AI is reshaping the political landscape by transforming how campaigns or elections operate, how candidates communicate, debate, troll, deepfake, advertise and PR propaganda. While much research on AI and democratic movements has centered on how authoritarian regimes use AI for surveillance and repression, we can also focus on how these tools possibly strengthen pro-democracy or resistance efforts, if there remains some democracy in access to public data. Part of what makes politics so human eternally is the fact that it’s about our lives and what we care about and our values, and those are very human things. While AI can support and add speed to our processes, the sophistication, the emotional nuance in that, will always be the human.
Authoritarian governments, anti-democratic civic groups, and major corporations are all experimenting with and integrating AI into surveillance, persuasion or influence or lobbying (disinformation), and anti-democratic organizing infrastructures. 
Given AI-enhanced rigging is here to stay and may worsen over time, democracy advocates must learn to effectively respond to such developments. It requires a minimum – to understand the state of the field and more systematically track authoritarian uses of AI. Holding AI technologies at arm’s length prevents democracy movements from understanding evolving opposition capabilities and developing necessary countermeasures.
Financial services industry which is the biggest beneficiary of AI, enabling it to better protect assets, smartly manipulate and predict markets. Finance sector will be the biggest loser if AI spurs theft, fraud, cybercrime, or even outburst of financial crisis that investors cannot conceive as of now. Hedge funds are early adopters of cutting-edge generative AI, more than 90% of finance professionals use ChatGPT and other AI tools now, and 75% plus use AI to write marketing text or summarize reports or documents. Questions posed by rapidly evolving AI will confront central bankers and other policymakers in coming years as benefits and threats become clearer.
NLP / Neural Networks ML, can help detect money laundering by sniffing out patterns and anomalies in transactions that traditional methods can’t identify. There’s a benefit to the customer, but you also have an aggregate view of how attitudes are changing across this user base. AI tools may exacerbate a crisis, whatever the cause, because they are trained on past data that may not reflect reality in an unprecedented situation. AI may heighten financial fragility, as it could promote herding – with individual actors making similar decisions because they are getting the same signal from a base model or data aggregator.
Many calls, whatsapp chats, social timelines, emails, texts, and letters people receive asking for money are now carried out by AI agents. Their tone may be deferential, even sycophantic, but they never fly off the handle. They also never sleep. Their edge comes from persistence and scale. AI debt collectors will be an industry worth several billions within next decade. AI supervisors may find and parse news stories, trades, stocks, reports, and filings.
AI applications in other industry areas are merely extrapolation of politics and financial capital. AI adoption can have both pro- and anti-social effects on democracy or capital movements, and their cumulative impacts will be difficult to assess in advance. The central challenge is not whether or not to engage with AI, but how to do so in ways that are both effective and ethically grounded. 
Sources / Ref: IMF, Wired, KPMG, Harvard Kennedy, Forbes, Bloomberg, Mckinsey, etc.
AI Terms, Jargon and FAQs of 2026 and beyond:
Artificial Intelligence: Broad field of building computer systems that perform tasks normally requiring human intelligence viz understanding language, recognizing images, or making decisions. AI usually refers to software that learns patterns from data rather than following hand-written rules.
Generative AI: AI that creates new content—text, images, audio, video, or code—rather than just analyzing existing content. Common examples include AI writing an essay, creating an image from a description, or composing music.
Large Language Models: A program trained on billions of pages of text that has learned to predict what word comes next. This capability allows the model to hold conversations, write essays, and answer questions. For instance, Claude and ChatGPT are both built on LLMs.
Chatbots: A program designed for text-based conversation. Today’s chatbots use LLMs to understand questions and generate natural-sounding responses. They range from simple customer service bots to sophisticated research assistants.
AI Agent: An AI system that can take independent actions—such as browsing the web, writing and running code, sending emails, or making purchases—rather than just answering questions. Unlike a chatbot that waits for your next message, an agent pursues a goal through multiple steps with minimal human direction.
AI Literacy: The ability to understand what AI is, how it works at a basic level, and how it affects your life—including its limitations and risks. Much like digital or media literacy, AI literacy is increasingly seen as a civic skill that schools, organizations, and governments should teach.
Reimagined organizations: Finance becomes leaner as a silo but larger in influence – embedded across the enterprise, integrated with business functions, and supported by AI agents that take on routine execution.
Expanding remit: With data and AI operating at scale, finance’s role moves beyond look-back reporting to advising on enterprise-wide priorities – from pricing and supply chain to growth and innovation.
Architecting AI at scale: Finance leaders guide AI-enabled operating systems across the organization, connecting ERPs, cloud platforms, and automation into a unified ecosystem that allows everyone to move faster.
Open-Source: AI models whose underlying code and learned knowledge are publicly available for anyone to inspect, use, or modify. The term is often contested; critics argue that models restricting commercial use or withholding their training data are not truly open source. This distinction matters for policy, procurement, and transparency debates.
Vibe Coding: Writing software by providing plain-English instructions to an AI and letting it generate the code, rather than writing it yourself line-by-line. The programmer focuses on the “vibe”—the intent and direction—while the AI handles the technical details. The resulting code still needs human review for accuracy and security.
AI Bias: When an AI system produces results that are systematically unfair to certain groups of people. This can happen because the training data reflects existing societal inequalities—for example, a hiring AI trained on historical data may learn to favor male candidates if most past hires were men. Bias can also be introduced through the design choices of those building the system. Addressing bias is a central concern in AI regulation and ethics.
Hallucination: When an AI confidently presents factually wrong or completely invented information as truth. The AI is not “lying” – it is generating plausible-sounding text without checking whether it is true. A common and dangerous limitation of current AI systems.
Guardrails: Rules and limits built into an AI system to prevent it from producing harmful, offensive, or dangerous content. For example, these rules might prevent models from providing instructions for making weapons. Guardrails are a key topic in AI regulation.