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Engineering Manager Vs IC: How to Choose With Clarity

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This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!

The Individual Contributor–Manager Fork: It’s Not a Promotion. It’s a Profession Change.

When I was promoted to engineering manager of a mid-sized team at Clorox, I thought I had made it.

More money. More stock. More visibility. More proximity to senior leadership. From the outside, and on paper, it was clearly a promotion.

I had often heard the phrase, “Management isn’t a promotion. It’s a job switch.” I brushed it off as cliché advice engineers tell each other to sound wise.

It turns out both things were true. It was a promotion. It was also an entirely different job.

And I was nowhere near ready for what that meant.

A Shift in Priorities

There’s surprisingly little training for new managers. As engineers, we’re highly technical and used to mastering complex systems. Many of us assume managing people will be easier than distributed systems. Or we assume it’s just “more meetings.”

Both assumptions are wrong.

Yes, I had more meetings. But what changed most wasn’t my calendar, it was how my impact was measured. As an individual contributor, my output was visible. Code shipped. Features delivered. Bugs fixed.

As a manager, my impact became indirect. It flowed through other people.

That shift was disorienting.

So I fell back into my comfort zone. I started writing more code. I tried to be the strongest engineer on the team. It felt productive and measurable.

It was also a mistake.

By trying to be the number one engineer, I was neglecting my actual job. I wasn’t supporting senior engineers. I wasn’t unblocking systemic problems. I wasn’t building career paths. I was competing with the very people I was supposed to enable.

Management is about amplification.

Learning to Redefine Impact

The turning point came when I began each week with a simple question:

What is the single most impactful thing I can do right now?

Often, it wasn’t code. It was writing a document that clarified direction. It was fixing a broken process with a single point of failure. It was redistributing ownership so that knowledge wasn’t concentrated in one person.

I started deliberately removing myself from implementation work. I committed to writing almost no code. That forced trust. It also revealed gaps in the system that I could address at the right level: through coaching, documentation, hiring, or process changes.

Another major shift was taking one-on-one meetings seriously.

Many engineers dislike one-on-ones. They can feel awkward or devolve into status updates. I scheduled them every other week and approached them with a mix of tactical alignment and human check-in.

I rarely started with engineering questions. Instead:

  • Are you happy with the work you’re doing?
  • Do you feel stretched or stagnant?
  • What’s frustrating you right now?

Burnout doesn’t show up in Jira tickets. Neither does quiet disengagement.

Those conversations helped me anticipate turnover, redistribute workload, and build trust.

I also spent more time thinking about career ladders. Was I giving my team the kind of work that would help them grow? Was I hoarding high-visibility projects? Was I clear about what senior-level impact looked like?

That work felt less tangible than code, but it moved the needle far more.

Why I Went Back to IC

Ultimately, I returned to the individual contributor track.

Part of it was practical: I was laid off from my management role, and the market rewarded senior IC roles more strongly at the time. But if I’m honest, the deeper reason was simpler.

I love writing code.

I enjoy improving systems and helping people, but the part of my day that energized me most was still building. Management required relinquishing that. You can’t be absorbed in technical implementation and deeply people-focused at the same time. Something has to give.

Personally, I don’t need to climb the corporate ladder to feel successful. And you might not have to. Many organizations offer technical leadership tracks that are truly in parity with management when it comes to salary bands. Staff and principal engineers steer strategy without managing people.

If you want to remain deeply technical, you should think very carefully before moving into people management. It requires surrendering control over implementation and focusing on alignment, growth, and long-range planning. If you don’t genuinely care about those things, you won’t just be unhappy, you’ll make your team unhappy.

A Simple Test Before You Choose

Before taking a management role, ask yourself:

  • Do I get energy from solving people-problems every day?
  • Am I comfortable measuring impact indirectly?
  • Would I be satisfied if I rarely wrote production code again?
  • Do I want leverage or craft?

There’s no right answer.

The IC/manager fork isn’t about prestige. It’s about what kind of work you want your days to consist of.

Choose based on energy, not ego.

—Brian

Stanford University’s AI Index is out for 2026, tracking trends and noble developments in artificial intelligence. This year, China has taken a notable lead in AI model releases and industrial robotics compared to previous years. AIs are rapidly reaching benchmarks and achieving high levels of compute, but public trust in AI and confidence in government regulation of AI is mixed.

Read more here.

Much like large language models have learned from existing texts, new AI physics models are being trained on simulation results. This results in “large physics models” that can simulate situations in transportation, aerospace, or semiconductor engineering much faster than traditional physics simulations. Using new AI physics models “can be anywhere between 10,000 to close to a million times faster,” says Jacomo Corbo, CEO and co-founder of PhysicsX.

Read more here.

Kyle McGinley is an IEEE Student Member pursuing a bachelor’s degree in electrical and computer engineering at Temple University. Joining IEEE helped him to develop the skills necessary for real-world teams. “In school, they don’t teach you how to communicate with people. They only teach you how to remember stuff,” he says.

Read more here.

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Nvidia closes in on Hugging Face acquisition

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Nvidia has agreed to buy Hugging Face for $12.9 billion, The Information reported Wednesday night, citing a source familiar with the matter. Business Insider, which first reported over the weekend that Hugging Face was fielding takeover interest, reported Wednesday night that the talks — which would value the company at more than $13 billion — had not yet produced a signed agreement and could still atomize.

TechCrunch reached out earlier to both Nvidia and Hugging Face for comment, and neither has yet responded. (Nvidia’s silence is particularly noteworthy here as the company has moved quickly in the past to address reports it considers inaccurate.)

Maybe it was destined from the start. Hugging Face, founded in 2016, is one of the most popular hubs where developers share and download open-source AI models. Buying it would give Nvidia a strong foothold in the world of open-source AI, right as open-source developers are doing their level best to catch up to closed AI systems from companies like Anthropic and OpenAI.

Why would Nvidia want that? Most obviously, it comes down to protecting its dominance in AI chips, which, from the outside at least, appears increasingly at risk, even with Nvidia’s aggressive chip-release schedule. Pretty much all of the biggest closed-source AI labs (OpenAI, Google, Amazon, and Anthropic) are now in the process of building their own AI chips to lessen their reliance on Nvidia. A thriving ecosystem of open-source AI models gives customers more alternatives to those closed labs, which in turn keeps more of the market dependent on Nvidia’s hardware. That’s also why Nvidia has already poured tens of billions of dollars into building its own open-source AI models.

Should we be surprised that Hugging Face’s days as an independent outfit appear numbered? Not really. Hugging Face CEO Clem Delangue has spent much of this year publicly aligned with Nvidia’s open-source push, amid a debate that has been building for months, as Washington officials reportedly weighed restrictions on open-weight models. (After Chinese labs like Moonshot AI released systems like its Kimi K3 model that matched leading U.S. models on benchmarks while costing a lot less to run, talk of competitive and national-security concerns appeared to grow in Washington, with some critics of closed labs — like White House advisor David Sacks — suggesting the fears were being fanned by the “duopoly” of Anthropic and OpenAI.)

In an appearance on CBS’s “Face the Nation” earlier this month, for example, Delangue said Hugging Face used an Nvidia-modified version of a Chinese open-source model to defend itself after a cyberattack and pointed to a recent letter — signed by Nvidia CEO Jensen Huang and 24 other companies, including Hugging Face — urging the U.S. government to support open models rather than restrict them. In a separate CNBC interview in late July, Delangue made similar points, citing that same letter while warning that China is “clearly dominating” open-source AI.

The deal would also mark something of a comeback for Nvidia in cloud computing. Nvidia reportedly scaled back its own cloud business, called DGX Cloud, about a year ago. But according to The Information, owning Hugging Face — which already helps developers run their AI models using rented computing power — could give Nvidia a way back into that market without starting from scratch.

There’s also a financial safety net at play. Nvidia has promised to help cover the cost of tens of billions of dollars in cloud computing deals for its customers. If those customers end up not using all the computing power they signed up for, Nvidia could get stuck with it. Owning Hugging Face would give Nvidia the ability to sell that unused capacity to Hugging Face’s customers.

The price marks a huge jump from Hugging Face’s last known value. The company raised $235 million in 2023 in a funding round that valued it at $4.5 billion. That round was led by Salesforce Ventures, with money also coming from Alphabet’s GV, IBM Ventures, and Nvidia itself, among others.

This wouldn’t be Hugging Face’s first brush with an Nvidia offer, either. Hugging Face turned down a $500 million investment offer from Nvidia late last year that would have valued it at $7 billion, the Financial Times previously reported. Hugging Face said at the time it didn’t want a dominant investor that could sway its decisions.

As for why it would say yes now, one could argue that a buyout is different from taking on one giant backer — a scenario that often means ceding control while being pressured to continue growing.

Hugging Face is also still a comparatively small business by revenue in the world of AI. The Information reported it was recently generating about $150 million a year in revenue, up from roughly $100 million just two months earlier.

That growth has enabled the company to get “close to profitability,” as Delangue told TechCrunch last month. Still, a price near $13 billion would be a massive multiple for a company this size and hard to resist.

Not last, the deal would give Hugging Face access to Nvidia’s much deeper pockets just as other, AI infrastructure competitors start to get pulled into other outfits, as suggested by Stripe’s recent deal to acquire OpenRouter, a startup founded in early 2023 that helps customers select different AI models to perform different tasks depending on their needs and budget.

OpenRouter was valued at just $1.3 billion back in May during its Series B round. Stripe reportedly paid more than $7 billion to make it its own earlier this month.

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OpenAI Restores 5-Hour Codex Limit for ChatGPT Plus

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ChatGPT Plus users got a taste of fewer restrictions, but OpenAI has now put the clock back on Codex and ChatGPT Work.

OpenAI restored a usage allowance that resets every five hours for ChatGPT Plus subscribers using Codex and ChatGPT Work on Aug. 25, ending a temporary period when only the weekly quota applied.

Thibault “Tibo” Sottiaux, an OpenAI engineering lead working on Codex and ChatGPT, announced the change on X after the company temporarily removed the five-hour restriction in July. The move gave Plus users more freedom to use the tools during individual sessions, but that period has now ended.

Sottiaux said the five-hour window helps OpenAI spread computing demand more evenly while preserving a relatively generous weekly allowance.

He also said some newer and more casual Plus subscribers were unintentionally consuming their entire weekly allowance in a single stretch, leaving them confused when they could no longer use the tools.

What happens when users hit the limit

The five-hour restriction works alongside the weekly quota. Once a Plus subscriber exhausts either allowance, the user must wait for the relevant reset or purchase additional credits to continue using Codex.

OpenAI had temporarily removed the five-hour window in July, while also resetting weekly allowances early on some occasions as Codex and ChatGPT Work reached usage milestones. That gave developers and other heavy users a short period of greater flexibility before the restriction returned.

For now, Sottiaux said the five-hour restriction will remain disabled “for the upcoming months” for users on the plans he identified as the $100 and $200 tiers. Enterprise and Edu accounts use a separate credit-based system and are not covered by the Plus-plan change.

More must-read AI coverage

A less predictable experience for developers

For frequent Codex users, the restored five-hour window reduces flexibility. A developer working through a demanding project could exhaust that window’s allowance even when weekly usage remains available.

But there is a practical reason for the restriction. AI coding tools can consume significant computing resources, and allowing users to concentrate large amounts of usage into short periods can make demand harder to manage. Spreading that usage across five-hour windows gives OpenAI more control over its infrastructure while preserving a larger weekly pool.

Plus subscribers working on demanding projects now need to monitor both their five-hour and weekly allowances. Before beginning a long coding session, users should check their remaining capacity and plan for a reset or additional credit purchase if either allowance is running low. The change gives OpenAI more control over computing demand, but it also makes usage less predictable for developers who rely on Codex throughout the workday.

Read more: OpenAI’s Codex Windows app brings its AI coding workspace to more developers, with tools for managing multiple coding tasks from a dedicated desktop interface.

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Viral AI startup Instinct has raised $350 million at a $2.5 billion valuation

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Instinct, a startup founded only last year and helmed by a 23-year-old, has managed to ride the wave of AI enthusiasm toward a gargantuan valuation over the course of the summer.

The company, which offers an AI assistant that has inspired enthusiasm among its early users, told the Wall Street Journal on Wednesday that it had raised $250 million in a recent Series B funding round. That new round brings the company’s total funding to $350 million and gives the startup a valuation of $2.5 billion.

That new funding round was co-led by Index Ventures and Benchmark, the Journal reported.

Instinct, which is offered by the company Spear Street Technology and led by founder Noah Shinn, is an agent that the company says can efficiently organize your life. Users connect it to their apps and devices and can communicate with it via texts and calls.

“I’m thrilled with everything our early users are doing with Instinct,” Shinn wrote in a tweet on Wednesday. “They’ve told us they’ve planned cross-country road trips, bought weekly groceries and concert tickets, and cancelled hundreds of dollars of subscriptions. Someone’s even planning their wedding with Instinct.”

Instinct, which rocks a decidedly lo-fi website, is currently in private beta, but it has already inspired a certain amount of controversy due to privacy concerns. Online, users have worried about the overly generous permissions that the app requires as well as its terms of use that has disturbed some users because of their invasive potential.

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