“I am a great admirer of mystery and magic. Look at this life… all mystery and magic.” — Harry Houdini
Life inside the OpenAI rocketship
This week, Lenny Rachitsky spoke with Logan Kilpatrick, head of developer relations at OpenAI. The conversation was great for two reasons: For one, it offered a sense of the fast-moving culture within OpenAI. “Folks are so high agency,” Logan says. “They see a problem and they go and tackle it.”
Second, the conversation explores the increasing number of applications for how developers are leveraging ChatGPT—and building entire workflows on the OpenAI platform. “I think engineering is actually one of the highest leverage things that you could be using AI to do today, and really unlocking, probably on the order of at least a 50% improvement, especially for some of the lower-hanging-fruit software engineering tasks.” (Unfortunately, they didn’t cover the recent news around OpenAI’s $7 trillion (!!) fundraising effort.)
“Even if revenue is a goal, it’s like revenue is not actually the goal. Revenue is a proxy for getting more compute, which is then actually what helps us get towards getting more GPUs so that we can train better models and actually get to the goal. So there’s all these intermediate layers where even if we say something is the goal, and you hear that in a vacuum and you’re like, ‘Oh, well OpenAI just wants to make money,’ and it’s like, ‘Well, really money is the mechanism to get better models so that we can achieve our mission.’ And I think there’s a bunch of interesting angles like that as well.”
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How to achieve “meeting utopia”
“Most meetings are about people sitting around a table or tiled on a zoom call trying to sound smart,” writes the NZS Capital team. “They have less to do with the task at hand and more to do with career advancement. In short, most meetings…well, they suck.”
Many of us would likely agree: meetings tend to be less about debating the true merits of an idea, but rather about advocating for a specific point of view. How does one break this cycle? The recent NZS Capital white paper—which is relevant for pretty much anyone who has meetings with more than one person—offers several smart ideas about how to foster an environment of trust, create a meeting culture based on meritocracy, and ultimately make better decisions.
“An individual decision maker will often offer the illusion of certainty due to quick decision making with conviction. However, our experience is that these decisions tend to be more bravado than substance. What really leads to better decisions is listening to and understanding others’ informed perspectives. These additional perspectives tend to increase the level of spirited debate, which, in turn, leads to new perspectives that go beyond individuals to something greater. From this foundation, the group can become more than the sum of its parts; one plus one can equal three. Instead of placing confidence in one’s opinion, we change the focus such that the confidence lies in the group – that the group will come to fantastic decisions, and those decisions will not be reliant upon anyone’s ability to predict the future better than others.”
“I recently spent two weeks traveling in Japan, meeting with leaders across a range of industries, from banking to real estate to industrials to technology. Over the past several decades, our team has visited the country consistently, helping us find many ‘diamonds in the rough.’ We have noted positive – albeit slow – developments across the corporate landscape, particularly over the past several years. Increasingly, I have heard investors speaking of enormous change underway in Japan, but my trip revealed a more nuanced story of continued incremental progress rather than radical change.”
“In the context of technological advancements, the growth trajectory’s ability to meet or exceed performance demands determines its disruptive potential. In our case of search technology, the performance demands can be outlined as follows: Providing answers to user queries as swiftly as Google Search does today. Delivering answers with the same level of accuracy as Google Search currently offers. To establish performance benchmarks, it’s prudent to refer to Google Search metrics, given its dominant position with over 90% market share. Now, considering the application of Large Language Models (LLMs), especially advanced models like GPT-4, on the first point, which pertains to the speed of obtaining answers, it can be argued that LLMs already match or potentially surpass traditional Search.”
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