The Complete Guide to Using AI in the Government Industry in Taiwan in 2025

Taiwan treats AI as national infrastructure: from the Taiwan AI Action Plan and MODA's AI Evaluation Center to a pending Draft AI Act, planners are pushing a risk‑based, standards‑focused approach so government services can scale safely - spotting tax evasion, speeding pandemic tracing and even surfacing sentencing trends in court records - while industrial policy builds chips, supercomputing and regional AI labs to turn the island into a global AI hub.

The government's 2040 vision sets bold targets - massive workforce training and large‑scale projects such as the

Ten Major AI Infrastructure Projects

- because the payoff is both civic (faster, fairer services) and strategic (semiconductor‑backed AI sovereignty).

Read the legal and regulatory context in Lee & Li's practice guide on Taiwan's Draft AI Act and public deployments and see the national strategy goals and workforce targets in the 2025 hub plan.

Lee & Li practice guide on Taiwan Draft AI Act and government AI programs, Taiwan national AI hub strategy and workforce targets (2025).

Taiwan's AI strategy reads like an industrial playbook and a civic roadmap rolled into one: government policy funnels talent, chips and compute into targeted priorities - education programmes such as “Befriended with AI,” the AI on Chip Taiwan Alliance to lock in semiconductor advantages, and big‑iron projects like the TAIWANIA 2 supercomputer that made possible TAIDE, a localised large language model refined on public records including Judicial Yuan decisions and Constitutional Court interpretations and even trained to handle Taiwanese and Hakka - all designed so AI grows here on native data and trusted infrastructure.

At the same time regulators are busy building the scaffolding: the NSTC's draft AI Basic Act and MODA's AI Evaluation Centre aim for a risk‑based, standards‑forward approach that keeps innovation doors open while setting certification, transparency and data‑governance rules.

Industry alliances (AITA, the new AI Innovation Application Alliance) and R&D hubs mean multinational labs and homegrown startups can plug into testbeds and sandboxes.

The result is a purposeful ecosystem where chips, models, data and rulemaking are coordinated - imagine judges' rulings becoming training signals for a model that can explain local law in Hakka - a concrete example of why Taiwan's strategy is as much about civic capacity as commercial edge (see Lee & Li's legal overview and the Global Practice Guide on Taiwan AI for more detail).

“Early communication with stakeholders is crucial.”

The new Draft AI Basic Act has been Taiwan's headline policy for 2024–25: first published by the NSTC in July 2024 (opened to a 60‑day public consultation) as a principles‑driven “basic law” to put seven core values - sustainable development, human autonomy, privacy and data governance, security, transparency, fairness and accountability - at the centre of national AI policy, and since then it has been iterated by party caucuses and moved through Executive Yuan and legislative reviews with competing drafts from the DPP and KMT and sectoral amendments aimed at deepfakes, fraud and data governance.

Key architecture in the bill gives MODA and sector regulators a risk‑classification role, encourages regulatory sandboxes and evaluation tools (linked to MODA's AI Evaluation Centre), and pushes data‑opening measures that will underpin models like TAIDE - but civil society groups (TAHR, Judicial Reform Foundation) warn the draft lacks concrete enforcement, could clash with the PDPA, and may leave too‑wide exemptions for “development” activity.

The practical upshot for government teams: prepare for a phased, risk‑based rollout rather than a single rulebook, watch MODA's risk framework, and follow the evolving debates captured in legal briefs and policy explainers such as the NSTC Draft AI Basic Act regulatory framework and civil-society critiques - Lexology and the Taiwan AI strategy, regulation and legal framework explainer - Law.asia, as well as detailed timelines and policy analysis at Lee & Li.

Compute and data are the engine behind Taiwan's sovereign‑AI ambitions: the National Center for High‑Performance Computing's TAIWANIA 2 already stitches together 252 nodes (8 GPUs per node) to deliver about 9 PFLOPS while using just 798 kW and an energy efficiency of 11.285 GF/W, and that on‑ramp into government and academic projects feeds model work such as TAIDE and the Taiwan AI RAP platform for locally tuned LLMs and retrieval‑augmented services; the same national stack includes the Taiwan Computing Cloud (TWCC) that lets researchers and agencies spin up containers and Slurm jobs to train and deploy models on shared datasets and secure storage.

Newer NVIDIA‑powered systems announced in 2025 promise an order‑of‑magnitude jump in AI throughput, accelerating TAIDE's rollout of Llama3.1‑TAIDE and Nemotron‑based services for education, healthcare and epidemic monitoring - so government teams can prototype chatbots, summarizers and retrieval pipelines locally without shipping sensitive data offshore.

For practical planning, treat compute as capacity and policy: reserve scheduling windows on TWCC, design datasets to match node and GPU counts, and prioritise hybrid workflows where TAIWANIA 2 handles secure model training while newer Blackwell/H200 clusters speed large‑scale experimentation (see NCHC's TAIWANIA 2 specs and the NVIDIA announcement for the new NCHC supercomputer).

“The new NCHC supercomputer will drive breakthroughs in sovereign AI, quantum computing and advanced scientific computation.”

Regulatory responses in Taiwan lean pragmatic: the Financial Supervisory Commission's FinTech Development and Innovation and Experiment Act (the Sandbox Act) gives firms a controlled space to pilot novel services - applications are reviewed in 60 days, experiments run 12–36 months, and activity caps (NT$100 million by default, up to NT$200 million with approval) protect consumers while letting innovation scale - so AI‑powered finance pilots can test models and data flows without immediately triggering full licensing.

The sandbox framework keeps hard lines where they matter: AML/KYC, counter‑terrorism rules and PDPA obligations remain in force, cybersecurity and exit mechanisms must be baked into plans, and liability to customers can't simply be waived, even when the FSC grants targeted exemptions; see the Lee & Li overview of Taiwan's sandbox and fintech law for details.

Layered on top, sector guidance now addresses AI specifically - the FSC's AI core principles (Oct 2023) and June 2024 Guidelines set expectations on governance, fairness, privacy, security and transparency and point to risk‑based, sectoral enforcement aligned with the NSTC Draft AI Basic Act - so teams building government or financial AI services must combine sandbox discipline, robust data governance and clear audit trails to move from prototype to public deployment safely (see the Fintech 2025 practice guide for the regulatory context).

A memorable rule of thumb: treat the sandbox like a time‑limited, well‑fenced lab - powerful for learning, but designed so participants leave with obligations, not loopholes.

“Taiwan is one of the most important strategic growth markets for EMQ and the regulatory approval from the FSC [Financial Supervisory Commission] represents a significant milestone for our operation in Taiwan.”

Taiwan's government pilots show how AI moves from lab to courtroom and back again: the Judicial Yuan has pushed generative and decision‑support tools - commissioning Chunghwa Telecom to build a TMT5‑based judgment‑drafting system trained on decades of case law (1996–2021) to auto‑generate full draft judgments for judges' review - and limited early trials to straightforward case types like DUI to cut backlog while keeping judges as the final arbiters (Lexology analysis of the Judicial Yuan AI judgment-drafting system).

Complementing that operational project, an ICAROB 2025 study analysed 198 homicide verdicts to design a sentencing information system aimed at narrowing variance and helping citizen judges apply Article 57 sentencing factors consistently (ICAROB 2025 study on an AI sentencing information system).

These pilots underscore a practical trade‑off: clear efficiency gains (auto‑drafts and pattern spotting) versus real public worry about bias, training on flawed precedents, and transparency of the training corpus - issues that legal groups and the public have repeatedly flagged as tests of whether AI will augment justice or mechanically replicate past errors.

Judges' retained authority and careful, staged pilots are how Taiwan is trying to thread that needle.

“the AI system that it would use is ‘specifically trained to produce drafts for rulings,' rather than being merely a draft generator”

Industry adoption in Taiwan is being driven less by hype than by hardware: the island's chip ecosystem - led by TSMC's relentless R&D in AI‑optimized processes and near‑memory computing - plugs straight into practical AI use cases from edge devices to cloud training, creating a virtuous loop where fabs, packagers and system integrators set the de facto standards for performance and reliability; analysts describe this as a semiconductor‑AI synergy that turns Taiwan into a one‑stop supply chain for AI hardware and applications (Beaumont Capital Markets: Taiwan AI and semiconductor synergy analysis).

That integration shows up at trade halls and in government policy: SEMI Taiwan's new AI Technology Zone highlights edge servers, vision intelligence and advanced packaging by local leaders like Advantech, ASE and Egis - a concrete sign that standards (thermal design, power/efficiency, and co‑packaged optics) are being set here by manufacturers and their customers together (SEMI Taiwan AI Technology Zone overview highlighting edge servers and advanced packaging).

For government teams planning pilots, the takeaway is clear: design around the island's hardware strengths - TSMC‑grade nodes, advanced packaging and edge platforms - and lean on established partnerships with cloud and GPU partners so procurement, cooling and energy standards are aligned with device‑level innovation (TSMC research on AI hardware and near‑memory computing).

Imagine booking a 90‑minute high‑speed rail hop and seeing nearly every link in an AI supply chain in one 200‑km corridor - that geographic concentration is why Taiwan sets both the technical and practical standards for AI adoption across healthcare, smart manufacturing and public services.

Taiwan's AI story is a study in concentrated advantage and cautious vulnerability: the island has become the backbone of global AI hardware - controlling as much as 90% of AI server manufacturing capacity and anchoring an integrated ecosystem that runs from chips and advanced packaging to thermal and power systems - so strengths include unmatched speed, supply‑chain density and deep co‑design between fabs and system makers (see the Trax Technologies analysis on Taiwan's AI supply‑chain dominance and SEMI's writeup on Taiwan's competitive advantages).

That vertical mastery (and TSMC's outsized foundry role) gives Taipei real leverage - what analysts call an emerging “AI shield” that amplifies the strategic value of its semiconductor lead - but it also concentrates risk: geopolitical pressure, export controls and the recent push by OEMs to near‑shore server assembly (Mexico and the US) expose logistics, tariff and resilience weak points discussed in The Diplomat and ModernDiplomacy.

Operational limits matter too - complex, multi‑modal shipments across 147 destination countries and dozens of currencies demand sophisticated freight and data systems, and moves to spread capacity can bump into power and water constraints in new sites.

For government planners the takeaways are pragmatic: lean into Taiwan's hardware depth and standardisation, build contingency routes and diversify partners, and treat the island's dominance as a strategic asset that must be managed, not assumed.

“Taiwan is small, and Taipei is small, and in that small area everything moves super fast,” the 35-year-old Harvard graduate said after one of his trips to secure production.

Will robots take my job? In Taiwan the picture is mixed: the island already ranks high in automation density (about 177 industrial robots per 10,000 employees), so routine, clerical and manual roles are the most exposed while government first‑tier inquiries and helpdesks are already shifting to chatbots and automated workflows; see City A.M.'s breakdown of occupations at risk and Taiwan's robot density for context.

At the same time demographic pressures and a talent squeeze - 78% of employers report difficulty filling roles - mean automation is often used to plug gaps in IT operations, system monitoring and back‑office processes rather than just cut headcount, as outlined in local automation case studies.

That combination creates a clear “so what”: expect displacement in repetitive clerical, data‑entry and some manufacturing tasks, rising demand for people who can run, audit and tune automated systems, and practical government transitions where AI handles volume while humans take on complex, empathetic and supervisory work (see Nucamp's guide to at‑risk government jobs and adaptation tips).

A sensible approach is hybrid: automate the routine, retrain the workforce, and design services so machines free people for higher‑value work.

Beginners in Taiwan should approach government AI with a simple, safety-first playbook: start with low‑risk pilots (for example, first‑tier chatbots and retrieval‑augmented helpers), use a regulatory sandbox or phased rollout to test governance and exit plans, and bake PDPA‑compliant data practices into every step so consent, minimisation and breach reporting are routine rather than afterthoughts; see AmCham explainer on Taiwan's Draft AI Basic Act (risk-based AI regulation overview) for why a risk‑based, stakeholder‑driven approach matters.

Practical next steps are concrete: map where personal data enters your project, document purpose and retention, automate PDPA processes where possible (data subject requests, portability and rectification), and lean on trusted cloud or vendor controls that align with Taiwan rules (see Securiti Taiwan PDPA compliance guidance).

For hands‑on skills - prompting, tool selection and prompt‑based workflows - consider a short course that teaches workplace AI use cases and audit‑ready prompts; the AI Essentials for Work bootcamp registration (15-week AI Essentials for Work course) offers a 15‑week route to get teams productive and policy‑aware quickly.

The overarching rule: pick small, measurable projects, document decisions for traceability, engage stakeholders early, and scale only after audits, human oversight and PDPA controls are in place.

“Early communication with stakeholders is crucial.”