Anthropic联合创始人Jared Kaplan认为，AI领域仍处于早期阶段，许多基础问题尚未得到解答。他强调，提出看似“愚蠢”或“天真”的问题，反而能帮助我们更精确地理解和推进AI技术的发展。Kaplan以“大数据”为例，提出关于数据规模和实际助益的疑问，最终促成了对AI模型大小和计算量之间关系的突破性研究，即“缩放定律”。他认为，这种从根本上提问的方式，能够帮助我们拨开迷雾，抓住核心，从而获得更多有价值的工具和见解，推动AI领域的进步。Anthropic在AI辅助编码方面取得了显著成就，其Claude Sonnet 3.5模型在代码生成质量和速度上表现突出。

💡 提出“愚蠢”问题是AI进步的关键：Anthropic联合创始人Jared Kaplan指出，AI是一个非常新的领域，许多基本问题仍未解决。通过提出看似“愚蠢”或“天真”的问题，可以使大趋势更加具体化，从而推动AI技术的深入发展。这种方式源于他作为物理学家的训练，即审视全局并提出最根本的问题。

🔬 从“大数据”的疑问中发现“缩放定律”：Kaplan回忆起2010年代“大数据”是热门趋势时，他提出的“数据需要多大？它到底有多大帮助？”等问题。这些看似简单的问题，促使他与团队研究AI性能是否能通过模型大小和计算量来预测，最终发现了“缩放定律”，这是AI训练中的一个重要且出人意料的发现，证明了提出基础性问题的价值。

💻 Anthropic在AI辅助编码方面的突破：Anthropic在AI辅助编码领域表现出色，其Claude Sonnet 3.5模型在生成高质量、长度合适的代码方面备受赞誉。公司联合创始人Ben Mann表示，提升AI编码能力很大程度上依赖于试错和可衡量的反馈，而代码的直接输出和测试使得这一过程得以优化。

📈 AI投资的衡量标准：AI投资者Elad Gil认同试错和反馈的重要性，尤其是在编码领域，因为代码的运行和测试提供了清晰的衡量标准，可以针对性地进行优化。这种“内置的效用函数”使得AI在特定任务上的改进更加直接和有效。

Anthropic's cofounder said the key to advancing AI is asking "very naive, dumb questions."AI is an "incredibly new field" and "a lot of the most basic questions haven't been answered," said Jared Kaplan.Dumb questions can make big trends "as precise as possible," the chief science officer added.

Anthropic's cofounder said the key to advancing AI isn't rocket science — it's asking the obvious stuff nobody wants to say out loud.

"It's really asking very naive, dumb questions that get you very far," said Jared Kaplan at a Y Combinator event last month.

The chief science officer at Anthropic said in the video published by Y Combinator on Tuesday that AI is an "incredibly new field" and "a lot of the most basic questions haven't been answered."

For instance, Kaplan recalled how in the 2010s, everyone in tech kept saying that "big data" was the future. He asked: How big does the data need to be? How much does it actually help?

That line of thinking eventually led him and his team to study whether AI performance could be predicted based on the size of the model and the amount of compute used — a breakthrough that became known as scaling laws.

"We got really lucky. We found that there's actually something very, very, very precise and surprising underlying AI training," he said. "This was something that came about because I was just sort of asking the dumbest possible question."

Kaplan added that as a physicist, that was exactly what he was trained to do. "You sort of look at the big picture and you ask really dumb things."

Simple questions can make big trends "as precise as possible," and that can "give you a lot of tools," Kaplan said.

"It allows you to ask: What does it really mean to move the needle?" he added.

Kaplan and Anthropic did not respond to a request for comment from Business Insider.

Anthropic's AI breakthroughs

Anthropic has emerged as a powerhouse in AI‑assisted coding, especially after the release of its Claude Sonnet 3.5 model in June 2024.

"Anthropic changed everything," Sourcegraph's Quinn Slack said in a BI report published last week.

"We immediately said, 'This model is better than anything else out there in terms of its ability to write code at length' — high-quality code that a human would be proud to write," he added.

"And as a startup, if you're not moving at that speed, you're gonna die."

Anthropic cofounder Ben Mann said in a recent episode of the "No Priors Podcast" that figuring out how to make AI code better and faster has been largely driven by trial and error and measurable feedback.

"Sometimes you just won't know and you have to try stuff — and with code that's easy because we can just do it in a loop," Mann said.

Elad Gil, a top AI investor and No Priors host, concurred, saying the clear signals from deploying code and seeing if it works make this process fruitful.

"With coding, you actually have like a direct output that you can measure: You can run the code, you can test the code," he said. "There's sort of a baked-in utility function you can optimize against."

BI's Alistair Barr wrote in an exclusive report last week about how the startup might have achieved its AI coding breakthrough, crediting approaches like Reinforcement Learning from Human Feedback, or RLHF, and Constitutional AI.

Anthropic may soon be worth $100 billion, as the startup pulls in billions of dollars from companies paying for access to its models, Barr wrote.