The AI Frontier

The AI Frontier Substack offers insights on building AI products, AI models, and the AI industry, with a focus on practical lessons. It addresses the importance of data quality, effective pricing models, specific evaluations, and differentiating through customer data. It also discusses AI market trends, challenges, and innovation opportunities.

AI Product Development Data Quality AI Pricing Models LLM Evaluations AI Market Trends AI Innovation

The hottest Substack posts of The AI Frontier

And their main takeaways

The future of AI pricing

259 implied HN points • 15 Aug 24
💼 Business Pricing Strategy AI Tools Market Trends Productivity
  1. AI tools should use work-based pricing instead of seat-based pricing. This means companies pay for the amount of work the AI actually does, not just who has access to it.
  2. Consumption-based pricing isn't new; it's been around in various forms for a long time. Many software services bill customers based on how much they use, which can help companies understand costs better.
  3. Work-based pricing can make customers skeptical because it's hard to measure what 'work done' means. Companies need to show how AI adds value and build trust with users.

There's no silver bullet in AI

99 implied HN points • 25 Jul 24
🕹 Technology AI Machine Learning Data science Software Development Tech Innovations
  1. In AI, there's no single fix that will solve all problems. Success comes from making lots of small improvements over time.
  2. Data quality is very important. If you don't start with good data, the results won't be good either.
  3. It's essential to measure changes carefully when building AI applications. Understanding what works and what doesn't can save you from costly mistakes.

You can't build a moat with AI

459 implied HN points • 11 Apr 24
🕹 Technology AI Data Software Engineering Startups
  1. You can't really set yourself apart with just AI models because they're becoming similar across different companies. What matters more is the unique data you use to feed those models.
  2. Even if your prompts seem special, they won't give you a long-term advantage. Competitors can quickly figure out how to improve their prompts, making them less valuable for differentiation.
  3. To succeed in building AI applications, focus on understanding and using your customers' data effectively. Good data engineering can really make a difference in how well your application performs.

In defense of vibes-based evaluations

79 implied HN points • 01 Aug 24
🕹 Technology AI Research Product Development Evaluation Metrics User Experience Data Analysis
  1. Vibes-based evaluations are a helpful starting point for assessing AI quality, especially when specific metrics are hard to define. They allow for initial impressions based on user interactions rather than strict guidelines.
  2. Customers often have unique and unexpected requests that can't easily fit into predefined test sets. Vibes allow for flexibility in understanding real-world usage.
  3. While vibes are useful, they also have downsides, like strong first impressions and limited feedback. A mix of vibes and structured evaluations can provide a better overall understanding of an AI's performance.

Introducing the AI Frontier

59 implied HN points • 08 Aug 24
🕹 Technology AI Innovation Software Startups Research
  1. The blog is now focusing more on specific AI topics instead of a wide range of subjects. This will help them share deeper insights and experiences.
  2. They aim to discuss what they've learned from building their AI product and how technology changes impact AI startups.
  3. Going forward, the blog will highlight useful projects and focus on practical lessons, like data cleaning, rather than generic news about AI.
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OpenAI is too cheap to beat (redux)

59 implied HN points • 18 Jul 24
🕹 Technology AI Infrastructure Data Economics Scalability
  1. Data and infrastructure are really important for companies like OpenAI. They collect a lot of data, which helps them improve their models faster than others.
  2. OpenAI is cheaper for fine-tuning models compared to using your own infrastructure. This means most companies will find it more cost-effective to use OpenAI's services instead of trying to run their own setups.
  3. Even though open-source models have potential, big companies will likely stay ahead due to their ability to serve models quickly and cheaply. Switching to a different system is hard and expensive, making it tough for smaller players.

How to build your first LLM evaluation

159 implied HN points • 16 May 24
🕹 Technology AI Machine Learning Data Analysis Software Development
  1. AI needs to show real value to its customers, which means proving it can create real profits. Without this, it’s hard to justify the excitement around AI.
  2. To understand how well AI products perform, it’s important to create custom evaluations that target specific goals. Generic measurements like MMLU don't provide useful insights for particular applications.
  3. Improving AI evaluations is a continuous process that requires careful scoring and can benefit from community feedback. It's crucial to identify weaknesses and refine metrics for more accurate assessments.

AI is breadth-first, not depth-first

99 implied HN points • 06 Jun 24
🕹 Technology AI Product Development Innovation Data Strategy Market Analysis
  1. AI works well across many tasks but struggles with the details. It can help with brainstorming or basic coding but doesn't replace expert-level understanding.
  2. When building AI products, think beyond one industry or function. There are opportunities where different jobs connect and can benefit from shared data.
  3. It's important to understand what experts want from your AI. They expect quality insights, so your AI should be ready to provide that next level of detail.

LLMs are becoming commodities

99 implied HN points • 30 May 24
🕹 Technology AI Software Innovation Data Industry
  1. LLMs are growing similar and it's hard to tell them apart. Companies must now find new ways to stand out as features become alike.
  2. The race to create better models is very fast, and some newer models are catching up to the established ones. This means that model quality is no longer the main thing that makes a provider unique.
  3. For businesses and users, having more options is good for getting better deals. But, many people will likely stick with known brands rather than trying new, less familiar choices.

Open LLMs don’t need to beat OpenAI

119 implied HN points • 09 May 24
🕹 Technology AI Machine Learning Open Source Software Development Data science
  1. Open LLMs, like Llama 3, are getting really good and can perform well in many tasks. This improvement makes them a strong option for various applications.
  2. Fine-tuning open LLMs is becoming more attractive because of their improved quality and lower costs. This means smaller, specialized models can be more easily developed and used.
  3. However, open models likely won't surpass OpenAI's offerings. The proprietary models have a big advantage, but open LLMs can still thrive by focusing on efficiency and specific use cases.

RunLLM: The custom assistant for developer tools

179 implied HN points • 28 Mar 24
🕹 Technology AI Development Software Tools Innovation
  1. RunLLM is a special AI assistant designed for developers, helping them with coding, answering questions, and fixing bugs. It uses specific training to understand a developer's tools and needs better than general assistants.
  2. The way RunLLM works allows it to provide accurate and relevant information quickly. It does this by fine-tuning its learning based on user feedback and the specific data it needs to use.
  3. Setting up RunLLM is easy and can be done through various platforms like Slack and Discord. Developers can quickly start using it to improve their workflow.

We need better LLM evaluations

159 implied HN points • 04 Apr 24
🕹 Technology AI Software Machine Learning Data science Internet
  1. Current methods for evaluating language models (LLMs) are not effective because they try to give one-size-fits-all answers. Each LLM is better suited for different tasks, so we need evaluations that reflect that.
  2. It’s important to look at specific skills of LLMs, like how well they follow instructions or retrieve information. This will help users understand which model works best for their needs.
  3. We need more detailed benchmarks that assess individual capabilities rather than general performance scores. This way, developers can make smarter choices when selecting LLMs for their projects.

AI news roundup: You see what you want to see

79 implied HN points • 23 May 24
🕹 Technology AI Innovation Consumer Tech Market Trends Product Development
  1. Recent AI updates have sparked excitement and frustration; everyone interprets them differently, like a Rorschach test.
  2. The improvements in AI tech are impressive, particularly in multimodality, but their impact varies between consumer and enterprise applications.
  3. The AI market is growing rapidly, with hype increasing and many companies looking to innovate, but there are still big questions about the future and how to stay competitive.

The bull case for AI startups

59 implied HN points • 13 Jun 24
🕹 Technology AI Startups Innovation Product Development Market Trends Enterprise
  1. AI startups have a lot of room for innovation, even with big companies investing heavily in AI. There are still many opportunities for new ideas and products.
  2. Startups can take more risks and try out unusual ideas that bigger companies might avoid due to reputation concerns. This freedom can lead to exciting new products.
  3. While big companies have access to a lot of data and resources, startups can be more flexible and connect data from various sources. This can give them an advantage in creating better solutions for customers.

How to talk to someone who doesn't trust AI

59 implied HN points • 25 Apr 24
🕹 Technology AI Machine Learning Software Development User Experience Product Design
  1. Many people doubt AI tools because they believe they only look good in demos but don't perform well in real life. Trying out LLMs like ChatGPT can often change that opinion for the better.
  2. Some skeptics challenge AI by asking tricky questions that the AI can't answer. It's important to remember that AI has limitations and not every mistake means it's useless.
  3. People notice that AI responses can seem similar, making it hard to trust their accuracy. Customizing answers and improving quality can help address this issue.

Lessons from customer evaluations of an AI product

59 implied HN points • 18 Apr 24
🕹 Technology AI Product Development Data Quality Market Trends
  1. Customers who have experience with AI products often have a better understanding of what to look for. They know what works and what doesn't, so they can more easily evaluate new AI tools.
  2. The quality of data is super important for AI performance. If the data is good, the answers will be better, so paying attention to data quality is key.
  3. Expectations around AI products can be tricky. Some people think AI is not useful, while others expect it to know everything. It's important to set clear expectations about what AI can do.

The rise of AI work

5 HN points • 22 Aug 24
🕹 Technology AI Data Productivity Workforce Automation
  1. AI products should focus on automating work that humans often find tedious. This helps measure their true value to consumers and businesses.
  2. Companies can choose to specialize deeply in one area or offer a broad service across multiple tasks. Each approach has its own strengths and weaknesses.
  3. Finding a middle ground might be beneficial, as it allows companies to manage a workflow that spans several tasks, though they should focus on making sure their quality remains high.

AI must be more than a checkbox

39 implied HN points • 02 May 24
🕹 Technology AI Startups Sales Market Trends Product Development
  1. AI should be seen as more than just a box to tick off. Companies need to genuinely understand how AI can help them, rather than just wanting to say they have an AI strategy.
  2. Startups often waste time on leads that aren’t serious. They need to be smart about who they spend time with to avoid low-quality customers and wasted effort.
  3. When companies buy AI products without knowing the benefits, it can lead to regret and wasted money. It's important for both buyers and sellers to clearly understand the value AI brings.

AI applications are more than an LLM

19 implied HN points • 20 Jun 24
🕹 Technology AI Software Programming Engineering Data
  1. AI applications are more than just using a big model; they need careful design and planning to be effective. It's like building a nice piece of furniture versus just putting some wood together.
  2. Quality comes with a cost, and building great AI solutions takes more time and resources. Cheaper options might save money now, but they often lead to poorer results.
  3. Not all AI applications perform the same, even if they use the same tools. Good performance comes from thoughtful engineering and working with the data properly.

Untangling concerns about consolidation in AI

39 implied HN points • 21 Mar 24
🕹 Technology AI Innovation Ecosystem Regulation
  1. Big companies like Microsoft and Google are becoming dominant players in AI, which could limit competition and innovation. This brings both concerns and some advantages for smaller companies using their technologies.
  2. Acquisitions in the startup world can help new businesses thrive, giving their teams a payoff and bringing fresh ideas to larger companies. However, not every acquisition is a success, and it's important to watch how this affects the market.
  3. As powerful players in AI grow, so does scrutiny from governments. Stricter regulations could create challenges for smaller startups, so finding the right balance is crucial for fostering innovation.

Mid-2024 Predictions Review

0 implied HN points • 11 Jul 24
🕹 Technology AI Machine Learning Software Development Data science Regulation
  1. Commercial large language models (LLMs) like OpenAI's and Anthropic's are still leading the market. They have a big advantage that makes it hard for new competitors to catch up quickly.
  2. Open-source LLMs are improving faster than expected. Their quality is getting closer to commercial models, and they offer appealing price and performance.
  3. Regulation in the AI space is becoming more important. There's a growing need to watch how governments respond and manage AI developments moving forward.