The Ledger
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Krieger: Anthropic Labs builds for capabilities models will gain in six months
“The two most useful thought exercises we do in labs, one is like visualize the gap between what the models can do today and how most people use it. It can be closed that gap. That's one. And the other one is imagine what the models are bad at now that they're …”
Krieger: AI needs 75% to 80% human quality to actually accelerate work
“If you get to, like, 50% as good as you would have done yourself, I don't think that's good enough, and it won't speed you up. And in fact, it's like, I don't know, I could have just done this myself, and now, now, then at least I would have known what it's do…”
Krieger: Massive Platforms Can Only Introduce One New Behavior Per Generation
“Once you get a service as widespread as Instagram or as widespread as Facebook or WhatsApp, it's hard to introduce a new behavior there. You know, we did it with stories. I think they've since done it with reels but it's almost like one, you get one per genera…”
Krieger: First-week reactions to new AI models should be ignored
“I've learned to not really trust day of or even week of model reactions. You don't really know until you've put it through its paces. And so like, I almost just completely block out the noise in the first couple of days of any new model release, because I don'…”
Krieger: Initial model release reactions are unreliable without real-world workflows
“I've learned to not really trust day of or even week of model reactions. You don't really know until you've put it through its paces. And so like, I almost just, Completely block out the noise in the first couple of days of any new model release, because I don…”
Krieger: Anthropic Labs designs products for model capabilities expected in six months.
“The two most useful thought exercises we do in labs, one is like visualize the gap between what the models can do today and how most people use it. It can be closed that gap. That's one. And the other one is imagine what the models are bad at now that they're …”
Krieger: AI tools still place too much technical burden on everyday users.
“We're still asking people to understand way too many concepts of like, what is, you know, the difference between like my sandbox environment and production or like connected MCP as myself or others, or how should I store data?”
Krieger: Data gravity prevents users from switching to 2x better AI apps.
“Even if somebody has a like two X better AI powered, you know, doc editor, you're going to move all your stuff up. Maybe, probably not.”
Krieger: Scaling laws describe what is possible, not what is predetermined
“The scaling laws, I think, paint a picture of what is possible, but is not predetermined. Like, to actually get there, there's a lot of, actually, really difficult, both machine learning and engineering work”
Krieger: Social talent in AI reflects prior concentration of product talent
“I think it's less that there's a lot of social media sort of oriented folks that have now moved into AI. It's more that I think a lot of the best product people were focused on that, you know, even four years ago, you know, pre-chat GPT you know, pre the emerg…”
Krieger: Creative tools historically fail to become enduring social networks
“You would see an emergence of a creative tool and whether they were able to sort of transcend that to being a network that you come back to was often not the case.”
Krieger: Coding capability is on the critical path to agentic AI
“For us, the coding piece, beyond just the fact that coding is a really high value economic activity, I really see the model's ability to plan, write code, solve problems as not just being useful for software engineering, but being really critical path to the k…”