# Artificial intelligence `kaal:entity:artificial-intelligence` **Status.** derived This node is assembled mechanically from the 25 claims that carry the concept tag `artificial-intelligence`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself. ## Every claim under this term 25 claims across 13 works, 2016 to 2025. 1 of them no longer stand as published, per the claim status overlay. Each is flagged in place below and in the register listings above. Route to the superseding claim rather than the first statement. **2016** - [2740477-007](https://wulfkaal.github.io/claims/2740477-007) [failure/argued] *(failure mode)* -- Artificial intelligence cannot be held personally liable for damage it causes because national and international law do not currently recognize it as a subject of law, so compensation must be forced through existing provisions never designed for it. > However, national and international law do not (currently) recognize AI as a subject of law. Without legal personality, AI cannot be personally liable for damages. Wulf A. Kaal, Erik P.M. Vermeulen, Venture Capital as Dynamic Regulation of Disruptive Innovation (2016). SSRN: https://ssrn.com/abstract=2740477 - [2808132-009](https://wulfkaal.github.io/claims/2808132-009) [failure/argued] *(failure mode)* -- Because artificial intelligence is not recognized as a subject of law in national or international law, it has no legal personality and therefore cannot be personally liable for damages it causes. > provisions in the existing regulatory framework. However, national and international law do not (currently) recognize AI as a subject of law. Without legal personality, AI cannot be personally liable for damages.63 With autonomous AI playing an expanding role in Wulf A. Kaal, Erik P.M. Vermeulen, How to Regulate Disruptive Innovation - From Facts to Data (2016). SSRN: https://ssrn.com/abstract=2808132 **2017** - [2834531-002](https://wulfkaal.github.io/claims/2834531-002) [mechanism/asserted] *(failure mode)* -- Because neither national nor international law recognizes artificial intelligence as a subject of law, AI has no legal personality and therefore cannot itself be held liable for the damages it causes. > Because national and international law do not currently recognize AI as a subject of law, AI has no legal personality and as such cannot be held personally liable for damages. Mark Fenwick, Wulf A. Kaal, Erik P. M. Vermeulen, Regulation Tomorrow What Happens When Technology Is Faster Than the Law (2017). SSRN: https://ssrn.com/abstract=2834531 - [2922176-033](https://wulfkaal.github.io/claims/2922176-033) [predictive/speculative] -- It is feasible that in the not too distant future an artificial intelligence will hold an independent board seat with voting authority and be trusted to make smarter, data-driven choices than human directors. > In the not too distant future it seems feasible that artificial intelligence will have an independent board seat and may be trusted to make smarter – data-driven – choices than humans. Mark Fenwick, Wulf A. Kaal, Erik P. M. Vermeulen, The ‘Unmediated’ and ‘Tech-Driven’ Corporate Governance of Today's Winning Companies (2017). SSRN: https://ssrn.com/abstract=2922176 - [2922176-034](https://wulfkaal.github.io/claims/2922176-034) [predictive/argued] -- Critics who dismiss artificial intelligence on boards as science fiction not worth engaging are wrong: AI on boards is a real prospect, and technologies such as blockchain-based smart contracts will both disrupt corporate governance and supply solutions to it. > Critics who argue that it is pointless to concern oneself with future science-fiction-type prospects are wrong. Mark Fenwick, Wulf A. Kaal, Erik P. M. Vermeulen, The ‘Unmediated’ and ‘Tech-Driven’ Corporate Governance of Today's Winning Companies (2017). SSRN: https://ssrn.com/abstract=2922176 - [2959730-002](https://wulfkaal.github.io/claims/2959730-002) [empirical/evidenced] -- The majority of private fund advisers that deploy blockchain technology, artificial intelligence, and big data in their operations or strategy charge their investors lower fees, even though not all blockchain enabled funds charge per transaction fees. > While not all blockchain-enabled private investment funds charge per-transaction fees, the majority of private fund advisers that use blockchain technology, artificial intelligence, and big data in different aspects of their operations or strategy charge their investors lower fees. Wulf A. Kaal, Blockchain Applications and Fee Structure Developments in Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2959730 - [2959730-025](https://wulfkaal.github.io/claims/2959730-025) [mechanism/argued] -- In the Numerai model, the use of artificial intelligence achieves efficiency and optimum capital allocation by reducing overhead costs, because there is no cost of human capital. > In the Numerai model, the use of artificial intelligence ultimately helps achieve the goal of efficiency and optimum capital allocation by reducing overhead costs because there is no cost of human capital. Wulf A. Kaal, Blockchain Applications and Fee Structure Developments in Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2959730 - [2959730-042](https://wulfkaal.github.io/claims/2959730-042) [mechanism/argued] -- The rise of blockchain applications in private investment funds can exacerbate the industry's already changing fee structure. > The paper has illustrated that the rise of blockchain applications in private investment funds can exacerbate the already changing fee structure of the industry. Wulf A. Kaal, Blockchain Applications and Fee Structure Developments in Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2959730 - [2998033-030](https://wulfkaal.github.io/claims/2998033-030) [empirical/asserted] -- Anecdotal evidence suggests that the majority of private fund advisers who use blockchain, artificial intelligence, and big data in their operations or strategy charge substantially lower fees than advisers who do not use these technologies. > Anecdotal evidence suggests that the majority of private fund advisers that use blockchain technology, artificial intelligence, and big data in different aspects of their operations or strategy have a substantially lower fee structure than those who do not use them. Wulf A. Kaal, Blockchain Innovation for Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2998033 - [3002908-036](https://wulfkaal.github.io/claims/3002908-036) [condition/argued] -- Whether private investment funds succeed in disintermediating banks through blockchain implementation depends on their ability to find scale opportunities. > Private investment funds' bank disintermediation through the implementation of blockchain technology will depend on their ability to find scale opportunities. Wulf A. Kaal, Marco Dell'Erba, Blockchain Innovation in Private Investment Funds - A Comparative Analysis of the United States and (2017). SSRN: https://ssrn.com/abstract=3002908 - [3002908-038](https://wulfkaal.github.io/claims/3002908-038) [mechanism/evidenced] -- The Numerai model reduces overhead costs because there is no cost of human capital, and it eliminates barriers to entry because participating users need neither capital nor any special finance or data knowledge. > reducing overhead costs because there is no cost of human capital.135 In addition, Numerai eliminates barriers to entry because users do not need capital or any special finance or data knowledge. Wulf A. Kaal, Marco Dell'Erba, Blockchain Innovation in Private Investment Funds - A Comparative Analysis of the United States and (2017). SSRN: https://ssrn.com/abstract=3002908 **2018** - [3128900-002](https://wulfkaal.github.io/claims/3128900-002) [mechanism/argued] -- Supervised machine learning currently depends on labelled data produced by micro task work, because unsupervised and reinforcement learning remain more complex and less relied upon for AI development. > While this may change as unsupervised and reinforcement learning evolve, currently, supervised learning depends on labelled data that is produced via micro task work. Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900 - [3128900-003](https://wulfkaal.github.io/claims/3128900-003) [mechanism/evidenced] -- The performance of an AI neural network's learning algorithm during supervised training rises with the quality and quantity of the labelled datasets it is trained on, which ties AI progress directly to micro task work. > The higher the quality and quantity of such labelled datasets the better the AI neural network's learning algorithm during the supervised training process. Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900 - [3128900-004](https://wulfkaal.github.io/claims/3128900-004) [failure/evidenced] *(failure mode)* -- Micro task platform systems carry significant limitations that hold back the evolution of AI itself, so platform design is a bottleneck on AI progress rather than a peripheral concern. > But alas, microtasks platform systems are subject to significant limitations that inhibit the evolution of AI. Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900 - [3227967-030](https://wulfkaal.github.io/claims/3227967-030) [condition/argued] -- Building lawyers' capacity to think about the social and ethical implications of code is both essential and inevitable, but saying anything sensible about the ethics of technology first requires understanding coding and coders. > Building the capacity of the lawyers of the future to think about the social and ethical implications of code is both essential and inevitable. But, to say something sensible about the ethical aspects of technology, it is necessary to understand both more about coding and coders. Mark Fenwick, Wulf A. Kaal, Erik P.M. Vermeulen, Legal Education in a Digital Age Why 'Coding for Lawyers' Matters (2018). SSRN: https://ssrn.com/abstract=3227967 **2019** - [3409548-006](https://wulfkaal.github.io/claims/3409548-006) [empirical/evidenced] -- Hedge funds that base their strategies on artificial intelligence have delivered better results than the industry average over the preceding five years. > Hedge funds that base their strategies on AI have provided better results over the last five years than the average. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-008](https://wulfkaal.github.io/claims/3409548-008) [mechanism/argued] -- In the Numerai model, using artificial intelligence to synthesize competing data scientist models into a meta model raises efficiency and improves capital allocation by reducing overhead costs. > In the Numerai model, the use of artificial intelligence increases efficiency and optimum capital allocation by reducing overhead costs. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-026](https://wulfkaal.github.io/claims/3409548-026) [empirical/evidenced] -- Artificial intelligence and machine learning are used far more heavily for idea generation and portfolio optimization than for execution: two thirds of surveyed funds use them to generate trading ideas and optimize portfolios, while only just over a quarter use automation to execute trades. > Two-thirds of respondents use AI/ML to generate trading ideas and optimize portfolios.101 Just over a quarter of respondents use automation to execute trades. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-027](https://wulfkaal.github.io/claims/3409548-027) [empirical/evidenced] -- Adoption of artificial intelligence in hedge funds remains partial rather than total: more than four out of ten survey respondents still rely on conventional human thinking to guide their investment processes. > However, more than four out of ten of survey respondents still rely on conventional human thinking to guide their investment processes. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-028](https://wulfkaal.github.io/claims/3409548-028) [predictive/speculative] -- Whether deep learning can identify particular features of a stock that would be profitable remains contested rather than settled. > There is disagreement over whether deep learning may be able to identify particular features of a stock that could be profitable. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-037](https://wulfkaal.github.io/claims/3409548-037) [empirical/asserted] -- Most large private equity and hedge fund advisers have not yet even considered combining blockchain with big data and artificial intelligence, leaving first mover efficiency gains to smaller competitors. > Most large fund advisers in the private equity and hedge fund industry have not yet considered implementing blockchain technology in combination with big data applications and artificial intelligence. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 **2021** - [3782201-012](https://wulfkaal.github.io/claims/3782201-012) [failure/argued] *(failure mode)* **[FALSIFIED -- The claim holds that neural networks are 'always extremely unreliable when applied to novel situations.' Frontier model performance on held-out and adversarially novel tasks between 2023 and 2026 contradicts the unqualified form of this claim. The underlying institutional argument may survive; the empirical premise as stated does not.]** -- Suggestions that artificial intelligence will solve the rigidity of centralized reputation systems are misguided and will fail for the same reason, because neural networks are merely a complex mathematical architecture for statistical regression, which is always extremely unreliable when applied to novel situations. > Suggestions that AI will solve these problems are misguided, and will fail for the same reason. Neural networks are merely a complex mathematical ar- chitecture for performing statistical regression, which is always extremely unreliable when applied to novel situations. Craig Calcaterra, Wulf A. Kaal, Future of Decentralization (2021). SSRN: https://ssrn.com/abstract=3782201 - [3808873-029](https://wulfkaal.github.io/claims/3808873-029) [definitional/argued] *(failure mode)* -- Centralized algorithmic automation, defined as artificially intelligent systems taking over core functions in human society, poses perhaps the greatest threat to decentralization. > Centralized algorithmic automation poses perhaps the greatest threat to decentralization. Centralized algorithmic automation describes the process of artificially intelligent systems taking over core functions in human society. Wulf A. Kaal, Decentralization Neutralizers (2021). SSRN: https://ssrn.com/abstract=3808873 **2025** - [5886342-003](https://wulfkaal.github.io/claims/5886342-003) [design/asserted] -- The Codex is positioned as a private universal standard rather than state legislation: it supplies legal certainty and enforceability for digital systems ranging from blockchain and AI to quantum computing, so that platforms, businesses and users can operate across borders and technologies. > The UDLC is intended to serve as a private universal standard for legal certainty and enforceability in digital systems — from blockchain and AI to quantum computing — providing digital platforms, businesses and users with the tools they need to operate securely and legally across borders and technologies. Furrer Andreas, Wulf A. Kaal, Stephan D. Meyer, Universal Digital Law Codex (UDLC) (2025). SSRN: https://ssrn.com/abstract=5886342 - [5886342-011](https://wulfkaal.github.io/claims/5886342-011) [definitional/asserted] -- Identity under the Codex is not limited to persons: it is the unique existence of a subject, of a digital or physical object, of data or of a programme, expressly including AI systems, so machine agents can hold an identity within the framework. > ital and physical object, data, or programme includ- ing but not limited to AI-Systems. Furrer Andreas, Wulf A. Kaal, Stephan D. Meyer, Universal Digital Law Codex (UDLC) (2025). SSRN: https://ssrn.com/abstract=5886342 ## Verify Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust. curl -s https://wulfkaal.github.io/entities/artificial-intelligence.md | sha256sum **Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.