A primer on AI for busy wine people

👋 Welcome to New Vintage, a weekly taste of tech for wine professionals to level up with no-code and AI tools.

2024 was a hectic year. If you haven't kept pace with the rapid changes in AI, this overview highlights the advancements most relevant to your 2025 planning. Think something’s missing? Comment so we all benefit.

We’ll cover how AI matured—from technical leaps to real business applications—and what these developments mean for leaders. The bottom line: AI should be a top strategic priority next year.

Bottom Line Up Front

2024 marked AI's transition from an experimental technology to a practical business tool. For executives focused on brand building, customer experience, and operational efficiency, several key developments stand out:

• Historic Investment: Over $26B poured into three foundation-model companies (OpenAI, Anthropic, xAI) indicates AI’s shift from experimental to essential.
• Multimodal Proficiency: AI now handles text, voice, images, and video in ways that impact marketing, customer service, and operations.
• Next-Gen Search & Research: Tools are transforming market intelligence and customer insights, speeding decision-making.
• People & Processes > Infrastructure: Immediate gains depend more on workforce readiness and strategic implementation than on building in-house technical capacity.

The Technology Landscape: What Executives Need to Know

Investment and Market Dynamics

The scale of investment in AI reached unprecedented levels in 2024, with funding rounds that would have been unthinkable just a few years ago.

• OpenAI: Raised $6.6B at a $157B valuation—largest VC round ever.
• xAI (Elon Musk): Secured $11B in under a year.
• Anthropic: Total funding hit $13.7B.

These unprecedented investments, largely from Microsoft, Amazon, and Google, show AI is now a business imperative. Meta stands apart by focusing on open-source models. There is a race for AI-dominance amongst "Big Tech". For executives, this means AI will advance rapidly, reshaping every industry.

Core Technologies That Matter Now

Foundation Models and Business Applications

These advanced AI systems now power thousands of business applications without requiring companies to develop their own models.

Key model developments:

• OpenAI's GPT-4o: The first multi-modal native model (can handle text, images, audio all in one model)
• OpenAI's o1: Enhanced reasoning and problem-solving capabilities (first model to think through multiple steps)
• Meta's Llama 3.1: Open-source alternative for customized applications
• Claude 3.5 Sonnet: Advanced capabilities in analysis and communication

Top models (referred to as frontier models) are approaching PhD level sophistication in academic benchmarks and are outperforming humans in software engineering tasks.

As a mental model, assume you can leverage these capabilities either via their chat interfaces or by making requests of these systems.

Practical Capabilities for Business Impact

Multimodal Models - AI models can 'see', 'hear', and 'speak'

• Strong handling of text, voice, images, and video within interfaces like ChatGPT and Claude
  • ex. use the Voice Mode in ChatGPT to talk to it, instead of type OR upload a picture and ask a question about it
• Applications: Marketing content creation, customer service, document processing
• Impact: Faster content creation, consistent brand voice, efficient customer support

Knowledge Integration (RAG) - AI systems can reference documents

• Enables AI to accurately access and reference company-specific information
  • ex. upload a long pdf to Claude and ask it questions about it
• Applications: Customer service, employee training, research
• Impact: More accurate responses, reduced training time, consistent messaging, adherence to SOPs

Enhanced Search and Research - 'Googling' is evolving into AI summaries

• New tools like Perplexity ($10 off) and NotebookLM are transforming market research and competitive analysis
• ChatGPT can now browse the internet during a chat
• Applications: Consumer insights, trend analysis, competitive intelligence
• Impact: Faster decision-making, deeper market understanding, increased analyst productivity

Image/Audio/Video Generation - You can produce very convincing content

• Flux is paving the way for image generation
• Companies like ElevenLabs are pioneering voice model creation and generation
• OpenAI's Sora, Meta's Movie Gen, and companies like RunwayML are pushing the boundaries of video generation and editing forward from text commands
• Applications: Employee training, personalized videos, social media content
• Impact: Faster iteration, better creative, deeper personalization

This evolution of foundation models highlights a crucial industry dynamic: the separation between model development and application deployment.

While most consumers know these models through interfaces like ChatGPT (OpenAI) and Claude (Anthropic), the same underlying models power thousands of third-party applications and startup products.

It's easier than ever to experiment with AI – start by isolating high volume tasks that need to be automated and high value tasks that need to go faster.

Looking Ahead

As we look toward 2025, several trends deserve careful attention:

1. Autonomous AI Agents: Expect more sophisticated agents handling complex, multi-step workflows. This shift from reactive to proactive AI will redefine collaboration and automation.
2. Specialized Models: Niche, fine-tuned models will emerge for specific industries and tasks, using unique internal data.
3. AI Safety & Governance: As systems grow more powerful, standards around “fair use” and responsible AI will gain urgency, but regulation will likely lag.

Key Opportunities for Executives

1. Workforce Development
   1. Prioritize training and upskilling current employees
   2. Create roles that combine industry expertise with AI capabilities
   3. Start small, introducing new tools to familiar tasks
2. Strategic Implementation
   1. Identify high-impact processes for AI enhancement (think high volume tasks or high value workflows)
   2. Focus on operational efficiency improvements first, start with small projects
   3. Expect an iterative process for AI system design and testing, start with out-of-the box tools to optimize for signal on what's working
   4. Assume you'll have human oversight for all early projects/initiatives and develop a way to grade success in tasks completed by AI systems

In the immediate term, success with AI depends more on organizational readiness than technical sophistication. Yes, there are a ton of tools and lots of technical considerations, but the competitive advantages will go to the teams and companies that start.

Dip your toes in with using ChatGPT or Claude for tasks. Brainstorm with your team to identify potential workflows that could be improved. Become a student of the game and learn as much as you can.

The wine industry is great at rolling up their sleeves and getting to work. This is one of those times. Hope this helps get you started.

Cheers,

Stephen

This analysis is based on publicly available information and industry observations. Given the rapid pace of AI development, executives are encouraged to regularly update their understanding and strategies as new capabilities and challenges emerge.