Next major revolutions in foundation models will come from small language models

Next major revolutions in foundation models will come from small language models | I I I strongly suspect that the next major revolutions in in like 01 level revolutions in in foundation models will come from the small side because it's so much more accessible and so much easier for researchers to make progress

I I I strongly suspect that the next major revolutions in in like 01 level revolutions in in foundation models will come from the small side because it's so much more accessible and so much easier for researchers to make progress

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{
  "nia": false,
  "url": "https://www.youtube.com/watch?v=dmtvGKuRE64",
  "mode": "PREDICTION",
  "role": "Host",
  "context": "I I I strongly suspect that the next major revolutions in in like 01 level revolutions in in foundation models will come from the small side because it's so much more accessible and so much easier for researchers to make progress",
  "verbatim": "I I I strongly suspect that the next major revolutions in in like 01 level revolutions in in foundation models will come from the small side because it's so much more accessible and so much easier for researchers to make progress",
  "conv_cues": "strongly suspect",
  "direction": "HAPPEN",
  "timeframe": "Unspecified future",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "An open SLM (<=15B params) matches GPT-4-class on MMLU/HellaSwag standard benchmarks",
      "source": "Phi-4-mini 67% MMLU at 3.8B (Microsoft 2025-2026)",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.75,
      "source_url": "https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/",
      "expected_date": "2027-03-17",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2027-09-30",
        "from": "2026-09-01"
      },
      "measurement_criterion": "Public benchmark (MMLU >=86, GPQA >=50) achieved by a model with <=15B activated params, peer-reproduced"
    },
    {
      "kind": "llm_pre_event",
      "label": "First 'O1-class' reasoning architectural breakthrough published from SLM-side research",
      "source": "Wissner-Gross thesis on accessibility-driven research",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.6,
      "expected_date": "2027-06-16",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2028-06-30",
        "from": "2026-06-01"
      },
      "measurement_criterion": "Peer-reviewed or arXiv paper from non-frontier-lab origin demonstrates novel reasoning paradigm at <=20B params with clear lift over prior SOTA"
    },
    {
      "kind": "llm_pre_event",
      "label": "Academic research output on SLMs surpasses LLM-scaling output",
      "source": "Wissner-Gross accessibility argument + observed 2024-2026 trend",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.65,
      "expected_date": "2028-01-01",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2028-12-31",
        "from": "2027-01-01"
      },
      "measurement_criterion": "ArXiv cs.CL submissions tagged for compact/efficient/SLM models exceed those tagged for >100B-scale work, per Semantic Scholar trend"
    },
    {
      "kind": "llm_pre_event",
      "label": "On-device SLMs reach 100% smartphone shipment penetration",
      "source": "Apple Intelligence + Galaxy AI + Gemini Nano deployment trajectory",
      "status": "pending",
      "weight": 0.4,
      "ordinal": -5,
      "source_id": null,
      "confidence": 0.8,
      "expected_date": "2028-09-15",
      "research_origin": "training",
      "expected_date_range": {
        "to": "2029-12-31",
        "from": "2027-06-01"
      },
      "measurement_criterion": "All major mobile OEMs (Apple, Samsung, Google) ship flagship + mid-tier with on-device SLM by default per IDC tracker"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "pending",
      "weight": 0.05,
      "ordinal": -4,
      "source_id": null,
      "expected_date": "2028-11-10",
      "observed_date": null
    },
    {
      "kind": "llm_pre_event",
      "label": "SLM enterprise adoption crosses LLM API spend (industry inflection)",
      "source": "Gartner 3x SLM-vs-LLM use forecast by 2027",
      "status": "pending",
      "weight": 0.4,
... (truncated)