This is a long-form interview with Akash, a former head of data at Third Point, about how alternative data, process discipline, and now AI agents are changing hedge fund workflows. His core message is that real alpha comes from a differentiated, measurable opinion, and the future of investing is a convergence of discretionary judgment and quantitative tooling, with agentic systems encoding the judgment layer.
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Akash’s core thesis is that alpha in markets comes from a differentiated view that can be quantified, tested, and embedded into a repeatable process. He argues that the old split between discretionary “qual” investors and quant investors is fading because modern AI systems can now handle unstructured information, search, reasoning, and workflow orchestration well enough to encode human judgment into scalable research processes. In his telling, the future is not AI replacing investors, but AI becoming the infrastructure that lets investors do more, faster, and with tighter feedback loops. A large part of the conversation centers on his work building the data team at Third Point beginning in 2017. He describes the initial challenge as institutional: building secure, point-in-time, legally compliant, accurate data pipelines before any real signal work could be trusted. …
Near term, the tradeable story is that AI tooling will keep improving research workflows, but finance adoption will stay gated by eval quality, data access, and human oversight.
Over the next few months, the base case is broader deployment of agentic workflows for earnings prep, monitoring, and portfolio support, with the biggest gains in repeatable SOPs rather than fully autonomous decision-making.
Longer term, the structure of finance may shift toward a hybrid regime where discretionary and quantitative research converge into one workflow layer, with alpha increasingly coming from how well firms encode and evaluate judgment.
Alpha comes from a differentiated opinion on something already known, and it must be measurable to count as real differentiation.
He explicitly defines alpha as differentiated opinion plus the ability to put a number on it.
Third Point needed an institutional-grade data pipeline before it could trust or use alternative data in investment decisions.
He describes the first months as building accurate, point-in-time, legally compliant infrastructure before value delivery.
For discretionary investors, alternative data is most useful when it is directional, relative, and paired with confidence intervals rather than treated as a single forecast.
He repeatedly says PMs want factual information, directional signals, point estimates, and error bands.
How are agentic workflows being used in hedge fund investing?
He says the field has shifted from prompt engineering to context engineering, with newer models improving accuracy enough to make the workflows more useful in finance. He adds that the analyst's judgment is being encoded into these workflows, and he expects a convergence of quantitative and qualitative work.
Can you talk about founding the data team at Third Point?
He explains that the team was built in 2017 to bring a disciplined alternative-data process into an event-driven hedge fund. A major part of the early work was building institutional-grade data and legal infrastructure so analysts could trust the data, including point-in-time copies and proper vendor/documentation processes.
How do fundamental discretionary hedge funds use data for event-driven investing?
He says the team first mapped each PM's information needs and the sectors they covered, then built catalogs of relevant data sources. He gives examples like transaction data, parking lot counts, and cohort analysis to compare performance across companies and relative to benchmarks.
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