This week’s new podcasts and curated resources:

• AI for Finance and Fundraising - Rod Turner, Manhattan Street Capital

• AI for Music Entrepreneurs - Allan Sutton, Piano-Technique Montréal

• When does generative AI qualify for fair use? (OpenAI whistleblower)

• How can engineers future proof their careers in light of LLMs? (HackerNews)

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AI for Finance and Fundraising - Rod Turner, Manhattan Street Capital

Rod Turner is Founder and CEO of Manhattan Street Capital, the #1 Growth Capital platform for mature startup and mid-sized US and Canadian companies. He helps companies raise growth capital via S-1 IPO, Direct IPO Listings, SEC compliant ICOs and token offerings for the blockchain.

A serial entrepreneur, Rod has built seven successful High Tech Startups to success and liquid outcomes, including 2 IPOs as a senior executive. He served as Executive VP for Symantec where he launched Norton Antivirus, growing it from $20M/year to $190M/year revenue during his tenure.

• Linkedin: https://www.linkedin.com/in/rod-turner-49b7b2 

• Website: https://www.manhattanstreetcapital.com

AI for Music Entrepreneurs - Allan Sutton, Piano-Technique Montréal

Allan Sutton is a piano tuner and technician based in Montreal and founder of Piano-Technique Montréal. His work combines a love for traditional craftsmanship with the transformative power of artificial intelligence, which he uses daily to enhance client communications and streamline my business.

Over the years, this interest has grown, leading him to present at the PTG convention and publish in industry journals on AI's role in piano maintenance. He's excited to share insights on how AI tools have become integral in his field and to discuss innovative ways he's leveraging them to improve client experiences.

• Linkedin: https://www.linkedin.com/in/allan-sutton-21157235

• Website: https://pianotechniquemontreal.com

When does generative AI qualify for fair use?

Suchir Balaji is the OpenAI whistleblower who passed away earlier this week. I don’t know any more than what’s in the news, but I wanted to share the blog post he published on gen AI, copyright, and fair use.

Generative AI and fair use is a complex topic. When you train a model, it copies copyrighted data, and determining whether this constitutes fair use hinges on several factors: purpose and nature of the use, amount used, and the effect on market value. Each of these factors can tip the scales differently based on context.

For generative AI, the main contention lies in the commercial nature of its outputs and the market impact on the original sources, like Stack Overflow and Chegg use both severely declining. Does AI's outputs serve as substitutes or complements to the original works? If AI provides answers similar enough to those on Stack Overflow, both in purpose and quality, then it might be seen as a substitute, potentially harming the market for the original. The degree of transformation in AI outputs — whether they're seen as significantly modifying the original work — is another crucial factor in deciding fair use. And, when AI outputs incorporate substantial elements of the training data, it blurs the line between inspiration and duplication. This is especially evident in scenarios where the model's low-entropy outputs suggest reliance on the training data.

Nothing is legally settled or clarified yet, but I recommend his post to learn more.

How can engineers future proof their careers in light of LLMs? (HackerNews)

The more AI excels at code generation, the more it supposedly threatens the need for software engineers, particularly at the junior and mid levels.

But if we take a step back, isn't this just the latest chapter in a longstanding story of automation anxiety? The top comments in this HN thread discuss other technologies that threatened to dismantle whole industries but instead transformed them. As AI takes on repetitive well-defined tasks, software engineers should (in theory) have more bandwidth to devote to problem-solving and creative thinking.

Ultimately, software engineering isn't just about pumping out lines of code. It's more about understanding and prioritizing what problems truly need solving, constrained by user requirements, existing code, timelines, etc, and architecting an elegant solution.

Here’s the full HackerNews thread.

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