Kenneth Yesh

Femtech is mostly bullshit. Bullshit spun by conference organizers, VCs, and corporations jumping on a conversation that is edgy but that is not turning into real-world transactions the hype would make you assume are happening. We have the data. And then there's those that do the work. Like Dr. Sioned Fôn Jones at BoobyBiome, Hannah Samano at Unfabled and Alice Pelton at The Lowdown. I had the privilege to moderate the panel Femtech Heroes: Untold Stories at Decoding the Future of Women conference this month. My goal was to energize those in the audience who wanted to start up their own Femtech companies. However, as the session progressed, we, for once, decided to tell real, raw stories instead of "focusing on the positive," as we tend to do in public speaking. Their stories about fundraising, managing real life with modest salaries, and being solo female founders were sobering, and I think some of the aspiring startup founders in the room walked out a bit less energized than when they walked in. And that may be a good thing. Our time, funds, and sanity are valuable. Think twice before you quit your job to become a Femtech founder. Again, data shows that the odds are not on our side. I ask us, Femtech founders and operators, to call on the bullshit we are hearing and getting so we can make real progress. I wish you all a good weekend with restful sleep, you know what I mean ❤️

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Not sure if anyone has noticed... I saw a bunch of LI posts from YC's LinkedIn page showcasing (marketing) their portfolios. Almost immediately you have actual engineers and builders (mostly outside of SF) commenting on the technical limitations of said highly funded and valued startups. It's not verbose lazy commentary but actually very insightful and detailed "why would I use {YC portfolio}? When I can use {a,b,c} for 1/10th the cost". And such comments actually generate the most likes which for me signals x-SF/builder consensus on their amusement on how these folks got funded.

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Median pay for #AI engineers is now 52% higher than non-AI engineers and many are asking for equity packages that are 2-3x more than what typical engineers would command. A new report by Rosie Bradbury from PitchBook provides insight into the latest talent shortage facing the technology industry. To truly see the value of #GenAI, our industry needs to rapidly increase access to: 1) Compute - We need to build and deploy new servers to run models 2) Energy - We need a sustainable source of energy to power these servers 3) Data - We need access to clean data to train models and increase accuracy 4) Talent - We need more engineers who are trained to build and leverage #LLMs It will take time for the talent gap to reduce as engineers re-train and new graduates enter the labor market. In the meantime though, it doesn't hurt to be an AI engineer! Read the full article here: https://lnkd.in/gvYmE8YP

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It's time. I'm looking for a U.S.-based Engineer—preferably in Detroit—who's ready to gain experience working with a new AI startup. This is an equity-based position until we secure our first round of funding. As a tech headhunter for other startups, I understand the importance of salary. However, this is a bootstrapped startup at the moment, led by myself and another co-founder. We're building something truly groundbreaking: an AI-First Social Networking Startup, unlike anything that exists today. The MVP is 95% built (by me), and we're gearing up for a Beta launch. I need a founding engineer to: Help finalize code to production standard Deploy and test both the staging and production servers Bring 3-4 years of Machine Learning experience to the table and help take the LLM I've built to the next level Tech Stack MUST know - Multi-Agent AI systems, (Flask) Python, Tensorflow & Pytorch, Node, ReactJS, Redis, Docker / Dockerhub, Tailwind, Various AI Models, LLM Training, Digital Ocean / AWS, MongoDB, Web Scraping, Third Party APIs, Full-Stack Architecture, Message Queuing, CI/CD Pipelines This is an opportunity to be a founding Engineer and play a key role in shaping a transformative platform that has great impact. If you're passionate about AI, ready to sign an NDA, and willing to have some skin in the game like myself and my co-founder, let's connect. Please email me at david@baird.tech. Note: I’m not looking for a team or an agency—just dedicated individuals who are ready to build something incredible.

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I'm currently going through some very old SnagIT screenshots from 2015. I'm trying to recall which hype cycle this is from. Which hype cycle was relevant in 2015? #SelfServeAnalytics? Had we reached #BigData yet? The progress in "#reusability" has been remarkable for the cartoon industry. 😂 #SameHypeDifferentDay

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Notion vs Glean reminds me a lot of Tesla vs Waymo... Just like I think Tesla is going to solve the 'software' problem of autonomous vehicles before Waywmo solves the 'hardware' problem... I think that Notion is going to solve 'enterprise knowledge search' before Glean solves the problem of document and knowledge creation... Why? A couple reasons: 1) Just like Tesla, Notion has a huge advantage when it comes to the amount of underlying data and documents to train on... They already have 100M+ users, and likely 10x that in terms of the number of documents, files, etc. in team's workspaces. 2) It's very hard to change people's behaviour re: where/how they create 'work'. It's why a lot of companies have come and gone in that category... As exciting as Glean's AI writing assistants might seem, most people are just fine (or even prefer) a good old fashioned editor/writer. I could be way off here, but this will be an intersting one to follow. Thoughts? Where am I getting it wrong? #ai #enterprise #knowledge #management #notion #glean

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With today’s AI advances, it’s arguably the most exciting time in software history – and it’s been fun to be along for the ride (even more fun for my four-year-old). Yet any tech tool stops and starts with implementation. AI use has to land with our members, care teams, and customers to improve outcomes, and increase ease of use. We’ve had a long and extended journey here. Learn more about how Omada Health has implemented AI through the years: https://buff.ly/4c33haM

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Interesting perspective on MobilityTech. I wish there were a little more openness about, when it comes to features like "the in-car assistant should be able to quickly direct them to a nearby restaurant that they might prefer", being advertising-driven, not just user-preference driven, though. https://lnkd.in/gMgpeM3X

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5 intriguing AI developments I learned about last week: 1: Komodo Health + LangChain It’s one thing to see AI Agents and Compound AI Systems being launched in Retail etc but seeing Komodo Health build an AI assistant that “leverages the power of NLP and GenAI to provide personalized and informative responses to healthcare queries” within a highly regulated industry is significant. Big shoutout to the rockstars over at LangChain including Harrison Chase + Jacob Lee. No surprise they're involved. Link: https://lnkd.in/gjPMcH2S 2: AbbVie Gen AI enabled Enterprise Product Incubator for Capabilities (EPIC) Same as #1. This is happening in a highly regulated industry… “What does it look like to scale Generative AI to the enterprise from a grass roots level?” “Seventy-plus of the power users and builders leveraging our AbbVie Intelligence platform capabilities met to dig into the new advances in our vendor agnostic, enterprise supported, GxP ready, secure LLM powered ILIAD platform. Congratulations to Jon Stevens and the rest of the Enterprise Product Incubator for Capabilities (EPIC) team for coordinating such a great event!” - Brian Martin Chief AI Product Owner, ACOS Senior Research Fellow at AbbVie Link: https://lnkd.in/gS2RC-Q5 3: Google's NotebookLM A quote like this most definitely gets my attention - “The best software ever created for leveraging AI in your creative work.” - Tiago Forte, author of “Building a Second Brain.” Link: https://notebooklm.google/ 4: Arcade Company “Arcade enables anyone to design, purchase, and sell custom, manufacturable products with a simple text or image prompt.” “Arcade brings together the power of generative AI with a global network of top artisans to turn user ideas into personalized, physical products in a quantity of one. Arcade aims to redefine commerce by offering unprecedented personal choice, expression, and meaning in product creation. Link: https://lnkd.in/gGud-jHD 5: “Every white collar job will have an AI co-pilot. Then an AI agent.” - Angela Strange, Andreessen Horowitz “Which roles might change most? James da Costa and I took a look at the Bureau of Labor statistics to inspire some ideas.” Link: https://lnkd.in/gMJ8VASz Preview of next week? Ethan Mollick comments below about LLM enabled ad models and the reactions are likely to make it... ;) “No one has figured out how you integrate advertising with LLM replies.” “This is sort of a big deal, given that where the sloshing pool of ad dollars flows determines almost everything about the digital spaces in which we operate.” Enjoy your Monday!

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Their goal is to create 1 billion personas as synthetic characters for various scenarios. I'm going through the first release. The released data is all generated by public available models (GPT-4, Llama-3 and Qwen), and I have been using them simultaneously along with Gemini, Claude and Mistrel. Synthetic Data Samples: 50,000 math problems 50,000 logical reasoning problems 50,000 instructions 10,000 knowledge-rich texts 10,000 game NPCs 5,000 tools (functions) A subset of PERSONA HUB: 200,000 personas

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OpenAI just announced a $200/monthly access for ChatGPT Pro. https://lnkd.in/gfacR57t It's offering "scaled access" and smarter models. Right now, their API is pretty affordable, without a $200-a-month bill. It'll be disappointing if they apply new scaling restrictions to everything that isn't ChatGPT Pro. For the smarter models, users have become less sensitive to those differences. It's not that important, in my experience, that the model can solve some very difficult coding problems. Doing that better doesn't mean it handles operational use-cases better. When it fails, it's often because it's missing context, like making mistakes with recently released software because that software isn't in its training data. I'll probably pay for this because I have some scaled client use-cases, but I'd be surprised if there are huge sign ups for such a price point.

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A random thought while reflecting on what really goes down during product development… “An underappreciated engineer and an overwhelmed product manager.” Two sides of the same coin, yet often miles apart. The engineer is buried in code, solving problems, and tackling bugs, while the product manager is juggling deadlines, aligning stakeholders, and keeping the vision intact. Both roles are critical, but somehow, the connection between them can feel strained. In many product-led companies, it seems like the engineer’s efforts don’t always get the recognition they deserve—especially when the results aren’t immediately visible. On the flip side, the product manager is often stuck managing shifting priorities and expectations, trying to keep everyone happy. The end result? Frustration all around. But what if we could bridge that gap? Would clearer communication help? Or maybe a stronger sense of empathy for each other’s challenges? Perhaps the answer lies in a shared understanding that neither role operates in a vacuum. At the end of the day, successful products aren’t just about code or vision—they’re about the collaboration behind it all. Just a thought I’ve been mulling over—perhaps an immature one, but enough to spark some reflection. #ProductManagement #Engineering #TechCulture #Collaboration #Leadership

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🧪 Last week, $1.01 B was invested in AI. We are seeing a growing trend in funds going into Healthcare related AI use cases with $105m going into the sector last week alone. AI has huge potential to help in this space. Diagnosis, drug discovery, monitoring, analysis, reporting. ~~~ All of these tasks require large volumes of repetitive actions, but with a requirement for personalised repetitive actions. ~~~ The challenges for this industry are still, well, challenging. --Data privacy remains a top concern, there really isn’t any consumer data that is more sensitive. Consumers and regulators worry about personal data getting leaked, getting accessed or training wider models. --Bias. Bias occurs when training data and models are not perfectly balanced. For example, imagine an AI model missing a diagnosis for Malaria because it was trained on data from a single town in a country with no Malaria. --“It wasn’t me”. Humans and AI make mistakes, and when this results in damaged health or death - it is a tragedy. Who is liable at the end of the day? Clinicians? But they are acting on insights from AI…. This is a sensitive issue and will require very strong frameworks and regulations. --Validation. Yes the insights from AI may look correct, but are they really? Integration of AI tools will require a period of “supervised learning” where models are fine tuned based on what human clinicians observe. Clinicians are already very overworked and this period will only increase that. ~~~ A few mentions about the companies who got funding last week: --Huma - $80m, “hospital at home” offering. Huma collects patient data remotely and proactively connects patients to clinical teams. Whilst this offering supports those who need specialised care, Huma also aims to help those working in the industry - an industry very overburdened and prone to burnout. --Xellar Biosystems - $13.8m, wet lab scalable human biology and drug discovery. What is “wet lab” - well it’s a method of computation that favours pure experimental data over simulation. Human biology is still being explored and learned about, there are still so many things we don’t understand. Using real local data to compute is highly exciting here and I expect this method (and technology) to trickle down to other industries and use cases. --dotLumen - $5.5m, AI Guide Dogs for the visually impaired. “Guide dogs are great, but not scalable”. This is wearable technology that indicates safe walking paths, roads, puddles and dangers. They also recognise text allowing users to read and seek jobs they weren’t able to do previously. ~~~ These 3 examples are another showcase of AI enabling a higher range of capabilities (as opposed to merely replacing existing capabilities). AI will not replace jobs, it will create them.

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(1) Powerful and sophisticated (2) Fast time to value (3) Easy to maintain Generative AI has been a "CHOOSE ONE" world for the past couple of years. With an enterprise generative AI platform like Writer, it's a CHOOSE THREE world. Great example of this from the healthcare space from Sam Julien — a powerful, fast, and easy to scale app built on Palmyra-Med to develop SOAP notes functionality alongside patient / clinician transcripts / live chats.

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I am giving back to Healthcare. If your hospital uses Epic and you are considering note taking apps like Nabla /Abridge where you would spend hundreds of $ per user per month: I will develop a custom application for you for under $100K connected with your OpenAi account (using LLM/Whisper) and integrated with Epic. Your monthly running cost per user per day would be pennies. If nothing else then show this offer to Nabla/Abridge rep and get the price under $50 per month. If you develop with me: - **NEW**: You can resell the app to other hospitals. The IP and codebase will be in your hospital's name. I will charge no money if you resell. - Customized app for <$100K one time. Integrated with Epic. - Only pay for your LLM and Whisper spend directly to OpenAI (pennies for an hour of Whisper). - Possibility to use RAG for help with coding. There may be an additional few grands for this. - Deployed on your AWS/Azure cloud.

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new post - a few thoughts on AI in healthcare: https://lnkd.in/eGjqDtaj -OpenAI and how AI healthcare companies can build defensibility -How AI will create new types of data and what they might be -Why AI in healthcare probably won’t reduce costs + an example

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Chicago's AI community is growing like wildfire. Nothing shows this more than Logan Kilpatrick and Matt Ridenour's developer week at the Google Chicago office last week. We talked about all things Gemini for developers, AI product development, and how Google is leading the market in multimodality, context window length, and developer tools. The highlight of the week was getting to meet other incredible AI startup founders in Chicago, and hear their Nicolas Cage (That's a gone in 60 seconds joke) pitch. If you haven't met them already, check out: Babafemi Ekundayo from Calabax Srirajasekhar Bobby Koritala from ChatMemoir Corp. Tim Moore from Clixie AI Joel Dokmegang from World Prayer App Josh Streets from Efficiencies.AI Raul Alcantara from Entiendo (Neo ‘23) Gautam Ankala from Gitty AI Morgan Jones from G-Data Labs Kenya Brooks~Jones from Jones Software Corporation Sebastian Hooker from Nimstrata Kanav Setia from qBraid Mustafa Pesh from ClassBuddy Luis Emilio Suarez from Sanarai Nico Aguilar from Speeko — A.I. Speech Coach Edmund Dantés Hamilton from Zitnalta AI Shout out to the ones jumping into the arena and building with the Google suite to solve real customer problems. If you've been following along, part of my reason for moving back to Chicago was to turn the city into a hub for AI knowledge sharing and building. With support from Big Tech Players, we have the fertile soil to build an incredible ecosystem when combined with what AI Tinkerers Chicago has built for technical players. Special shoutout to Edmund Dantés Hamilton for sharing this video of our pitch, Matt Ridenour for letting me go a few seconds over, and Roya Kandalan for her exceptional knowledge sharing on the panel. Had to get an Indiana University Bloomington Basketball example throw in there. #gocharliego #letthedogsout #google #gonein60seconds #startups

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