Build a Market Landscape

from gildea_sdk import Gildea

client = Gildea()

# 1. Pull all themes by axis
value_chain = client.themes.list(axis="value_chain")
market_force = client.themes.list(axis="market_force")

# 2. Summarize theme trajectories
print("VALUE CHAIN THEMES:")
print(f"{'Theme':30} {'Direction':>12} {'Notability':>12} {'SoV':>8}")
for theme in value_chain["data"]:
    print(f"{theme['label']:30} {theme['direction'] or 'N/A':>12} {theme['notability'] or 'N/A':>12} {theme['trend']['share_of_voice']:>7.1%}")

print(f"\nMARKET FORCE THEMES:")
print(f"{'Theme':30} {'Direction':>12} {'Notability':>12} {'SoV':>8}")
for theme in market_force["data"]:
    print(f"{theme['label']:30} {theme['direction'] or 'N/A':>12} {theme['notability'] or 'N/A':>12} {theme['trend']['share_of_voice']:>7.1%}")

# 3. Get full detail for a theme of interest
infra = client.themes.get("value_chain", "Infrastructure")

print(f"Infrastructure:")
print(f"  Direction: {infra['direction']}")
print(f"  Confidence: {infra['confidence']}")
print(f"  Notability: {infra['notability']}")
print(f"  Reasoning: {infra['notability_reasoning']}")

# Co-occurring themes from the other axis
print(f"\n  Co-occurs with (market forces):")
for related in infra.get("related_themes", []):
    print(f"    {related['label']} ({related['co_occurrence_count']} signals)")

# 4. Find the most active entities within a theme
entities = client.entities.list(theme="Infrastructure", sort="signal_count", limit=10)

print(f"\nTop entities in Infrastructure:")
for ent in entities["data"]:
    print(f"  {ent['display_name']:25} signals={ent['signal_count']:>4}  scale={ent['scale']}")

# Which themes are moving fastest?
all_themes = value_chain["data"] + market_force["data"]
by_slope = sorted(all_themes, key=lambda t: abs(t["trend"]["theil_sen_slope"]), reverse=True)

print("Themes with strongest directional movement:")
for theme in by_slope[:5]:
    slope = theme["trend"]["theil_sen_slope"]
    direction = "up" if slope > 0 else "down"
    print(f"  {theme['label']:30} slope={slope:+.4f} ({direction})")

# For each value chain segment, show which market forces drive it
for theme in value_chain["data"]:
    detail = client.themes.get("value_chain", theme["label"])
    forces = detail.get("related_themes", [])[:3]
    force_names = ", ".join(f["label"] for f in forces)
    print(f"{theme['label']:25} driven by: {force_names}")

import json

landscape = {
    "generated_at": "2026-04-17",
    "value_chain": [],
    "market_forces": [],
}

for theme in value_chain["data"]:
    detail = client.themes.get("value_chain", theme["label"])
    top_entities = client.entities.list(theme=theme["label"], sort="signal_count", limit=5)
    
    landscape["value_chain"].append({
        "theme": theme["label"],
        "direction": theme["direction"],
        "notability": theme["notability"],
        "share_of_voice": theme["trend"]["share_of_voice"],
        "slope": theme["trend"]["theil_sen_slope"],
        "top_entities": [e["display_name"] for e in top_entities["data"]],
        "driven_by": [r["label"] for r in detail.get("related_themes", [])[:3]],
    })

for theme in market_force["data"]:
    detail = client.themes.get("market_force", theme["label"])
    top_entities = client.entities.list(theme=theme["label"], sort="signal_count", limit=5)

    landscape["market_forces"].append({
        "theme": theme["label"],
        "direction": theme["direction"],
        "notability": theme["notability"],
        "share_of_voice": theme["trend"]["share_of_voice"],
        "slope": theme["trend"]["theil_sen_slope"],
        "top_entities": [e["display_name"] for e in top_entities["data"]],
        "driven_by": [r["label"] for r in detail.get("related_themes", [])[:3]],
    })

# Output as JSON for downstream use (slides, dashboards, agent context)
print(json.dumps(landscape, indent=2))

landscape_json = json.dumps(landscape, indent=2)

SYSTEM_PROMPT = """You are a senior strategy consultant producing an AI market
landscape brief for a client executive audience. You will receive structured
theme data from Gildea's AI market intelligence platform covering the full AI
economy across two axes: value chain (where in the stack) and market forces
(what dynamics are at play).

Rules:
- Write for a CTO, managing partner, or investment committee — not a technical audience.
- Lead with the 2-3 most important strategic takeaways, not a data dump.
- For each theme, translate direction/slope into plain language:
  "rising" = growing share of expert attention, "declining" = fading from discourse.
- Name specific companies when they appear in top_entities. Executives want names.
- Identify the 1-2 most important INTERSECTIONS between value chain and market
  forces (e.g., "Infrastructure is being driven primarily by Competitive Dynamics,
  meaning the compute layer is where the fiercest battles are happening").
- End with "implications" — what a decision-maker should do with this information.
- Keep it under 600 words.

Output format (markdown):

## AI Market Landscape Brief — [Month Year]

### Key Takeaways
<3 bullet points — the headlines a busy executive reads first>

### Value Chain Overview
<For each value chain theme: 1-2 sentences on trajectory + top players.
 Group into "Rising," "Stable," and "Declining" sections.>

### Market Forces Overview
<For each market force: 1-2 sentences on what's driving it + key players.>

### Critical Intersections
<2-3 observations about which forces are driving which segments>

### Strategic Implications
<3-4 bullet points: what should the reader DO based on this landscape?>
"""

USER_PROMPT = f"""Produce an AI market landscape brief from this structured data:

{landscape_json}
"""

# Pass SYSTEM_PROMPT and USER_PROMPT to your LLM of choice.
# Example with Anthropic SDK:
#
# import anthropic
# llm = anthropic.Anthropic()
# response = llm.messages.create(
#     model="claude-sonnet-4-20250514",
#     max_tokens=2048,
#     system=SYSTEM_PROMPT,
#     messages=[{"role": "user", "content": USER_PROMPT}],
# )
# brief = response.content[0].text

print("=== SYSTEM PROMPT ===")
print(SYSTEM_PROMPT)
print("=== USER PROMPT ===")
print(USER_PROMPT)

## AI Market Landscape Brief — April 2026

### Key Takeaways
- **Infrastructure remains the dominant value chain segment** (32% share of voice,
  rising) — NVIDIA, Google, and Microsoft are the primary battleground players.
- **Regulatory & Legal is the fastest-accelerating market force** (slope +0.0082),
  driven by EU AI Act enforcement timelines and US executive order implementation.
- **Application Layer is rising fastest in the value chain**, with OpenAI, Anthropic,
  and a long tail of vertical SaaS companies competing for enterprise adoption.

### Value Chain Overview

**Rising:** Infrastructure (NVIDIA, Google, AMD) continues to dominate as compute
demand outpaces supply. Application Layer (OpenAI, Anthropic, Salesforce) is
accelerating as enterprises move from experimentation to deployment.

**Stable:** Models & Training (Google, Meta, Anthropic) holds steady share — the
frontier model race continues but is no longer novel. Data & Processing maintains
consistent attention.

**Declining:** None significantly — all segments maintain floor-level coverage.

### Market Forces Overview

Competitive Dynamics (35% SoV) drives the most discourse — this is a market defined
by rivalry. Regulatory & Legal is the fastest mover, reflecting real policy deadlines.
Capital & Investment remains elevated but is plateauing as the "AI funding boom"
narrative matures.

### Critical Intersections
- Infrastructure x Competitive Dynamics: The compute layer is where the fiercest
  battles play out. NVIDIA's dominance is the most-discussed competitive dynamic.
- Application Layer x Regulatory & Legal: Enterprise AI adoption is colliding with
  compliance requirements. Expect this intersection to intensify through 2026.

### Strategic Implications
- **If you're investing:** Infrastructure remains the safest bet, but Application
  Layer offers the highest growth delta. Watch Regulatory for deal-breaker risks.
- **If you're building:** Regulatory readiness is becoming a competitive advantage,
  not just a compliance cost. Build it in now.
- **If you're advising:** Lead client conversations with the Infrastructure +
  Competitive Dynamics intersection — it's where the money and attention concentrate.