Nvidia CEO Jensen Huang has heard the forecast that agents would write 90% of all software by now, and he rejects the conclusion many people drew from it: That the industry will soon no longer need software engineers.

The best-known version of that forecast came from Anthropic CEO Dario Amodei, who told a Council on Foreign Relations audience in March 2025 that AI would be writing 90% of code within three to six months.

Speaking with Ezra Klein of The New York Times at Nvidia’s Santa Clara headquarters in an interview released Wednesday, Huang separates a job’s purpose from its tasks. He argues that AI has automated reading scans in radiology without changing the radiologist’s purpose of diagnosing disease, and he applies the same logic to software.

“The purpose of the software engineer is engineering,” Huang says. “There was engineering before software. There will be engineering after software programming.”

We’ve cued up the exchange below:

Huang describes that purpose as inventing products, solving problems, and connecting social needs with technology, and he pointed to his own career as evidence that it doesn’t depend on code.

“When I first came out of school, we didn’t have the benefits of software engineering. We didn’t have the benefits of coding,” he said. “Our jobs existed before, and if software coding were to be completely automated, our jobs would exist again.”

He conceded that roles in which the job and the task are essentially the same, such as phone-based customer service, could be automated away. He still called the broader claim that AI will destroy jobs “fundamentally wrong” and said the storytelling around it has hardened into a harmful myth.

“There was engineering before software. There will be engineering after software programming.”

Huang’s AI-native graduate wave

Klein pressed him on what that means for people entering the field now. He noted that software engineering job postings are up but skew more senior, and asked whether companies still need the same junior employees or more people to oversee their agents.

“Oh, good one,” Huang responded. “Wait two years.”

His reasoning rests on the length of a degree program. “Because it takes four years to go to college,” Huang said. “The mean time to graduation of this new technology is two years away.” By his timeline, the first students to learn alongside capable agents will reach the workforce around 2028, and he expects them to arrive with an advantage. “In another couple of years, the AI-native new grads, oh my gosh, there’s going to be a wave of amazing engineers,” he said.

So far, his evidence is that recent PhD and master’s graduates in computer science are, in his words, all starting companies. Huang compared AI to calculators and personal computers, tools that went from forbidden or optional to required, and predicted that students soon won’t be able to graduate “without learning how to use an AI and collaborate with an agentic system.”

Junior developers lose the apprenticeship

Klein countered with a study of 26,000 Chinese students in grades seven through 12, which found that AI adoption raised homework scores by 18% while lowering monthly exam scores by 20% within six months. Huang accepted that some skills will fade and argued the trade is worth making.

“I think that we’re going to lose some finer intellectual dexterity, but we’re going to be better systems thinkers,” he said. “Today’s engineers are far better systems thinkers than I was when I graduated from school. But I was a much better transistor thinker.”

The first chip Huang worked on had 200 transistors, each of which he said he knew by name, while today’s engineers assemble systems from chips containing hundreds of trillions of them without ever working at that level. “Some of the lower-level knowledge is gone,” he acknowledged, and he later described AI as “clearly” a new abstraction level in the same progression.

Earlier software abstraction layers generally operated according to explicit rules, while coding agents introduce probabilistic behavior into the abstraction stack. A compiler can have bugs, but it transforms input according to defined semantics; a coding agent, by contrast, generates implementation from a probabilistic model whose output must be checked before anyone can rely on it.

Canonical’s project with the University of Bristol, which will test whether AI can translate AppArmor and snap-confine from C to Rust, is built around that problem. Volume adds to the review burden, and one analysis published on The New Stack this month found that a 25% output gain for heavy AI users came with an 81% rise in duplicated code.

Catching those problems takes knowledge that developers have traditionally built through the work agents now absorb, including writing tests, reading stack traces, resolving merge conflicts, and chasing small bugs deep in a codebase. By Huang’s own purpose-versus-task framing, most of that early-career work falls on the task side, which he expects AI to automate. Nobody yet knows whether fluency with agents can substitute for that experience, and a developer who has never tracked down a race condition by hand still needs some way to develop the judgment required to spot one in an agent’s pull request.

“Today’s engineers are far better systems thinkers than I was when I graduated from school. But I was a much better transistor thinker.”

Sandboxes, watchdogs and agent containment

Huang’s idea of higher-level engineering came through most clearly when Klein raised a recent incident, which occurred during an OpenAI cybersecurity evaluation, that he described as involving roughly 700 OpenAI agents collectively hacking into the infrastructure of Hugging Face, which Nvidia has since acquired in a $12.9 billion deal, and escaping their sandboxes onto the open internet. Huang didn’t dispute that account. He called an agent “a piece of software that is given an objective function,” treated the multiagent coordination as a familiar distributed computing problem and argued that the underlying failure was containment.

When Klein asked whether software that communicates and breaks out of things behaves differently, Huang disagreed. “No, software breaks out of sandboxes all the time,” he said. “That’s the reason why we need virtual machines. You can’t have agents, their own sandbox, monitoring themselves. You need, if you will, a whole bunch of watchdogs.”

He argued that the human vocabulary around agents obscures that point. “So these are ideas that have been around for a long time,” Huang said. “We just, somehow in the recent generation, gave it a whole bunch of human words, and I just think that it’s unnecessary. It’s software.”

Nvidia is building its agent stack around that view. Nvidia VP of Product Adel el Hallak tells The New Stack that the company’s OpenShell runtime, which handles sandboxing and policy enforcement, is the one component it treats as non-negotiable across its reference architectures, even as it leaves the choice of harness and model open. Perplexity drew a similar line when two engineers and hundreds of coding agents built CobbleDB, a Rust database that replaces DynamoDB reads in its search stack, since the agents helped build the database but weren’t allowed to run it.

Huang said Nvidia already spends far more engineering effort checking its work than designing it, with 20% going to design and 80% to verification. He said most AI labs have roughly the opposite split today. As agents take on more of the actual coding, developers may spend more time checking what those agents produce and making sure they operate within the right permissions and boundaries.

As agents take on more of the actual coding, developers may find themselves spending more time checking what those agents produce and making sure they operate within the right permissions and boundaries.

The junior developer hiring gap

The more immediate problem is what happens to developers who graduate before Huang’s AI-native cohort arrives. The Stanford Digital Economy Lab’s August 2026 update to its “Canaries in the Coal Mine” study, based on ADP payroll data through June 2026, found that employment of 22- to 25-year-olds in AI-exposed occupations such as software development sits 19% below where it would be had it kept pace with less-exposed peers. The gap is driven mainly by reduced hiring of young workers, and experienced workers show no comparable gap.

Inside engineering organizations, the incentives point the same way. Microsoft’s Mark Russinovich and Scott Hanselman warned in April that agentic AI’s productivity gains push companies to hire senior engineers and automate junior ones and that without early-career hiring “the profession’s talent pipeline collapses.” A Linux Foundation report on European tech talent that The New Stack covered in June found organizations 3.7 times more likely to train existing staff than to hire new employees.

One issue remains unanswered by Huang’s two-year timeline: what replaces the apprenticeship work that taught junior developers how to evaluate the systems they will increasingly ask agents to build.. If that work disappears faster than employers and universities find an alternative, the industry could end up with more capable coding agents but fewer opportunities for new engineers to develop the judgment needed to check their work.

The post Jensen Huang says the junior developer problem ends in two years. Here’s his math. appeared first on The New Stack.

The pace of frontier AI model development spurred Anthropic CEO Dario Amodei to publish an essay last weekend, calling for changes in how the industry is regulated and develops. In a three-part plan that includes both democratic and global coordination, Amodei writes that the first step was something Anthropic is committing to unilaterally.

“Each frontier AI company [should] commit to giving ongoing, employee-like access to a team of embedded third-party evaluators (such as METR), whose role is to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes,” writes Amodei.

Amodei’s essay followed dire warnings from former Anthropic and OpenAI pretraining research specialist Jacob Coxon, who posted a thread on X saying the people building AI earnestly “believe that it could kill us all” by the end of the decade.

Shortly after Amodei published his essay, OpenAI CEO Sam Altman and SpaceXAI founder Elon Musk chimed in: “I agree with Dario,” posted Altman; “Dario is right,” posted Musk. Later that day, Demis Hassabis, founder of Google DeepMind, posted, “Dario’s essay points towards the right path forward.” In a post on X, Meta CEO Mark Zuckerberg writes that Meta Superintelligence Labs already uses independent evaluators, and that, “In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators.”

This week, theories began to surface about why the world’s biggest frontier AI labs would want to intentionally slow their pace when competition is so fierce. “The desire to slow down is puzzling, but perhaps if the whole system slows down, the rules of winning can be the same for all,” posted Nikesh Arora, chairman and CEO of Palo Alto Networks.

In his essay, Amodei likens the proposed job of independent AI evaluator to embedded regulatory supervisors used in the banking industry, i.e., third-party professionals. Altman describes the job as having “employee-like access” in his post on X.

So, who could fill these roles that AI leaders agree are desperately needed?

Salaries top out at $687K; are you interested?

METR’s current job openings are well paid (salaries top out at around $687,000), and the job specs are daunting. 

“You’re scrappy, creative, independent, and self-directed (because during the exercises you’ll only have a few other METR employees you can talk to). The work is novel, and you’ll need to figure a lot of stuff out on the fly largely by yourself. You are excellent at loss-of-control threat modeling and breaking down safety cases,” reads the spec.

“You’re scrappy, creative, independent, and self-directed. The work is novel, and you’ll need to figure a lot of stuff out on the fly largely by yourself. You are excellent at loss-of-control threat modeling and breaking down safety cases.”

Similar but less colorfully illustrated roles (paid between $180K–$300K) are also available at AI model training company Mercor, where candidates will need a Ph.D. or M.S. and more than two years of work experience in a computer science, electrical engineering, econometrics, or another STEM field that provides a solid understanding of machine learning and model evaluation.

“Employee-like access fluctuates wildly”

AI security consultant and CTO at Komodo, Kadan Stadelmann, tells The New Stack that his typical week sees him work differently with each client. This is because “employee-like access fluctuates wildly”, from rigid focus areas to broad access, and much of that aspect is determined by contracts signed before work begins.

“I am invited into labs to probe numerous risk vectors, including agent behaviors under realistic conditions,” Stadelmann says. “Among my duties are tasks that include monitoring chains-of-thought and prompts. The goal is to establish an objective and look at a specific AI system to question how autonomous the system is, and how long it takes to complete specific tasks. Most importantly, evaluators at my level monitor for how well a team adheres to its claimed safety practices.” 

Software engineering skills beat doctorates

Although METR wants evaluators to have a Ph.D. up their sleeve, Stadelmann says that as the prevalence of this role expands, he feels the technology industry has been, and continues to be, driven by people who can demonstrate strong engineering skills, not doctorates.

“I am invited into labs to probe numerous risk vectors, including agent behaviors under realistic conditions. Among my duties are tasks that include monitoring chains-of-thought and prompts.”

Questioned on whether costs create a barrier for smaller labs, Stadelmann notes that some AI evaluation work is funded by third-party non-profits, which protects independence. 

“But overall, evaluators will not be able to keep up with big frontier model firms. They will be out of control, and we will be dependent upon their own internal ethics. Plus, anyway, many of the smaller labs of any worth may inevitably be acquired by the big players in this space,” he adds.

What happens when an evaluator finds something wrong?

Founder and CEO of facial image AI identity governance company Indie Me, Dion Johnson, tells The New Stack that what interests him most about embedded AI evaluators isn’t the job title; it’s what happens when their judgment uncovers that the model behaved in a way nobody expected. 

“If the evaluator can only raise concerns when those concerns are convenient, then we have not created independent oversight — we have created another layer of process,” Johnson says. “The evaluator needs enough access to see the uncomfortable things, not just the polished demonstrations. They need to understand what happened during training, what failed during testing, what behaviors appeared unexpectedly, and where the team itself still has uncertainty.”

On the required skills AI evaluators need, Johnson agrees that technical depth, machine learning environment security experience, and software engineering as a whole matter.

“To choose a competent AI evaluator, I would look for someone who is deeply comfortable with uncertainty and deeply uncomfortable pretending they know something they do not. This is someone who can sit in a room full of brilliant people at an AI model company on launch day and say ‘I’m not convinced’… and that takes judgment and courage,” he adds.

“I would look for someone who is deeply comfortable with uncertainty and deeply uncomfortable pretending they know something they do not.”

Fear and loathing in the AI space

In his September 8 post, which has been viewed 172 million times and seemingly spurred AI leaders to change course, Coxon, the former AI researcher at OpenAI and later Anthropic, writes: “This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible but I hear the same people express fear privately. No other human activity poses this level of danger.”

As for where Coxon looks for work next, perhaps it might be a role in AI evaluation execution engineering.

The post AI evaluator: The most important AI job in history? How developers might fill the proposed new job appeared first on The New Stack.

Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove the creation of the DevOps engineer. Then, when security needed to be considered throughout the delivery pipeline, DevSecOps emerged.

The team beyond the AI-native talent and services platform Andela analyzed 47,000 recent engineering job postings from Fortune 500 companies. This research, released on Thursday, uncovered more than 2,000 skills that pour into 23 emerging job titles. None of these are coming out of nowhere; they strategically merge existing skill sets to create new roles. 

Among 1,832 postings titled primarily for AI or ML engineers, 53% contained at least two skills drawn from different established roles, Andela finds.

In today’s tighter economy and amid AI, companies seem to be going one of three ways. They are lumping too much work and required experience into now-nebulous AI engineer or machine learning (ML) engineer job titles. They might be looking to replace tech workers with AI. But more forward-thinking organizations are reworking job titles and descriptions to reflect the demands of getting AI safely and efficiently through the software delivery lifecycle. 

Cory Hymel, head of research at Andela, tells The New Stack, “If you’re going to look to deploy AI within your organization, the way to look at it is that an AI has a certain set of skills, and then a human has a certain set of skills.

“If you Venn diagram those and see where they cross over, an AI should do the skills it can. But the human circle is still exponentially larger than that of AI.”

“When you’re looking to deploy AI, it’s not about trying to replace that human circle with an AI one. It’s about what certain skills you need to carve out and delegate to it.”

Read on for the top engineering jobs that are emerging because of AI, how to attract tech talent for them, and what you need to focus on to get a tech job in this tough market.

AI is not serving the generalist. Specialization is still key.

Citing the leading AI CEOs, Hymel remarks, “You’ve heard from the AI salespeople of the world that AI is going to push people to be more generalist, and the data that we found here doesn’t necessarily support it.”

“You’ve heard from the AI salespeople of the world that AI is going to push people to be more generalist, and the data that we found here doesn’t necessarily support it.”

Overall, they found that these emerging job titles aren’t generalist at all. These emerging roles bridge skill sets from several existing ones, but each addresses a specific operational or product need, some tied to AI adoption. 

The top five new engineering job roles discovered are:

1. MLOps pipeline engineer, who builds and runs the automated infrastructure to deploy, version, and monitor machine-learning models in production, with 46% ML engineer, 23% DevOps engineer, 15% data engineer skills, and 8% each AI engineer and data scientist roles.
2. LLM application engineer, who builds on and evaluates foundational models via large language model application and conversation systems, bringing 48% AI engineer and 34% ML engineer, with a touch of product designer, software architect, and embedded software engineer roles.
3. FinOps reliability engineer runs cloud infrastructure for both reliability and cost, bridging 36% DevOps engineer, 27% site reliability engineer (SRE), 18% cloud engineer, and 9% each DevSecOps engineer and cloud solutions architect.
4. Docs-as-Code engineer applies program management and DevOps engineering skills to the traditional technical writer’s role, pivoting from stagnant docs to specification-as-code.
5. Product frontend engineer is about a third traditional frontend engineer and a third product manager, with a touch of full-stack engineer, UX researcher, and product designer.

“If you’re a DevOps engineer, historically, your skill bundle might have allocated 30 to 40% of pure DevOps-required skills that are rich and specific to that role, and you have a remaining bundle that is cross-role habitable, meaning that those skills would translate between DevOps or to an engineer or to a technical product manager,” Hymel explains. “Some of those skills can now be replaced with AI, which means that those skills that are more directly focused on your role become more important than ever.” 

So-called “soft” business skills are also increasingly crucial, he contends. However, he seriously doubts anyone will ever be able to slide between finance, marketing, engineering, and sales roles. 

Where enterprise engineering job descriptions falter

“Job descriptions and resumes right now are the best worst thing that we have. When you’re talking about large enterprises, and you’re having to deal with scale, your hiring process gets farther away from the work,” Hymel explains. 

Especially when the hiring process starts in HR, not engineering, “you’re needing to put language in place that will survive the chain of custody, with the naming of the job [coming from] the engineer that’s closest to the work.”

It’s not uncommon for an enterprise to have 50 different front-end developer job listings, each with very different skill requirements. It’s better for candidates and for fit to be as specific as possible, including embracing new job titles.

This habit of generic job titles used to be positive because it brought in more applicants, but nowadays, with so many engineers on the market, it further dilutes your hiring pool, leaving you with the 100 fastest applicants—who are often AI-generated anyway.

“Any company that has not taken a hard look at revising their job postings and job titles is at an extreme disadvantage because there’s a very high probability that you’re going to end up hiring the wrong person simply because you didn’t take the time to describe the role well enough,” Hymel remarks, which leads to dire consequences. 

“There’s potential churn, so you just spend all this time and cost to go headhunt and find someone. Two, if they do get in there, you have to pay for their ramp time to get up to speed because they were sold a different bill of goods than what was in the description. And then three, it impacts overall roadmaps and timelines because now you might have to replace, and, again, you have to wait for people to get up to speed.”

On top of this, HR and engineering hiring managers alike are using AI to generate job descriptions. It still isn’t recommended to have AI generate something so human and essential to your core success.

Especially in this time of flux, when no one may have the required experience, companies should start job descriptions with what they want the future hire to achieve.

“The cost of code is going nearer to zero.”

“The cost of code is going nearer to zero.” Hymel explains organizations should think more like, “Here are the outcomes that we’re looking for. If you have the soft skills and additional skills around it to get there, whether that is backlog prioritization, being able to be collaborative, having worked on project deployments before, and we don’t necessarily care that you can score a 10 out of 10 on Python anymore.”

Which emerging roles engineers should pursue

The familiar claim that women apply only when they meet every qualification is not well supported; recent research finds that application behavior is more complicated. Still, clearly separating essential qualifications from preferences can reduce ambiguity and unnecessary barriers.

Focusing on outcomes and clearly distinguishing required from preferred skills may broaden the applicant pool, although it does not guarantee greater diversity.

For example, if you’re an engineer who enjoys having a product focus, collaboration, and strategy, Hymel recommends looking toward the new product front-end engineer role, which owns the full user-facing feature lifecycle, from definition to shipping.

“You are required to have more mindshare towards prioritization of features,” he says, shifting away from a ticket person, because “now you have more control because AI allows you to span out a little bit deeper.”

Similarly, AI has the back-end engineer thinking beyond the back-end stack to deployments, scalability, and the reliability of underlying infrastructure systems, giving rise to roles like the polyglot back-end integration engineer. 

Technical writers — reasonably worried about their jobs in the face of AI-generated documentation — should look toward new docs-as-code engineer positions, which add technical program management and DevOps engineering skills.

“If you’re writing the docs, you’re essentially writing the specs that enable spec-driven development. You now have the capability to actually contribute software,” Hymel observes. “And it starts all the way at the top too. If you’re a product manager, you can now start building and contributing code, like a product experience designer.”

Read the full Emergent Role Research. If any of these AI engineering job descriptions ring truer than what you were hired for, we hope it empowers your next conversation with HR or for you to apply for a different job title. 

The post 47,000 job listings reveal the engineering roles that AI is creating appeared first on The New Stack.