{"id":1989,"date":"2024-06-02T18:35:27","date_gmt":"2024-06-02T23:35:27","guid":{"rendered":"https:\/\/n2comms.com\/dev2024\/?p=1989"},"modified":"2024-07-02T13:36:49","modified_gmt":"2024-07-02T18:36:49","slug":"writers-in-the-loop","status":"publish","type":"post","link":"https:\/\/n2comms.com\/dev2024\/writers-in-the-loop\/","title":{"rendered":"Writers in the Loop"},"content":{"rendered":"
\n

\u201cA company asked why it was so hard to hire a good writer. I told them it was because good writing is an illusion: what people call good writing is actually good thinking, and of course good thinkers are rare.\u201d \u2013<\/em>Paul Graham, Y Combinator Founder, on X<\/a><\/em><\/p>\n<\/blockquote>\n

\n
\"Create
Image generated by Dali-E-3 via Poe. (See footnotes for full prompt.)<\/figcaption><\/figure>\n

Dark headlines have conditioned us to believe that AI is going to automate creatives out of existence.<\/h3>\n

As working creatives ourselves, we share these worries. We know that generative AI has the potential to upend the livelihoods of folks like us: writers, editors, and researchers, but also poets, painters, actors, and so on.<\/p>\n

But we also believe that the fears that AI will wholesale replace the creative community grossly underestimate the value of human creativity in AI. The reality is that AI needs creatives of all stripes in the loop to turn technological capabilities into real-world value. <\/strong><\/p>\n

This view isn\u2019t just wishful thinking. It\u2019s what we can learn from the long intertwined history of technology and business. For all the buzz and utopian vs. doomsday headlines, AI is just a really powerful data tool. The difference with this latest data revolution is that the data isn\u2019t the traditional \u201cstructured data\u201d of columns of numbers, it\u2019s the \u201cunstructured data\u201d of content: stories, videos, illustrations, music, dance, art. Until recently, this content was deemed too low-value to be worth the cost to capture, store, and use as data. Generative AI and LLMs have changed that equation.<\/p>\n

Whenever there is a big leap in data technology, the countries and companies that wield the best data generally win. Renaissance Italian merchant cities reintroduced double-entry bookkeeping to Europe<\/a> and dominated the continent\u2019s banking and trade for centuries. Efficiency obsessives like Andrew Carnegie and John D. Rockefeller built their Gilded Age empires more with adding machines, typewriters, and telegraphs than with ruthless Robber Baron tactics. Harvard Business School used standardized testing to find the \u201cWhiz Kids\u201d<\/a> GIs who helped the Allies win World War II, kick-started the Computer Age, and turned America into an economic superpower in the process.<\/p>\n

By the end of 2025, investment in AI is expected to approach $200 billion<\/a>, and most Fortune 500 companies plan to increase AI budgets 2-5x<\/a>. Less clear is where and how to do that safely and effectively. Just as frequently as we read about new AI startups and breakthroughs, cautionary headlines warn of LLMs prompted to reveal sensitive data<\/a>, chatbots hallucinating generous refund policies<\/a> that companies actually have to pay for, and model responses that reinforce discriminatory biases<\/a>.<\/p>\n

Quality content and skilled creators<\/strong> are critical for turning technological promise into real use cases and economic value\u2014and to avoid the pitfalls and lawsuits.<\/b><\/p>\n

The reason is simple: a language model is only as good as the data it is trained on, and better content is better data. To illustrate, about a year ago, I sat in on a private demo of a company\u2019s new AI Agent.1<\/sup>\u00a0Users could ask a question, and the agent would respond in natural language followed by a list of relevant links and citations from the company\u2019s proprietary content stored in a local database.<\/p>\n

A single engineer had worked part-time on the project2<\/sup> and had it up and running in a few months. The real value of the company\u2019s agent didn\u2019t come from the replicable technology; it came from the years of proprietary content. The technology gave users a better way to access that content, but the content itself was what drove value that no competitor could copy.<\/p>\n

More recently, LinkedIn founder and venture capitalist Reid Hoffman created an impressive AI twin of himself. The technology used includes synthetic audio from Eleven Labs<\/a> and a video avatar by Hour One<\/a>, but the real magic is in the content that trained the AI. As Reid says in the video description: \u201c[the] persona\u2014the way that REID AI formulates responses\u2014is generated from a custom chatbot built on GPT-4 that was trained on my books, speeches, podcasts and other content that I’ve produced over the last few decades.\u201d<\/p>\n

We might think of Reid Hoffman as a technologist, but he’s also a very good writer and storyteller who has spent thousands of hours and probably (many) thousands of dollars on editors, podcast producers, and other creatives to hone his skills and clean up his content. It’s this content\u2014the vast troves of unstructured training data\u2014that drove the quality of Hoffman’s AI twin.<\/p>\n