Spent two days at AI Conf in Moscow. The shift is complete: nobody talks about traditional ML anymore. It's all agents, RAG, and voice systems.
Average time from submission to publication at A-tier conferences: 9 months. Multiple review cycles, sequential improvements.
What researchers actually use LLMs for now:
Prediction: Future papers will include zip archives of experimental code that AI can verify. Human value shifts to idea generation, not implementation.
Built a working ReAct search agent in the workshop:
Stack cost: $0 for prototyping. Production cost: depends on scale.
| Tool | Approach | Best For |
|---|---|---|
| Langfuse | ||
| Manual integration, detailed traces | Custom setups, granular control | |
| Arize Phoenix | ||
| Auto-instrumentation, wraps everything | Quick setup, less configuration |
Both show traces, token counts, latency breakdowns. Phoenix wins if you want observability without wiring it yourself.
The terminology evolved:
Example: Deep Agents framework. Skill creation costs 2M tokens. Single invocation: 100K tokens. But the abstraction is cleaner than manual tool orchestration.
Voice-to-voice models exist but lack:
**Current production stack:** Speech-to-Text → LLM → Text-to-Speech
Voice-to-voice will replace this eventually, but not before tool calling and context compression catch up.
The industry moved on. If you're still pitching Random Forest improvements, you're talking to the wrong audience.
The conference confirmed what I see in production: agent orchestration is the new infrastructure layer. Not the models themselves — how you connect them, manage memory, route between skills, and monitor everything.
The companies winning aren't those with the best single model. They're those with the best agent architecture.
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