The Hedgineer Podcast

Instinct's agent-to-agent coordination is the most interesting thing a consumer AI app has shipped recently, and Michael and Jhanvi get into how they've been using it. Instinct lives entirely in iMessage. Michael can ask his Instinct to coordinate something with Jhanvi's, and the two agents handle the back-and-forth while each person approves what gets shared. They compare it with Muse, from data connectivity to the experience of interacting with the agent. They also debate where the moat sits when a feature like Instinct's is easy to copy and network effects can be bridged by integration, with accumulated memory about the user as a more durable advantage than any single feature. Finally, they cover the gaps that keep either app from replacing a daily driver, including limited calendar support and no way to read what your agent said on your behalf.The conversation then turns to what consumer AI means for the firms that buy enterprise tools. As people get used to simple, connected assistants at home, the pressure on enterprise AI policies around email and calendar access will grow. They close with model news: a new generation of cheaper, faster releases, the case for small fast models as top-of-funnel classifiers across large volumes of news and filings, and why token speed matters more as agents carry more context into every session.About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Instagram: https://www.instagram.com/hedgineer/LinkedIn: linkedin.com/company/hedgineer-io TikTok: tiktok.com/@hedgineerTwitter: x.com/hedgineeringEmail us at [email protected]

Opus 5.5 shipped at 40% below Fable. Meta Muse explodes in popularity. It's a busy time in the world of AI.Michael and Jhanvi go through their first reactions of Muse. The onboarding asks you to name an agent before you've asked it to do anything, then connects your email and calendar, then asks about your health, your finances, and the relationships you want to work on. It never asks which model you want. It never asks you to build complex AI workflows. Meta pulled human reasoning and expertise out of the loop entirely and gave the harness that call, which is the opposite of how most agent products work today. They also allows users to see the entire file system, so ordinary users are browsing memory files and markdown like a codebase. Whether that survives mass adoption decides which technical concepts become general knowledge, the same way "file" and "firewall" did.The through-line is who gets value. Most of the people currently getting real leverage out of AI work in technology, and that includes funds outside of tech who have bought the same tools and still can't reach the autonomy they read about. Consumer AI is how that closes. Every time someone makes an agent feel obvious to a hundred million people, the floor rises for everyone building on top. They also get into Apple's Siri beta and why the data advantage there remains the most underused in the industry, what the social media rollout should have taught everyone about moving quickly, and Jev, which charges nothing for output tokens and is quietly very good at the boring classification work underneath every agent. About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Instagram: https://www.instagram.com/hedgineer/LinkedIn: linkedin.com/company/hedgineer-io TikTok: tiktok.com/@hedgineerTwitter: x.com/hedgineeringEmail us at [email protected]

OpenAI and Anthropic are calling for a slowdown in frontier AI development. Nvidia and Meta are not. The line runs between companies whose revenue depends entirely on frontier research and companies with tested businesses underneath them. Jhanvi and Michael take apart the incentives behind the debate, and they cover everything from the moat heavier regulation would build for larger players to the data center fights in states like Michigan, where towns are debating whether these centers do more harm than good.Then the question every firm rolling out agents hits in week one: who is the agent logged in as? Authenticating as the user keeps accountability clean, until that person leaves and the job breaks. So is the answer service accounts with pre-configured permissions? Plus the new favorite way of talking to agents, which surprises no one: email. About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]

Data engineering in asset management was always a context problem, not a technology problem. The analyst knows what the data means, the data scientist knows how to query it, and the data engineer sits two steps removed from the domain, building pipelines against a business problem nobody translated cleanly. Michael and Jhanvi make the case that AI collapses that chain: you can now apply context at the last mile, at inference time, instead of spending the first 99 miles encoding it into a warehouse that turns into cement. The security master edge cases, the identifier mapping across 300 names, the non-GAAP KPI alignment against Visible Alpha, all of it fits in a cheap model's context window.So what does a data engineer do now? Build the context layer. Skills, agent instructions, tools, memory, and an observability layer that tells you which agents are failing and why before your users do, then harden pieces into deterministic code only once you actually understand them. Jhanvi and Michael also get into where this breaks: an agent harness that can't respect your data permissions, MCP connectors that authenticate as a service account instead of on behalf of the user, and the question of what accountability looks like when the work is done under an agent's name instead of yours.  About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]

Hedgineer's annualized AI bill has grown past $1M, which brought back up a long-running internal discussion at the company: how do you measure ROI on a token? Michael and Jhanvi work through both layers of the problem. The first is instilling good AI hygiene practices across the company — encouraging thoughtful model selection, session discipline, keeping irrelevant context out of the window to optimize on cost. The second is harder. Tag every session to the feature, product, or service it produced, and the company starts to look like a portfolio of assets, each with a cash flow you can discount, price, and reallocate capital against. Running a business through the lens of AI turns every operator into an asset manager.The complication is that building a product has never been cheaper and building a good product has never been harder. Everyone can prototype now, so the constraint moved from execution to taste, and killing good ideas in favor of great ones grows more challenging. They also get into why an observability layer and a skill library are worth more together than either is alone, and why some AI products are immediately understood and then never used. About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]

Stripe just bought OpenRouter for over $7B. At first glance, a payments company acquiring a model proxy for that much money sounds strange, until you consider what Stripe sees. It watches spend across a very large customer base, and AI spend is rising at an exponential pace. When inference becomes something you shop for rather than something you're locked into, owning the venue where tokens get priced starts to look not just strategic, but immensely lucrative.Underneath the deal is a question every company is now running live: does it make more sense to purchase AI inference through a router, or is sticking to a frontier model subscription still the right choice? The answer depends on how much of your work actually needs the best model available, and on whether you have the engineering capacity to build the tooling that makes a router usable in the first place. Michael and Jhanvi take opposite sides on how long premium reasoning stays worth the premium, which turns into the bigger question sitting under every frontier lab valuation. Those numbers assume companies reorganize around AI, that the work left for people is the hard judgment-heavy kind, and that the labs capture the spend for everything else. The destination is easy to describe. The route there is the argument. About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]

Michael and Jhanvi have never edited a video before, and this week they built an entire marketing campaign. They walk through the whole pipeline: Claude Code turning a long prompt about the product into a storyboard of 12 to 16 shots, generating a color scheme and reusable SVG characters, routing clip generation through Runway to Veo, Kling and Seedance, then pulling narration and a score timed to the story from ElevenLabs before stitching it all together with Remotion. Two days to build the process, and it now runs end to end from a phone.They then get into how the role of video will evolve as it becomes easier and cheaper to create. It has always had a place in sales, marketing, and recruiting. But what about IR, BD, and even research? About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]

Building application prototypes has never been easier. The ceiling comes after, when someone has to deploy it, host it, and keep it running without much engineering support. This week, Jhanvi and Michael work through what stands between a good prototype and a good application: setting up AI environments that are faster to work in and harder to break, wiring in MCP connectors thoughtfully, and what changes the moment something has to run in production.Along the way they get into why the same prompt in Cowork and in Claude Desktop produces two very different dashboards, and why only one of them refreshes with live data. They also make the case that not everything needs to be a dashboard, and where scheduling an agent can be more effective. And they share advice for engineers navigating a world with more vibecoding in it, and what providing value looks like when the front office can build its own tools. About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]

AI names were sold off for several days, a heavily levered AI fund wound down its entire equities book, and then one earnings print reversed most of the damage within hours. The same industry supported two opposite readings in the same day, which says more about how funds are measuring their AI exposure than it does about AI.Michael and Jhanvi start with why so few funds formally manage their exposure to an AI factor the same way they monitor crowding or market beta, and what happens when crowded exposure to the same handful of names gets booked as idiosyncratic risk and banks are comfortable lending against a portfolio that looks market neutral. From there they get into what makes a business resilient when public sentiment continues influencing public markets, why Microsoft has a head start against their cloud competitors, and how AI is pulling those funds into compute spend they would never have taken on before.Also in this week's episode: an Anthropic model published a malicious package to PyPI, one of the most trusted repositories in the developer stack, and the accountability question that opens up when a model running inside your infrastructure harms someone outside your company. They also get into the widening gap between the people using AI at work every day and the people booing it off commencement stages, and why Meta may be better positioned than any frontier lab to serve the small businesses through WhatsApp. About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]

Almost every fund has a couple of power users spending their weekends in Claude Code, prototyping automations, and proving out how useful AI can be. But then what? How do they hand it to a teammate, and how does a prototype become a production automation that compliance signs off on?Michael and Jhanvi make the case that the gap is a governance problem sitting upstream of every technical one. Everyone can build with AI. The harder half is getting compliance, IT, and cybersecurity aligned on the enablement guidelines: which tools are approved, on which operating systems, with what runtime and network observability, and who reviews usage after the fact. Answer those and you can put more powerful capabilities in your team's hands, including scheduling, skills, memory, and shared knowledge bases. Also in this episode: the case for sending email as an AI rather than as yourself, why em dashes get a message ignored, and what happened when frontier models refused to help investigate a breach that one of them had been used to cause. About HedgineerHedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows. The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day. Subscribe for weekly analysis on AI infrastructure and institutional finance.Watch the full episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at [email protected]