Czechia
Falling behindEuropeHigh income
Power #35 / 50Per-capita #27
Cognitive Power
27.3
of 100 · #35
CPI
27.3
CC
52.5
ADI
58.0
CDI
0.526
AIR
45.7
AIX
37.4
CRI
42.3
CM
38.4
CV
3.2%
Primary · absolute lens
Cognitive Power
27.3 · #35/50
Total national capacity, AI capital, compute, models & robotics. Strongest in robotics & automation, researchers (total), economic mass.
Per-capita lens
Cognitive Coefficient
52.5 · #27/50
How deep infrastructure runs per person, rewards intensity, like GDP-per-capita or the HDI.
Agentic Deployment Index, the army of agents
Czechia's agentic capacity
58.0
ADI · #27/50
How many capable, autonomous agents this society can field, how intelligent they are, and what they are permitted to do, across citizens, business and government. An automated army of agentic problem-solvers may be the single clearest near-term lever of cognitive advantage.
Agent capability (intelligence)68.0
Access, citizens57.0
Autonomy & reach (what agents can do)63.0
Business deployment45.0
Government deployment50.0
CC projection to 2040, Baseline
All 8 scenarios →Monte-Carlo band (P10-P90) around the median path. Dashed = illustrative historical reconstruction.
Median (P50)P10-P90 bandReconstruction
Pillar profile
Country report · generated from Czechia's indices & economy
Czechia, cognitive standing in full
Czechia is the world's #40 economy by GDP ($391bn) and ranks #35/50 on absolute Cognitive Power and #27/50 on per-capita intensity. No index ranks as a clear strength across the headline indices; its trajectory is Falling behind, growing more slowly than the frontier. The binding concern is cognitive power index.
Economic & scale context
The economic base AI capacity is built on, and how its scale interacts with the cognitive indices.
GDP (total)
$391bn
#40 of 50
GDP / capita
$60,487
High income · PPP
Population
11M
AI investment
$0.5bn
#38 globally
R&D intensity
2.00%
of GDP
Index-by-index read
For each index: what it means when high (the value) versus low (the risk), and Czechia's own standing.
| Index | Value | Standing | What a high vs low value means, and where Czechia sits |
|---|---|---|---|
| CPI Cognitive Power Index | 27.3 | Developing · #35/50 | Low here, limited absolute weight: a price-taker that depends on capacity built elsewhere. ▲ high: great-power scale: the country can shape the frontier, attract capital and talent, and set standards others follow · ▼ low: limited absolute weight: a price-taker that depends on capacity built elsewhere |
| CC Cognitive Coefficient | 52.5 | Moderate · #27/50 | Mid-pack. High would mean deep capability per person: broad productivity gains and a well-equipped workforce; low would mean thin per-person depth: gains stay concentrated and most of the workforce is under-equipped. ▲ high: deep capability per person: broad productivity gains and a well-equipped workforce · ▼ low: thin per-person depth: gains stay concentrated and most of the workforce is under-equipped |
| ADI Agentic Deployment Index | 58.0 | Moderate · #27/50 | Mid-pack. High would mean an automated-agent workforce multiplier is already in place across society; low would mean agents are under-deployed, the clearest near-term productivity lever is being left on the table. ▲ high: an automated-agent workforce multiplier is already in place across society · ▼ low: agents are under-deployed, the clearest near-term productivity lever is being left on the table |
| CDI Cognitive Development Index | 0.526 | Moderate · #27/50 | Mid-pack. High would mean broad, balanced development with no single dimension dragging it down; low would mean imbalance or underdevelopment, at least one dimension is a binding weakness. ▲ high: broad, balanced development with no single dimension dragging it down · ▼ low: imbalance or underdevelopment, at least one dimension is a binding weakness |
| AIR AI Readiness | 45.7 | Moderate · #26/50 | Mid-pack. High would mean institutions and infrastructure are ready to convert capability into real deployment; low would mean governance, data or infrastructure gaps will slow how fast AI can actually be used. ▲ high: institutions and infrastructure are ready to convert capability into real deployment · ▼ low: governance, data or infrastructure gaps will slow how fast AI can actually be used |
| AIX Augmentation Index | 37.4 | Moderate · #28/50 | Mid-pack. High would mean AI is actively in use across the workforce, so capability is being turned into value; low would mean access without adoption, the tools exist but are under-used, so the value is unrealised. ▲ high: AI is actively in use across the workforce, so capability is being turned into value · ▼ low: access without adoption, the tools exist but are under-used, so the value is unrealised |
| CRI Cognitive Resilience Index | 42.3 | Moderate · #29/50 | Mid-pack. High would mean strategic autonomy: a broad home-grown base that is hard to cut off or coerce; low would mean dependency risk: heavy reliance on frontier infrastructure others control, a strategic vulnerability. ▲ high: strategic autonomy: a broad home-grown base that is hard to cut off or coerce · ▼ low: dependency risk: heavy reliance on frontier infrastructure others control, a strategic vulnerability |
| CM Cognitive Momentum | 38.4 | Moderate · #28/50 | Mid-pack. High would mean investment and pipeline favour sustained improvement, today's position is likely to compound; low would mean a weak pipeline, the current position may stall or erode without new investment. ▲ high: investment and pipeline favour sustained improvement, today's position is likely to compound · ▼ low: a weak pipeline, the current position may stall or erode without new investment |
| CV Cognitive Velocity | 3.2% | Moderate · #30/50 | Mid-pack. High would mean improving quickly relative to a moving frontier; low would mean slow improvement, at risk of drifting backwards in relative terms as the frontier advances. ▲ high: improving quickly relative to a moving frontier · ▼ low: slow improvement, at risk of drifting backwards in relative terms as the frontier advances |
Trajectory & growth potential
Trajectory & growth potential
Falling behindCzechia's trajectory is Falling behind, growing more slowly than the frontier. Risk: the relative gap is widening, a compounding disadvantage that can pull capital and talent toward leaders and deepen dependency. The clearest call to act.
Momentum (CM): 38.4 #28/50
Velocity (CV): 3.2% #30/50
Closing the gap: -1.79 pts/yr vs the frontier
Projected CC by 2040: 69.8 (now 52.5, P50 · scenario not forecast)
Strengths to build on
- No standout strengths across the headline indices yet.
Risk factors
- Relative trajectory, Falling behind: Risk: the relative gap is widening, a compounding disadvantage that can pull capital and talent toward leaders and deepen dependency. The clearest call to act.
- Affluent but under-utilizing, a high-income economy (GDP/cap $60,487 PPP) whose AI utilization (AIX 37.4) lags its means.
- Binding weakness, Cognitive Power Index 27.3 (#35/50, Developing): limited absolute weight: a price-taker that depends on capacity built elsewhere.
Recommended priorities
- Prioritise the weakest pillar (Compute) to lift the geometric-mean development index (CDI).
- Convert access into utilization: workforce AI-skilling and agentic-workflow adoption.
- Broaden the base (education + data governance) to raise resilience and reduce concentration risk.
▲
These are model outputs and scenarios, not forecasts of actual outcomes. This platform measures access to, utilization of, and leverage from cognitive infrastructure, not intelligence. No causality or certainty is claimed.
Pillars
AI Access54.8
AI Adoption32.1
Compute26.0
Data79.4
Education65.3
Digital Literacy68.3
Innovation46.8
Knowledge Infrastructure37.8
Agentic Deployment62.1
Nearest peers
- Portugal51.6
- Italy53.5
- Poland50.0
- Spain55.7
- New Zealand56.3
Provenance & what changed · 39 indicator cells · year, source, measured vs estimated
Rank movement
- no rank movement since the previous snapshot
Applied research changes
- no applied research changes name this economy in the window
| Indicator | Value | Year | Source | Kind |
|---|---|---|---|---|
| agent_access_pct | 57.0 | 2025 | curated (agentic-deployment estimate) | estimated |
| agent_autonomy_idx | 63.0 | 2025 | curated (agentic-deployment estimate) | estimated |
| agent_capability_idx | 68.0 | 2025 | curated (agentic-deployment estimate) | estimated |
| ai_capital_b | 3.0 | 2024 | Stanford AI Index / state & infra capex (curated) | estimated |
| ai_index_score | 29 | 2024 | Tortoise Global AI Index 2024 | estimated |
| ai_private_investment_b | 0.5 | 2024 | Stanford AI Index 2025 (cumulative) | estimated |
| ai_pubs_share | 0.6 | 2023 | OECD / Elsevier | estimated |
| ai_regulation_idx | 70 | 2024 | EU AI Act/curated | estimated |
| ai_skill_penetration | 58 | 2024 | LinkedIn/Stanford AI Index 2025 | estimated |
| ai_strategy_year | 2019 | 2019 | OECD AI Policy Observatory | measured |
| ai_talent_idx | 46 | 2024 | Tortoise Global AI Index 2024 | estimated |
| cloud_regions | 1 | 2024 | AWS/Azure/GCP region maps 2024 | measured |
| data_governance_idx | 81 | 2024 | OECD/GDPR/curated | estimated |
| datacenter_mw | 250 | 2024 | curated estimate | estimated |
| digital_skills_idx | 66 | 2024 | WEF / ITU-style composite | estimated |
| enterprise_agent_adoption | 45.0 | 2025 | curated (agentic-deployment estimate) | estimated |
| fixed_broadband_per100 | 30 | 2023 | ITU 2023 | measured |
| gdp_pc_ppp | 60487.0 | 2025 | World Bank WDI (PPP) | measured |
| gdp_total_b | 391.0 | 2025 | World Bank WDI | measured |
| gii_score | 46 | 2024 | WIPO Global Innovation Index 2024 | measured |
| gov_agent_adoption | 50.0 | 2025 | curated (agentic-deployment estimate) | estimated |
| gov_ai_readiness | 68 | 2024 | Oxford Insights Gov AI Readiness 2024 | measured |
| human_capital_index | 0.75 | 2020 | World Bank HCI 2020 | measured |
| internet_pct | 92 | 2023 | ITU 2023 | measured |
| mean_years_schooling | 12.9 | 2022 | UNDP HDR 2023/24 | measured |
| mobile_broadband_per100 | 105 | 2023 | ITU 2023 | estimated |
| notable_models | 0 | 2024 | Stanford AI Index 2025 | estimated |
| patents_per_million | 70 | 2023 | WIPO | estimated |
| population_m | 10.9 | 2025 | World Bank WDI | measured |
| rd_gdp_pct | 2 | 2022 | OECD / Eurostat | measured |
| researchers_per_million | 4150 | 2021 | UNESCO UIS / OECD | measured |
| robot_density | 160.0 | 2024 | IFR World Robotics | estimated |
| robot_installs_k | 2.8 | 2024 | IFR World Robotics | estimated |
| robot_stock_k | 22.0 | 2024 | IFR World Robotics | estimated |
| sci_pubs_per_million | 1500 | 2022 | World Bank WDI | estimated |
| semiconductor_idx | 30 | 2024 | curated estimate (Onsemi) | estimated |
| stem_grads_share | 26 | 2020 | OECD/Eurostat | measured |
| tertiary_enrollment_pct | 67 | 2021 | UNESCO UIS | measured |
| top500_systems | 2 | 2024 | Top500 2024 | measured |