Alexander Good

Alexander Good is the founder of Post Fiat, a platform aimed at redefining economic frameworks in a post-fiat and AI-driven world. Alexander's background in finance and technology, coupled with his innovative approach to blockchain infrastructure, has positioned him as a significant figure in evolving digital economies. ^[1]​

Early Life & Education

Alexander Good's foundation in finance and technology was laid through his education and early professional experiences. He graduated from Wharton, where he honed his analytical skills that he would later apply to his various roles in finance and technology. ^[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 originated from work involving capital markets intelligence and developed software for advertising and market analysis, including applications within Amazon's advertising ecosystem.

In 2021, Ascential acquired Perpetua for $150 million. The company was later acquired by Omnicom.

GoodAlexander

From June 2018 to February 2025, Good worked as a Trader at GoodAlexander in Puerto Rico.

Post Fiat

Good founded Post Fiat in November 2024. The company operates a Layer 1 blockchain developed as a fork of XRP and incorporates on-chain privacy and OFAC compliance features. Its infrastructure includes an ontology designed for AI agents to interact with participants in distributed capital markets.

Post Fiat incorporates artificial intelligence into its blockchain architecture, including the use of large language models for economic transactions. The project examines models in which AI agents can participate in economic activity alongside human participants.

Good's work at Post Fiat has included research and writing on financial markets, digital economies, blockchain technology, and artificial intelligence. His writings include the Doom Thesis, as well as discussions of Web4 and the agent economy. ^{[1] [2] [3] [4]}

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. ^[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. ^[6]