Tag: chatgpt

Pacing, Not Stopping: What This Week’s AI Safety Moment Actually Means

By Victor Kuarsingh

Three AI CEOs who normally compete for the same customers, the same talent, and the same headlines agreed on something this week, and that alone is worth pausing on. Dario Amodei published an essay proposing the industry deliberately pace frontier AI development. Sam Altman backed it publicly, going as far as to tie OpenAI’s own decision to delay its IPO partly to unresolved safety questions. Elon Musk, who agrees with neither of them on much, offered two words: “Dario is right.”

Rivals don’t converge like this without a reason worth examining, and “AI leaders call for caution” is the kind of headline we tend to nod at and move past. The substance underneath it deserves more than that.

What “pacing” actually means

The easiest mistake here is hearing “slow down” and assuming someone means “stop.” No one serious does. Amodei’s framing is explicit: pacing, not stopping. The proposal has three concrete parts. Frontier labs should give independent safety evaluators the kind of standing access employees get — badges, laptops, desks, the ability to see what’s actually happening inside a lab rather than a sanitized summary after the fact. The U.S. government should grant a narrow antitrust waiver so competing labs can coordinate on safety standards without opening themselves to legal risk for talking to each other. And democratic governments need some mechanism to bring less cautious developers, into a common framework — otherwise a company pacing itself responsibly just hands its lead to whoever paces themselves the least.

This isn’t a September idea that appeared from nowhere. Back in July, more than 1,100 employees across the major labs — including Amodei, his Anthropic co-founders, OpenAI’s chief scientist, and safety leads at Meta and Google — signed a letter asking government to build the regulatory tools needed to pace development if it started accelerating beyond what anyone could meaningfully oversee. That letter didn’t call for an immediate slowdown either; it asked for the capability to slow down to exist before it’s needed, which is a more defensible ask than it sounds at first pass. As of this week, it’s moved from essays and letters to an actual working group: OpenAI, Anthropic, and Google have reportedly been meeting for weeks on a coordination framework modeled loosely on FINRA — voluntary 30-day pre-release reviews, embedded third-party evaluators, shared testing protocols.

We’ve been here before, just with different technology. The internet scaled globally not because one company set the rules for everyone, but because competing organizations agreed — often reluctantly — to coordinate through shared standards bodies rather than let raw speed dictate the outcome. That’s the entire premise behind standards processes many of us have spent years contributing to: rivals in the same room, building specifications none of them fully control, because a standard only holds if no single participant can bend it to their own advantage later. Pacing the frontier is, in effect, an attempt to build that same kind of coordination for AI, compressed into months instead of decades, with far higher stakes riding on getting it right the first time.

The part that shouldn’t get skipped

This is also where the story gets more interesting than “industry leaders show responsibility,” and where the coverage has mostly been too credulous. Cohere’s CEO called the coordination effort “a cartel by any other name.” David Sacks has argued the safety rationale is doing double duty as cover for ordinary commercial incentives — that reducing liability exposure and giving customers predictability is simply good business, dressed up as principle. Neither critique is unreasonable. The companies proposing to set the pace and the companies best positioned to benefit from a slower, more regulated frontier are the same companies, and standards built by whoever has the most to gain rarely serve everyone equally — ask any engineer who’s had to build around a proprietary “standard” that later became a market-share weapon. Meta, Microsoft, Amazon, and many other developers are, notably, not in this particular room. Whatever gets built here only means something once it extends to the players with the least incentive to slow down.

Why this matters beyond the frontier labs

The frontier-lab conversation is getting the headlines, but it’s not where most of us actually encounter AI. It’s downstream — in the products enterprises buy, the workflows we automate, the decisions we let a model make before a person ever reviews them. The same discipline Amodei, Altman, and Musk are applying to their own labs applies one level down, to every enterprise, provider, and consumer of AI pushing these systems into production: the same care, the same independent scrutiny, the same resistance to shipping capability faster than it can be verified as safe. A frontier lab pacing itself doesn’t accomplish much if the fifty enterprise deployments built on top of its models skip that same discipline in the name of shipping faster.

Where this leaves us

None of this is an argument for halting anything. It’s a case for intentional balancing — layering the right safeguards in as capability advances, from the foundation model providers building these systems down to the enterprises and teams putting them into production where the outcomes land on real people. The frontier labs made news this week for saying that out loud. The rest of us building on top of their work should be doing it just as deliberately, whether or not it makes headlines.

Risk Of Foundational Knowledge Beyond 2026

By Victor Kuarsingh

AI is pushing the boundaries of technological abstraction further than ever before. While no one is arguing we should abandon modern tooling and code like it’s 1965, this rapid evolution introduces a silent organizational risk. Every previous era of computing has shown that when an abstraction breaks, the organizations that are impacted most are the ones where technology creates problems and few or nobody is left in the building can reason about what is underneath.

The Challenge Ahead

When we look back over the past 60-70 years, the industry has come a long way from our early computing and programming era.  In those early days, the application user was most often the code developer with a necessarily deep understanding of the foundational computing system by which functions were built.   As operating environments evolved, such as the release of Unix by AT&T Bell Labs in the 1970s, programmers and users were able to leverage a more abstracted environment resulting in productivity gains. As we approached the 80s, that further evolved where end users were rarely the programmer, and even more unlikely to have a deep understanding of the foundational computing platforms. 

In the 1980s, the acceleration of networking was driven by the need to expand the early ARPANet that bloomed into the Internet by the mid 1990s.  Those early networking experts were most often computer scientists that formed early connectivity protocols and systems morphing into a defined discipline by the mid-1990s.  Early networking talent, just like the early systems and programming talent, was often very well versed in the foundational infrastructure’s base architecture, design and capabilities. Moving further into the 2000s, the rapid expansion of vendor supplied products, standard deployment models, and further abstraction began to distance users, administartors and programmers of networking systems from those foundational capabilities. 

The advantage of these abstractions both within the software and networking space were revolutionary in allowing for scaling and acceleration of deployments, modern applications and outcomes.  However, an artifact of this change is the slow and often silent deterioration of foundational expertise in many businesses. Areas where experience often now lacks includes networking, security, computing, operating systems and platforms.  The deep learning curve that was needed to be functional years ago, no longer is mandatory for most designers and engineers to operate within their areas of the business.  That separation and devolution of “making it work” contrasting to understanding “how it works” – poses a risk to business, especially when those foundations are needed for resiliency and overall business success.

AI, which has displaced so many foundational tasks and roles, not only further adds to the separation, but may further accelerate the deterioration of skills development and growth of cognitive reasoning when designing, using and improving systems or platforms.  The new risk somewhat exacerbated by AI is the release of engineering rigor as a pillar of development teams are able to produce without really knowing how the product (code or config) is being generated.   

With a declining population of highly competent engineers that understand the foundations, and an ever increasing demand for those people, its going to be interesting to see what transpires in the market and in businesses.  Early impacts will likely be filled with outages and missed opportunities for businesses.  The longer term impact may be foundationally  anemic businesses unable to produce substantive products and services relegated to producing fragmented and unreliable technical products.   The challenge we face as business leaders is – can we leverage AI to accelerate now without sacrificing our future success.

Modern examples where eroded engineering rigor and abstraction-layer failures caused significant business impact include CrowdStrike (2024), Meta (2021), and Knight Capital (2012) — each resulting in substantial financial loss and reputational harm.

So What

The challenge we face as leaders and within the Industry is clear.  We must leverage AI to accelerate our velocity without sacrificing our future integrity.  We need deliberate balancing between embracing this new layer of abstraction while still protecting our base of needed expertise. 

Allowing AI to completely sever our engineering teams from the underlying mechanics of our networks, systems, and platforms introduces a business risk. When a catastrophic failure inevitably occurs beneath the AI-generated layer, the absence of deep, cognitive reasoning won’t just cause a routine outage—it could result in an entirely unrecoverable situation where no one in the building knows how to rebuild the collapsed foundation.

AI can generate the code and configure the platforms, it cannot assume the risk. Moving forward, engineering rigor cannot be outsourced. We must intentionally cultivate, value, and retain the talent that understands the “how,” ensuring that as our technology becomes increasingly abstracted, our ability to govern, troubleshoot, and recover our businesses remains firmly intact.