Alexander Good

Alexander Good is the founder of Post Fiat, an XRP-derived Layer 1 blockchain network focused on capital markets and collective intelligence that is live on a public testnet and uses PFT as its native asset.^[7][8] Alexander's background in finance and technology, coupled with his work on validator-selection research and AI-assisted coordination mechanisms, has positioned him as a figure in discussions about evolving digital economies. He also writes extensively on artificial intelligence, markets, and institutional change on his research site, goodalexander.com.^[3]

Early Life & Education

Alexander Good earned a Bachelor of Science in Finance and Financial Management Services from the Wharton School of the University of Pennsylvania, studying there from 2006 to 2010.^[4]​^[2]​

Career

Citi

Alexander Good worked at Citi as a Sales and Trading Analyst from July 2010 to April 2012 in the New York City Metropolitan Area. His work included foreign exchange markets, providing experience in financial market operations and analysis, which served as the basis for later work with digital assets.

Palantir Technologies

From May 2012 to December 2013, Good worked at Palantir Technologies as a Deployment Strategist in the New York City Metropolitan Area. His work involved the use of data analytics and predictive methods in the analysis of capital markets.

Balyasny Asset Management

Good joined Balyasny Asset Management as an Analyst in May 2014 and remained in the position until January 2016. He was based in the New York City Metropolitan Area.

Perpetua Labs

Good co-founded Perpetua Labs in February 2016 and served as Co Founder until September 2017. The company evolved into an e-commerce advertising software provider focused on marketplaces such as Amazon and Instacart, offering tools for digital advertising and market analysis within those ecosystems.^[4]​

In 2021, Ascential acquired Perpetua for $150 million, and the business was later sold to Omnicom as part of the Flywheel Digital transaction.

Post Fiat

Good founded Post Fiat in November 2024. The project is described as an XRP-derived Layer 1 blockchain network for capital markets and collective intelligence, keeping XRPL-style fast settlement while adding privacy-enabled workflows and a distinct validator-selection framework.^[7]​^[8] Its infrastructure includes an ontology designed for AI agents to interact with participants in distributed capital markets and financial workflows.

Post Fiat is live on a public testnet. Publicly accessible components include a validator-history service, a testnet explorer, a public whitepaper, a validator benchmark, and the Task Node, which serves as the main contributor-participation surface by allowing community members to participate in tasks and validator operations rather than only holding the network token.^[7] The network is framed as infrastructure for model-assisted coordination across financial workflows, with validator behavior and selection made observable through published research and benchmarks.^[8]​

The native asset of the network is PFT, which is described as coordinating validators, messaging, privacy-enabled workflows, and Task Node participation. PFT has a fixed supply of 100 billion tokens and follows a non-inflationary, XRP-style fee model in which transaction fees are destroyed rather than paid to validators, so that validator incentives derive from participation in the broader ecosystem rather than direct fee revenue.

Good's work at Post Fiat has included research into replacing opaque validator-list curation with an auditable, replayable, AI-assisted validator-selection pipeline, in which validator lists are scored under published policies and produced in a way that can be independently replayed and challenged.^[8] The project emphasizes published validator-selection research, privacy-enabled workflows on an XRPL-derived stack, and the use of AI-assisted scoring to support validator-list publication, rather than treating validator-set construction as a closed, editorial process.^[7]​

Good's broader writing covers financial markets, digital economies, blockchain technology, and artificial intelligence. His published work includes the Doom Thesis and analyses of Web4 and agent economies.^[1]​^[2]​^[3]​^[4]

Talks and Interviews

AI, Markets and Crypto #01

On October 30, 2025, Good Alexander appeared on the TG Podcast, hosted by threadguy, in an interview covering artificial intelligence, cryptocurrency, financial markets, and macroeconomic developments. The discussion examined the extent to which AI had been incorporated into cryptocurrency markets and applications. According to Alexander, many proposed uses of cryptocurrency in AI had not yet reached broad adoption. He cited proof-of-work systems that perform AI inference, including Ambient, as an example of an alternative approach to combining computational workloads with cryptocurrency networks.

Alexander discussed the use of blockchain networks for securities trading, including stocks and bonds. He described private transaction systems and faster settlement as areas that could alter the structure of certain financial markets. Privacy was also discussed in relation to institutional participation, particularly the use of systems that allow transaction confidentiality while operating within regulatory requirements.

Stablecoins and their relationship with the United States financial system formed another part of the interview. Alexander linked the expansion of stablecoins to demand for U.S. government debt and to activity in cryptocurrency markets. He also argued that continued expansion would depend on sustained participation from investors and the ability of market participants to generate returns from these assets.

The conversation also covered broader economic and geopolitical subjects, including U.S. government debt, financial regulation, relations between China and the United States, and the interaction between traditional financial markets and cryptocurrency markets. Alexander described cryptocurrency as increasingly connected to conventional financial infrastructure and discussed the possibility of AI systems facilitating coordination and information exchange among market participants.

The interview concluded with a discussion of cryptocurrency's social and political dimensions. According to Alexander, cryptocurrency can be viewed not only as a financial and technological system but also in relation to individual financial autonomy and concerns about centralized economic and political institutions. These views were presented as Alexander's interpretation of the role of cryptocurrency within broader economic and social developments, and as one perspective rather than a consensus position in the field.^[5]

AI, Crypto, and AGI #02

On January 7, 2025, Good Alexander appeared on the YouTube channel threadguy in an interview covering artificial intelligence, cryptocurrency, financial markets, and the possible economic effects of artificial general intelligence (AGI). Alexander described AGI as a form of artificial intelligence capable of establishing objectives, carrying out tasks autonomously, and improving its own processes. He distinguished this model from AI agents designed to perform predefined sequences of tasks under human-specified instructions.

The interview examined the relationship between computational costs and the economic applications of AI. Alexander introduced the term “intelligence margin” to describe the relationship between the resources required to operate an AI system and the financial returns generated by its activity. He suggested that applications requiring comparatively low computational resources could become economically viable before applications involving higher computing requirements. Trading was discussed as an area in which AI could be applied to financial analysis and decision-making.

Alexander also described an experiment involving GPT-3.5 and equity-market earnings data. According to his account, he used the model to analyze the sentiment of earnings-call transcripts and compared its results with an existing trading strategy that incorporated financial information. He stated that the model-generated sentiment analysis produced better results in his testing than the version of the strategy that used financial data. He also acknowledged the possibility of training-data effects and overfitting before observing the approach across subsequent earnings periods.

Another concept discussed was “hallucination yield,” a term Alexander used to describe a difference between a cryptocurrency's market capitalization and the valuation that a language model might associate with the project based on its available information and narrative. He related the concept to the role of attention and information in cryptocurrency markets, arguing that language models could become another mechanism through which market participants encounter information about digital assets.

The interview also covered changes in the availability of information in financial markets. Alexander discussed the possibility that widespread access to AI systems could make commonly available analytical information less differentiated among market participants. He described alternative sources of differentiation involving restricted information, information-sharing networks, and mechanisms for compensating participants who contribute data or analysis.

Post Fiat was discussed in relation to these developments. Alexander described the project as an effort to combine cryptocurrency infrastructure with AI-driven systems for financial and other network activities. The discussion included the use of different types of network participants and automated systems to coordinate tasks and transactions.

The conversation further addressed the relationship between AI and cryptocurrency infrastructure. Alexander expressed the view that AI could be applied to existing blockchain networks and financial systems, rather than limiting blockchain applications to the development of AI infrastructure. The interview also covered the possible effects of AI on content creation, intellectual property, data used for model training, and economic activity in a post-AGI environment.

The interview therefore covered Alexander's views on AGI, AI applications in financial markets, cryptocurrency valuation, information economics, and the development of Post Fiat, and should be interpreted as his perspective rather than a settled consensus in the field.^[6]

Apes Together Strong — TOKEN2049 Singapore 2025

On October 1, 2025, Alexander Good delivered a keynote talk titled “Apes Together Strong” on the OKX Main Stage at TOKEN2049 Singapore, speaking in his capacity as CEO and founder of Post Fiat.^[10] In the presentation he discussed AI adoption, retail investor coordination, and cryptocurrency markets, outlining a view in which hive-mind-style coordination among retail participants, supported by AI tools and public blockchains, could change competitive dynamics between retail traders and institutions. He argued that AI-enabled retail coordination platforms could, under some conditions, outperform traditional passive index-investing strategies and described Post Fiat as an XRP-derived Layer 1 intended to coordinate AI agents and human participants involved in such networks. The keynote reflects Good's own perspective on these developments and does not represent a consensus position in the broader field.

The Crypto Endgame — TOKEN2049 Singapore 2025

In September 2025, Good appeared as a panelist on “The Crypto Endgame” at TOKEN2049 Singapore, joining other industry participants in a discussion of macro-level outcomes for digital assets.^[11] Speaking as CEO of Post Fiat, he commented on potential central bank digital currency (CBDC) designs, the tokenization of corporate assets, and how governments might deploy CBDCs to address concerns over monetary sovereignty. He suggested that major central banks could introduce CBDCs with integrated rails for tokenized assets during the mid-2020s, leading to an initial period of coexistence between CBDCs and public-chain crypto markets before possible tensions emerge as debt burdens and regulatory pressures increase. The views he expressed in the panel represent his interpretation of these scenarios rather than a definitive forecast or consensus among practitioners.