Artem Fokin

Fokin sees expert-call libraries and AI as the two disruptive innovations that most changed investment research, not merely incremental process improvements. Walker contrasted independent investors with institutions that could commission 20 calls at roughly $1,000 each and deploy large analyst teams. Fokin says libraries lowered expert-call costs and converted a pure variable-cost model into something semi-fixed and semivariable, while AI compresses summarization work. The emerging edge is “human plus machine,” not either one alone.

An expert call should target the load-bearing assumption in the investment thesis. If the thesis rests on product superiority, interview users, switchers, evaluators who declined, and people who never considered it; if distribution is the question, talk to former salespeople and reconstruct the sales motion. Fokin’s reminder is deceptively important: “Sales just don’t happen.”

The best expert work surfaces disagreement rather than manufacturing consensus. Walker’s medical-device example paired a reported 1% defect rate with 10% for older products, yet surgeons argued that the comparison used decades-old studies and ignored subsequent improvements in technique. Fokin similarly heard reactions ranging from liability-conscious enthusiasm to “I don’t really care” when asking doctors about Sofwave’s almost 10 FDA clearances: “Nothing is certain. Different people have different opinions, and that’s okay.”

Former employees are useful witnesses, but their testimony needs context, corroboration, and relentless follow-ups. Fokin asks laid-off employees whether they would return or recommend the company to family, then checks their claims against product quality, customer enthusiasm, management commentary, and other interviews. Investing rarely reaches “beyond a reasonable doubt”; the achievable standard is a “preponderance of evidence.”

Volume matters when the business is complex, but disciplined curation determines whether volume becomes insight. Fokin conducted five bespoke calls on IWG, still felt he had not figured it out, and then read roughly 70 IWG and WeWork calls; the available library has since approached 100. At Crocs, historical interviews helped distinguish a temporary COVID beneficiary from a company that had built its commercial foundation before COVID while the stock traded around 5.5–6 times 2022 earnings.

Fokin currently gives AI a vote in “digging” and part of “analyzing,” but zero authority in “deciding.” As of August 6, he had found many ways to compress existing work but no meaningful task AI enabled that he could never perform before. A question such as how a company expanded from 10 states can now be answered with historical context and sources in seconds instead of a manual filing search; Walker gave three-proxy incentive-compensation analysis as a similar time saver.

AlphaSense’s Grid and Deep Research workflows turn large document sets into navigable research maps, without replacing primary-source reading. A Grid can place 20 expert calls against as many as 12 recurring questions, exposing consensus, red flags, and the interviews worth reading in full; Deep Research can turn a five-page prompt into a sourced 30–40-page primer that “prepares the mind” before the filings and calls. Fokin attributes “90 or maybe even 95%” of what he knows about new AlphaSense features to repeated sessions with his account manager, while product managers provide another source of use cases and feedback.

Process quality—not a single year’s return—is what can actually improve an investor. Walker finds motivation in seeing someone conduct better research; Fokin still values comparable long records, arguing that roughly 25% CAGR for 20 years is probably evidence of exceptional skill. Both resist “scoreboard, bro” conclusions from concentrated records where a handful of bets may explain everything.

Compounder investing means deliberately betting against broad base rates, then demanding enough upside to make the asymmetry work. Most companies will not compound revenue or earnings at 20% for 10 years, so investors must improve the odds through filters such as owner-operators and attractive unit economics. Fokin’s second requirement is venture-style convexity: the winners must have enough upside to offset the many bets that will not work.

Fokin warns that AI might make markets more consensus-driven by encouraging investors to outsource the intellectual work that creates conviction. Walker argues that similar questions posed to similar models may produce similar answers, while Fokin says the ability to build a mosaic from 15 transcripts or numerous expert calls could atrophy like navigation after GPS. The winners will use AI “as a supplement” and recognize when its polished output creates “fake knowledge.”

As AI and expert libraries democratize information processing, edge may migrate toward creating information that was never online. A large fund once had an enormous advantage because it could finance extensive bespoke research; now smaller firms can translate filings, search libraries, and synthesize calls cheaply. The remaining opportunity is to uncover neglected companies, conduct customer work, attend industry events, and realize that “everybody else is running left” when the evidence says to run right.

Fokin’s biggest process change has been a much heavier focus on customers and the difference between satisfaction and genuine advocacy. His painful 2015 Agrify investment might have been avoided by discovering that customers valued the product but were not “raving fans.” Yet Walker’s pushback survives: customers can dismiss an unlaunched product and embrace it two weeks later, so calls are inputs—not rules.

A respected friend’s idea deserves priority in the research queue, not a lower underwriting standard. Fokin’s objective is to “make the conviction my own” by completing the same work—transcripts, customer calls, management questions, and other routines—regardless of who sourced the name. Investors should also weight tips by the source’s actual superpower: an obscure $200 million company may fit Fokin’s pattern recognition; a coal special-dividend trade probably does not.

Expected IRR cannot force-rank a portfolio unless it is explicitly penalized for leverage and existential risk. Walker finds raw IRR estimates pull him toward the most levered equities, while enterprise-value comparisons pull him toward cash-heavy companies with little upside torque. Fokin’s answer is judgment: two stocks offering 25% expected IRRs over three years are “not born equal” if one is debt-free and the other has 6× EBITDA, 4× debt, and 2× equity in the example.

Expert-call libraries and AI are a “match made in heaven,” provided the model accelerates learning rather than deciding the investment. Walker’s workflow moves from an LLM synthesis of five calls, to individual call summaries, and finally to full transcripts, improving both comprehension and retention. Fokin maps AI onto “digging, analyzing, deciding” and asks where it belongs heavily, sparingly, or not at all.