In AI product development, outputs are scrutinized by highly skilled and experienced people across anthropic, economic, and diverse criteria, marking ...Show more

Rather than showcasing trivial benchmarks, prioritize demonstrating AI on tasks with proven GDP impact to secure executive buy-in and excitement.

Technology leaders must convert product and engineering benefits into business metrics and language that resonate with boards, execs, and general mana...Show more

Cameron expresses a preference for watching AI-generated retro-style commercials over browsing TikTok, criticizing the platform’s content quality.

Tom argues that a well-designed multi-agent system leveraging open-source models like GPT OSS can outperform out-of-the-box top-tier closed models.

Large agencies access more advanced image-generation APIs through enterprise engagements, while average users rely on consumer-grade apps.

Scripting via API yields more reliable, automated workflows for image and video models than manual web interfaces.

High-quality generative video models like Sora sacrifice controllability, prompting a need for workflows that balance quality with precise direction.

World models will radically outperform today's point-cloud and frame-by-frame AI tools, making scene generation and camera control far more precise an...Show more

Current video AI models lack user-facing annotation components for interactive reasoning or on-screen masking and labeling during generation.

AI apps either enforce strict guardrails to avoid generating copyrighted content or take a YOLO approach believing they'll ‘run the gauntlet’ rather t...Show more

Tom Spencer argues that AI companies should explicitly account for anticipated legal costs and lawsuits in their fundraising discussions by budgeting ...Show more

Cameron Rohn observes that the courts lack the capacity and established frameworks to effectively arbitrate the burgeoning volume of AI-related lawsui...Show more

Focusing on a narrowband, productized AI video model can outperform general-purpose APIs in specific use cases like user-generated content.

Combining human-like operator tasks with deep text and non-text data scraping significantly boosts AI agent performance on complex queries.

AI agents without browsing and interactive context suffer in research applications, making browser integration essential for handling hard-to-find que...Show more

There's a natural breakpoint in AI tasks where text-only context hits its limit and you need more structured world models or multimodal inputs.

AI agents should support incremental back-and-forth interactions rather than one-shot completions to allow users to iterate and refine tasks.

The augmentation paradigm for AI agents must go beyond web-based streamable UI and may primarily remain chat with tools for knowledge workers, while a...Show more

Users need transparent access to AI agents’ reasoning and decision-making streams for better control and the ability to pivot tasks mid-execution.