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AI Power Race, Deep Research Innovations, and the Rise of Reasoning Models

Hello AI Friends,

This week brought exciting developments in the AI landscape, with significant focus on the evolution of reasoning models and deep research capabilities.

The announcement of GPT-4.5 and eventual GPT-5 sparked interesting discussions about whether we’re seeing genuine technological advancement or primarily product refinement.


Perplexity’s launch of their deep research competitor at one-tenth of OpenAI’s price point demonstrates how rapidly the market is evolving.

We also had fascinating discussions about model benchmarking and the ongoing challenge of hallucinations in AI outputs.


The energy in the room was particularly high during our discussion of Anthropic’s upcoming reasoning model and what it might mean for the future of AI research and development.


See you next week,

Harry Verity

Bali AI Meetup host

Co-Founder, AI To The World

Attendees:

  • Harry Verity (Host): Tech journalist and AI consultant
  • Dev Chandra – Military background, AI automation specialist, Process Hacker founder
  • Luis – Software engineer from San Francisco
  • Victor – Former software engineer from Ukraine, startup founder
  • Chris – Software engineer working with AI
  • Alex – EECM professional from Germany
  • Hannah – Working with education and AI
  • Sue – Graphic designer and business manager
  • Dan – Education business owner focusing on school worksheets
  • Michael – Using AI in sales and marketing
  • Several other attendees participating both in-person and online

Key Topics Discussed:

1. Model Updates and Industry Developments

Perplexity launched a deep research model rivaling OpenAI, priced at one-tenth the cost

  • Iterative research process with document reading and planning
  • Export capabilities to PDF
  • SEO-optimized sharable pages
  • 500 queries per day (vs OpenAI’s 100)
  • Based on Deep-Seek’s R1 reasoning model

Group Opinions:

  • Multiple attendees noted concerns about hallucination rates in research models
  • Victor shared his experience finding incorrect citations in deep research outputs
  • Luis suggested the lower price point might be linked to using Deep-Seek’s foundation
  • Several members discussed the importance of fact-checking outputs, especially for academic work

2. Anthropic’s New Reasoning Model

Anthropic is set to launch a new AI model merging language skills with advanced reasoning

  • Variable resource allocation through a slider system
  • Focus on enterprise applications
  • Integration with existing Anthropic models

Group Opinions:

  • Debate about whether Anthropic is playing catch-up with OpenAI
  • Questions about how this fits with Claude 3.5 Opus
  • Discussion of Anthropic’s projected revenue growth to $34.5B by 2027

3. OpenAI’s GPT-4.5 and GPT-5 Plans

OpenAI announced plans for GPT-4.5 release within weeks and laid out strategy for GPT-5

  • Moving away from multiple model versions to a single, tiered system
  • Three intelligence levels based on subscription type
  • Integration of O3 reasoning capabilities into main models
  • New product architecture

Group Opinions:

  • Luis expressed skepticism, suggesting it’s “more UX than new model”
  • Several attendees felt disappointed by the focus on product architecture rather than technological advancement
  • Discussion about whether this represents genuine progress or is primarily a business strategy
  • Debate about the impact of Deep-Seek’s release on OpenAI’s timeline

4. Deep-Seek’s Impact on the Industry

Deep-Seek’s $7M open-source model is shaking up the industry

Group Opinions:

  • Discussion of the cost implications for model training
  • Debate about the quality comparison between open and closed source models
  • Analysis of the impact on US-China AI competition
  • Questions about the future of proprietary vs open-source models

Technical Deep Dives

Hallucination Mitigation Strategies

  • The group explored various approaches to managing AI hallucinations:

RAG (Retrieval Augmented Generation):

  • Victor shared experiences with implementing
  • RAG systems Discussion of vector database implementation
  • Debate about whether RAG truly solves or just mitigates hallucination

Multi-Agent Verification:

  • Luis explained how reasoning models might be collections of smaller specialized models
  • Discussion of using multiple models to verify outputs
  • Analysis of the cost/benefit trade-offs of different verification approaches

Benchmarking Challenges

Significant discussion around the reliability of AI model benchmarks:

  • Luis argued benchmarks have “lost all credibility”
  • Discussion of the challenge of comparing models fairly
  • Debate about whether user testing is more valuable than formal benchmarks
  • Analysis of topic-dependent performance variations

Looking Forward

Industry Trends

  • Continued convergence of traditional language models with reasoning capabilities
  • Growing importance of fact-verification and hallucination mitigation
  • Increasing competition between US and Chinese AI companies
  • Trend toward simplified user experiences with more complex backend systems

Upcoming Developments

  • GPT-4.5 release expected within weeks
  • Anthropic’s new reasoning model launch timeline
  • Potential impact of Deep-Seek on market dynamics
  • Evolution of research and reasoning capabilities

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