Chapter 1: What is the main topic discussed in this episode?
Welcome to Tech News Briefing. It's Tuesday, March 17th. I'm Katie Dayton for The Wall Street Journal. Silicon Valley has a bot obsession. Software engineers used to burning the midnight oil, churning out lines and lines of code, and now using AI models to do the dirty work for them to varying degrees of success.
We're learning how the sudden shift in definition of tech work is changing the coding culture of the Bay Area. Then we're heading south to NVIDIA's annual AI conference in San Jose, where the word inference is on everybody's lips. Our reporter on the ground will be with us to explain how we got here.
But first, software engineers prone to working solo are finding themselves as managers of ambitious but sometimes unruly workers. And those workers, they're not human.
Chapter 2: What is the significance of Nvidia's pivot towards inference computing?
Tech heads are increasingly employing fleets of AI assistants to do their work. So much so that asking, what are your bots up to, has become one of the hottest questions in Silicon Valley. WSJ reporter Kate Clark has been following the trend. So, Kate, set the scene for us. It's a beautiful day in San Francisco.
The crowds are descending on Dolores Park and people are sitting in the sun with their laptops open. What exactly is going on here? That's exactly right. The AI agents, the AI personal assistants or work assistants have really taken over Silicon Valley and beyond.
It is particularly evident that people are obsessed with getting these AI agents set up, whether it's creating a spreadsheet for them and completing some basic work tasks or coding for them, or it's planning a vacation and handling all of the logistical tasks that take up a lot of our time.
What were some of the stranger anecdotes about this bot management culture that you unearthed reporting this story? It's really just how obsessed with it people are and how, you know, you would think this would make people more efficient. But it seems to me that people are actually a lot more attached to their devices now than they ever were.
And they are staying up into the late hours just kind of making sure that their AI agents are working. And so there is this anxiety that people feel right now. Because they know they can be so much more productive, they really want to make sure they are being as productive as possible.
So I think this interesting dynamic is happening, which is the people are working more despite having these tools that should be making them work less. This behavior appears to have swept the tech world incredibly quickly. What technology or LLM models have come on the scene to make all of this possible? Basically, the main models from OpenAI Anthropic have just been getting better and better.
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Chapter 3: How are software engineers adapting to AI tools in their workflow?
Every few weeks or every few months, they release model updates that have made these other tools built on top just work so much better. Even just in the last couple of weeks, OpenAI has made adjustments, improvements to their model that have made Codex, their AI coding tool, even better. So it's just kind of like the natural evolution of this LLM technology.
And there are many, many startups that are building tools on top of the LLMs that are powering these agents. And they're just catching on. They're just getting better by the day. There's more money being invested in them. It's just constant change. Now, I've watched enough Black Mirror to know that delegating all your grunt work to bots can have terrible repercussions.
Did you hear of any instances where this form of software engineering has gone incredibly wrong? For the most part, it's just they make mistakes and they mess everything up and you have to restart. You hear things about people's entire inboxes being deleted because of the stage we're at, which is really just people learning to use them, learning to see their capabilities.
We really haven't gotten to the part where there's horror stories after horror stories. For now, I think it's just people get irritated because they've got one, two, three, four agents working on their behalf, and they're just not doing the things that they're asking them to do, and they feel like they're basically babysitting the AI bots.
Chapter 4: What cultural shifts are occurring in Silicon Valley due to AI?
Right, like human management in a way. Exactly like human management, which is also a funny part of all of this. Software engineering is a skill that a lot of these people probably spend a lot of time honing. And in the Bay Area in particular, your ability to engineer was almost a status signifier. Is there any sense to all of this that this skill has just been rendered obsolete?
And if so, is there any sense of nostalgia or sadness wrapped up in that? Definitely. I came across such a broad range from people being really upset at the idea that the skill they spent their life learning is no longer relevant. I also came across a lot of people that feel like it is still incredibly important to understand the building blocks in order to successfully manage the AI agents.
But we're very quickly moving into an era where you and me can just use natural English language to command these agents to create websites, to create products. And as we get deeper into that chapter, there will be a lot more concerns about job loss for engineers. There already are signs of that.
But in a few more months, just because of how fast this is moving, there will probably be a much, much bigger story to tell about the true impact on software engineers. That was Wall Street Journal reporter Kate Clark. Are you deploying AI assistance in your work? If you're a listener on Spotify, be sure to let us know in the comments.
Chapter 5: What are the unique challenges of managing AI assistants?
Coming up, demand for inference computing is ramping up. We break down what this means for NVIDIA's business model. That's after the break. This week sees the return of NVIDIA GTC, the chipmaker's mammoth AI conference held in San Jose. All eyes yesterday were on Jensen Huang, the leather-jacketed CEO who addressed the rise of inference computing and what that means for the company going forward.
Finally, AI is able to do productive work. And therefore, the inflection point of inference has arrived. We got the lay of the land from WSJ reporter Robbie Whelan, who spoke to us from a car on the highway while heading to the conference. Robbie, what are we expecting to hear from NVIDIA?
NVIDIA is getting ready to roll out a brand new chip using technology from a company called Grok, which NVIDIA recently essentially acquired. Didn't fully acquire the company, but they paid $20 billion to license Grok's technology, which is chip design technology, and to hire Grok's top leadership. So basically it was what's called an Aqua hire.
And the reason why they did this was because NVIDIA at the moment dominates the world of what's known as AI training. but they don't have quite as much of a foothold in what's known as AI inference. And AI inference means how to run models and make them respond to your user queries faster. What are the different requirements for inference versus AI training chips?
Inference requires a lot more memory. So every computer chip that is in your iPhone or our laptops or even in data centers, it's actually a combination of multiple chips. So we talk about a chip, we talk about a processor, which in NVIDIA's case is called a GPU. And then there's other types of processors called CPUs. And then there's a whole bunch of chips that are known as memory chips.
And those are the ones that allow the computer brain to kind of go back into its memory and access all of its training, access all the databases that have been pumped into it to teach it how to be an AI model. And so inference requires a lot more memory because you have these models that are working to remember everything that they've been trained.
And training requires a lot more raw computing power. So if you think of training an AI model as just doing billions and billions of math problems over and over again, whereas inference, which means allowing a model to go out into the wild and respond to user queries, they have to go back All of the information that's been pumped into the model via training may have to access it.
And that means a lot more memory chips are required, a lot more high bandwidth memory. Inference also requires a lot of computing power. It's not like it doesn't require any good processing at all. It does require fast processors in order to act quickly. But the name of the game right now is driving down costs faster. to consumers and also driving speed to consumers.
The phase we're in right now, we've done a lot of growth, a lot of training of models. Now we're trying to monetize those models. And to do that, you have to have really fast, reliable response times. And so that means the quality of the answer has to be good and has to come quickly. And so that's why inference requires such a different computing profile than training.
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