers Behind the Rollout
Scale is one of OpenAI’s advantages. ChatGPT already serves hundreds of millions weekly active users, meaning GPT-5 landed in one of the largest live testbeds in tech history. On day one, it had instant distribution, immediate user feedback, and the ability to course-correct in near real time.
Formal benchmarks—such as SWE-Bench for coding or Chatbot Arena’s crowd-rated Elo scores—will take time to stabilize. Early signals show measurable jumps on reasoning and coding tasks, with GPT-5 expected to climb leaderboards as more head-to-head user votes are logged.
The Quiet Revolution: OpenAI Goes Open Source (Again)
Three days before the GPT-5 headlines, OpenAI quietly released two open-weight models—gpt-oss-120b and gpt-oss-20b—under an Apache 2.0 license.
This is the company’s first open-weight release since 2019, and it’s strategically profound:
- Run anywhere: Both models can operate locally on a high-end GPU or even certain edge devices.
- Approaching proprietary quality: Performance is competitive with smaller closed models, especially for reasoning and tool-use tasks.
- Enterprise-friendly: The Apache license allows private adaptation, a must for finance, healthcare, defense, and government.
Coverage from VentureBeat noted there is significant improvement of performance. Some early developer feedback is mixed—benchmarks look good, but real-world adoption will be the judge.
Strategically: OpenAI now owns both ends of the spectrum. At the edge: free, adaptable, open-weight models. In the cloud: the most powerful model available to the public. For CIOs, that means one vendor relationship can now serve sensitive, offline workloads and high-compute reasoning jobs.
Enterprise Traction: Real Cases, Not Just Demos
OpenAI’s B2B position isn’t theoretical. A few examples:
- PwC has rolled out ChatGPT Enterprise across the U.S. and U.K., with features like secure identity management and provisioning, and role-based access controls. This isn’t about novelty—it’s about being IT-ready.
- Azure OpenAI Service customers such as Petrobras, Motor Oil Group, and Physics Wallah report efficiency gains from summarizing for 110,000 employees to cutting “weeks to minutes” for standard workflows.
- OpenAI’s Stories showcase deployments in design (Figma), travel (Expedia), sports (San Antonio Spurs), and retail, each embedding ChatGPT in production processes.
The significance: OpenAI is delivering in both the consumer app market and enterprise procurement channels, without diluting its core technology between them.
Why This Strategy Is Unprecedented
1. Open source without surrendering the frontier.
By releasing Apache-licensed models, OpenAI fuels developer adoption and customization. These small models stay inside OpenAI’s conceptual ecosystem—similar instructions, tool integration, and safety tuning—making it easier for users to graduate to the frontier model when they need more power.
2. Advancing the flagship at speed.
GPT-5’s launch comes less than a year after GPT-4o, and delivers meaningful gains in reasoning and safety. Auto-routing and safe completions are the kinds of changes that move AI from a “sometimes tool” to a trusted daily partner.
3. Scaling consumer and enterprise and government together.
Most companies pick one lane—consumer or enterprise—and expand later. OpenAI is doing both at once, feeding usage data from hundreds of millions of individuals into a refinement loop that benefits Fortune 500 deployments.
This combination evolved the very structure of the AI industry faster and faster. One firm is simultaneously pushing the top end of performance while commoditizing the bottom end. Not only that, but the AI race between the US and China is faster and bigger than the space race was between USSR and the USA, and speeding up.
Has Anyone Done This Before?
The short answer: not like this. Microsoft has long balanced consumer and enterprise, Apple too. But no one has simultaneously:
- Released open-weight, permissively licensed AI models.
- Launched a state-of-the-art closed model.
- Deployed to hundreds of millions of consumers and production-ready enterprises.
And to do it within days is a velocity play rarely seen outside the very fastest wartime innovation.
What Leaders Should Do Next
- Transform your firm to a hybrid organization: Silicon intelligence continues to evolve at the top end, and the bottom end is getting rapidly commoditized. Only firms that combine these “high level” and “low level” tools will win.
- Exploit GPT5’s optimization-routing: Let the system handle model selection, but track failure modes internally to inform fine-tuning or retrieval augmentation.
- Measure impact on real tasks: Look beyond model benchmarks. Track KPIs like code fix times, support ticket resolution, and content QA accuracy.
- Make sure OpenAI’s products are in your solution set. Given the breakthrough nature of this compound strategy, any IT shop of size needs to have a relationship with the OpenAI ecosystem.
- Change your concept of time. Generative AI and AI are a capability, and capabilities can’t be bought – they must be grown as an integration of technology an organizational capacity. You need to recalibrate your concepts of time to compete effectively and growth capability quickly enough to drive competive value.
The Takeaway
We all have to adjust our clocks – to a faster cadence. This is true for how we run our organizations, and how we engage technology suppliers. We are watching the early chapters of a full-spectrum AI strategy executed in AI Time: edge openness, cloud frontier, consumer scale, and enterprise readiness—executed in lockstep. The early reviews tell OpenAI what to fix; the architecture tells us where it’s headed. Every firm must adapt it’s organization and economic model to take advantage of silicon intelligence. Remember that every ROI has an embedded time scale, and that time scale has not changed for over 100 years – since the railroad put us all on mechanical time – not seasonal/natural time. Now, AI time forces us all to question how fast is fast enough.
For business leaders, the message is clear: change your clocks, and begin to create and evolve your hybrid organization – with a combination of human and silicon intelligence – which I outlined in an earlier Forbes column. The sooner you change your organizational metronome, through hybridization of your business, the faster you will shifts from isolated traditional automation to compounding organizational advantage.