Day: December 2, 2025

• A 26-year-old Google software engineer says it took her a year to transition to an AI team.
• Maitri Mangal dedicated two hours daily toward upskilling and still spends hours learning weekly.
• She says making content helped her understand material and suggests solo projects to nail concepts.

This as-told-to essay is based on a conversation with Maitri Mangal, a 26-year-old software engineer at Google, based in New York. Her identity and employment have been verified by Business Insider. The following has been edited for length and clarity.

When I started off as a software engineer, my dad, who also works in tech, kept telling me to get into AI.

I brushed it off because I was just starting off my engineering career, and no one was really talking about AI in 2019, unless they were getting a PhD.

Then in 2023, the tech industry changed and everyone started going into AI. That led me to want to start pursuing AI as a job, and also creating content about it. When trying to join an AI team, I think having a strong presence and personal brand is crucial for others to take you seriously.

In my three years at Google, I’ve changed roles three times, most recently switching to the Workspace AI team.

It’s important to make a distinction between an AI machine learning engineer and an AI software engineer. An AI ML engineer creates the model, trains it, and evaluates it. An AI software engineer integrates AI capabilities into software applications, and builds APIs and infrastructure to serve the model to the end user.

My transition to an AI team didn’t happen overnight. It required spending about a year upskilling through courses and creating content about the material, which forced me to learn the concepts.

Here’s how I made the switch:

Creating content about AI

In the spring of 2024, I started creating tech content on Instagram and LinkedIn, outside my job. That became a major factor in my transition to an AI team.

Making content motivated me to keep learning and also made me confident about sharing what I knew. Once I started seeing how much it helped people, I wanted to learn more. So that’s where the upskilling started, and I started taking courses to understand the fundamentals of AI.

Eventually, I started applying to AI teams at Google. I felt like if I was going to spend so much time upskilling and making content about AI, I should make the most of what I had. I started searching for new roles in January, about seven months after I started upskilling. In March, I landed the new job.

I still spend an hour a day upskilling

I typically take Google’s internal courses to upskill. Coursera also has amazing courses.

The easiest way to start is by taking the basics of AI, like Google’s Introduction to Generative AI and Google Prompting Essentials. Since I have a computer science background, I was able to get more in-depth with concepts like linear regression and vector analysis.

I took courses for about two hours a day, but in order to absorb the material, I had to talk about it, not just read. When I verbalized the concepts through making content, it helped me understand the material.

I also get feedback from my followers, and when they ask follow-up questions in the comments, it makes me go even deeper into understanding a topic. Talking to friends or teammates who are excited about AI also helps me better understand the material.

In this field, it’s very hard not to learn. I’m not necessarily still dedicating two hours daily to courses, but I still spend about an hour a day upskilling, whether that’s in the form of internal trainings for my job, or watching YouTube courses for the content I create.

Not everyone wants to create content, so that’s not always the best way to go about transitioning to an AI team. If you’re just starting out in tech, my biggest piece of advice would be to take on projects. You should definitely take courses about AI, but keeping up-to-date with the news and doing AI projects also really helps. Many AI courses have users do mini projects, so you get to know how to work with it.

Since I applied internally, I didn’t have to go through the same interview process. However, I still had to submit my résumé, which included all of my side projects, and I think that really helps.

• IBM’s CEO walked through some napkin math on data centers— and said that there’s “no way” to turn a profit at current costs.
• “$8 trillion of CapEx means you need roughly $800 billion of profit just to pay for the interest,” Arvind Krishna told “Decoder.”
• Krishna was skeptical of that current tech would reach AGI, putting the likelihood between 0-1%.

AI companies are spending billions on data centers in the race to AGI. IBM CEO Arvind Krishna has some thoughts on the math behind those bets.

Data center spending is on the rise. During Meta’s recent earnings call, words like “capacity” and AI “infrastructure” were frequently used. Google just announced that it wants to eventually build them in space. The question remains: will the revenue generated from data centers ever justify all the capital expenditure?

On the “Decoder” podcast, Krishna concluded that there was likely “no way” these companies would make a return on their capex spending on data centers.

Couching that his napkin math was based on today’s costs, “because anything in the future is speculative,” Kirshna said that it takes about $80 billion to fill up a one-gigawatt data center.

“Okay, that’s today’s number. So, if you are going to commit 20 to 30 gigawatts, that’s one company, that’s $1.5 trillion of capex,” he said.

Krishna also referenced the depreciation of the AI chips inside data centers as another factor: “You’ve got to use it all in five years because at that point, you’ve got to throw it away and refill it,” he said.

Investor Michael Burry has recently taken aim at Nvidia over depreciating concerns, leading to a downturn in AI stocks.

“If I look at the total commits in the world in this space, in chasing AGI, it seems to be like 100 gigawatts with these announcements,” Krishna said.

At $80 billion each for 100 gigawatts, that sets Krishna’s price tag for computing commitments at roughly $8 trillion.

“It’s my view that there’s no way you’re going to get a return on that, because $8 trillion of capex means you need roughly $800 billion of profit just to pay for the interest,” he said.

Reaching that number of gigawatts has required massive spending from AI companies — and pushes for outside help. In an October letter to the White House’s Office of Science and Technology Policy, OpenAI CEO Sam Altman recommended that the US add 100 gigawatts in energy capacity every year.

“Decoder” host Nilay Patel pointed out that Altman believed OpenAI could generate a return on its capital expenditures. OpenAI has committed to spending some $1.4 trillion in a variety of deals. Here, Krishna said he diverged from Altman.

“That’s a belief,” Krishna said. “That’s what some people like to chase. I understand that from their perspective, but that’s different from agreeing with them.”

Krishna clarified that he wasn’t convinced that the current set of technologies would get us to AGI, a yet to be reached technological breakthrough generally agreed to be when AI is capable of completing complex tasks better than humans. He pegged the chances of achieving it without a further technological breakthrough at 0-1%.

Several other high-profile leaders have been skeptical of the acceleration to AGI. Marc Benioff said that he was “extremely suspect” of the AGI push, analogizing it to hypnosis. Google Brain founder Andrew Ng said that AGI was “overhyped,” and Mistral CEO Arthur Mensch said that AGI was a “marketing move.”

Even if AGI is the goal, scaling compute may not be the enough. OpenAI cofounder Ilya Sutskever said in November that the age of scaling was over, and that even 100x scaling of LLMs would not be completely transformative. “It’s back to the age of research again, just with big computers,” he said.

Krishna, who began his career at IBM in 1990 before rising to eventually be named CEO in 2020 and chairman in 2021, did praise the current set of AI tools.

“I think it’s going to unlock trillions of dollars of productivity in the enterprise, just to be absolutely clear,” he said.

But AGI will require “more technologies than the current LLM path,” Krisha said. He proposed fusing hard knowledge with LLMs as a possible future path.

How likely is that to reach AGI? “Even then, I’m a ‘maybe,'” he said.