Chris Sheehan

VP Sales and Customer Success, Applause

[Aaron Levie @levie]

Had meetings and a dinner with 20+ enterprise AI and IT leaders today. Lots of interesting conversations around the state of AI in large enterprises, especially regulated businesses.

Here are some of general trends:

* Agents are clearly the big thing. Enterprises moving from talking about chatbots to agents, though we’re still very early. Coding is still the dominant agentic use-case being adopted thus far, with other categories of across knowledge work starting to emerge. Lots of agentic work moving from pilots and PoCs into production, and some enterprises had lots of active live use-cases.

* Agentic use-cases span every part of a business, from back office operations to client facing experiences from sales to customer onboarding workflows. General feeling is that agentic workflows will hit every part of an organization, often with biggest focus on delivering better for customers, getting better insights and intelligence from data and documents, speeding up high ROI workflows with agents, and so on. Very limited discussion on pure cost cutting.

* Data and AI governance still remain core challenges. Getting data and content into a spot that agents can securely and easily operate on remains a huge task for more organizations. Years of data management fragmentation that wasn’t a problem now is an issue for enterprises looking to adopt agents. And governing what agents can do with data in a workflow still a major topic.

* Identity emerging as a big topic. Can the agent have access to everything you have? In a world of dozens of agents working on behalf, potentially too much data exposure and scope for the agents. How do we manage agents with partitioned level of access to your information?

* Lots of emerging questions on how we will budget for tokens across use-cases and teams. Companies don’t want to constrain use-cases, but equally need to be mindful of ultimate token budgets. This is going to become a bigger part of OpEx over time, and probably won’t make sense to be considered an IT budget anymore. Likely needs to be factored into the rest of operating expenses.

* Interoperability is key. Every enterprise is deploying multiple AI systems right now, and it’s unlikely that there’s going to be a single platform to rule them all. Customers are getting savvier on how to handle agent interoperability, and this will be one of the biggest drivers of an AI stack going forward.

Lots more takeaways than just this, but needless to say the momentum is building but equally enterprises are acutely aware of the change management and work ahead. Lots of opportunity right now.

[Pankaj Gupta @pankaj]

We're just getting started at @yupp_ai -- but thanks to a growing global community using Yupp for daily use cases, we are on the way to gathering a rich, diverse & high quality feedback dataset across AI models at "consumer" scale.

Our Chief Scientist @lintool gives an overview of Yupp's VIBE score & leaderboard (Beta) in this tweet thread below.

[Chris Dixon @cdixon]

I’m excited to announce we’ve led a $33 million seed round in @yupp_ai, a consumer product that allows anyone to discover and compare the latest AI models for free. AI needs robust and trustworthy human data. Crypto is built to provide it.

Modern AI systems are shaped not only by compute and algorithms but by human feedback. Companies use post-training techniques such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimisation (DPO) to improve their models. These techniques can reduce bias and enable higher quality, more coherent responses to prompts — crucial for accelerating progress in AI. Model evaluation is similarly critical, but a model can only be made better after first deciding what “better” means.

That’s where challenges arise: Companies don't like to share — they keep their data and training processes secret. As a result, model improvements are constrained by what can be learned from closed systems or static benchmarks that are rarely informed by real-world use. These constraints make AI models difficult to evaluate. Users are also left in the dark, with little insight into how their feedback shapes models or whether it’s used at all. Some leaderboards and crowdsourcing sites attempt to shed light here, but they generally don’t enable users to audit their contributions or see any direct benefit from participating. Platforms that claim to be fair and transparent often rely more on good faith than enforceable standards.

We believe crypto can bring transparency and ownership to this murky area of AI. Blockchains can make it easier for people to receive rewards for their contributions. They can also provide AI builders with assurances about the quality and provenance of the feedback data and evaluations they’re incorporating into their models. So users get incentives, builders get trustworthy data, and everyone can audit either side of the open market.

Yupp crowdsources model evaluation: users enter prompts, see multiple AI-generated responses side-by-side, and then pick the best ones. Their choices create digitally signed “packets” of preference data that are useful for AI post-training and evaluation. In addition to users getting access to the latest models for free, they receive rewards based on the feedback that they provide.

Yupp’s design turns human judgment into a renewable economic resource. Data “expires” as newer interactions replace it, creating a natural flywheel: more usage yields fresher evaluations; fresher evaluations yield better models; better models attract more usage. All participants — from users to AI model builders — can participate and see that the same transparent rules apply to everyone, ensuring a credibly neutral marketplace. No one can hide the scoreboard, and no one can manipulate the rewards or results.

The founders bring deep experience in both AI and crypto. They built consumer-scale machine learning products together in the early days of Twitter. @pankaj ran global consumer engineering for Google Pay and @Coinbase. @gilad was a machine learning lead at GoogleX. The early team already counts senior engineers from Google, Coinbase, and top research labs.

AI needs strong, reliable evaluation based on large-scale human input. Crypto is the trust machine that can help deliver it. By enabling people worldwide to contribute model-improving feedback, Yupp aims to become the default evaluation layer for the future of AI. We’re proud to back Yupp and look forward to helping them build the onchain feedback loop that ensures the rewards of AI innovation are shared by everyone who helps create it.

[Chris Sheehan @c_sheehan]

@lojeljourneys @kunal_trs We are having similar issues with their customer service. Purchased 2 suitcases yet one zipper doesn’t work so want to exchange. Making us do videos of the problem then no reply.

[dharmesh @dharmesh]

Here's the tweet I had meant to reference:

https://t.co/UKPTFu762t

[Chris Sheehan @c_sheehan]

@oledoteth 🍄

[Chris Sheehan @c_sheehan]

Looking forward to sharing best practices on testing LLM based apps! https://t.co/ykPE6xDLqK

[Applause @applause]

Useful in-market insights shed light on why and how your brand fails with subscribers. Learn how #media companies can take a proactive approach to #localization. https://t.co/tWNhq5g5PA

[Christopher Clarey 🇺🇸 🇫🇷 🇪🇸 @christophclarey]

Big news for me:

After more than 30 years of covering #tennis & global sports for The New York Times & its Paris-based sister publication The International Herald Tribune, I am leaving the @nytimes staff of my own accord & with nothing but gratitude to become a full-time author

[Chris Sheehan @c_sheehan]

@lufthansa Cant get into the app (its stuck), website doesnt offer any alternatives, and no one answering the phones @Lufthansa_USA @lufthansa

[Chris Sheehan @c_sheehan]

Hey @lufthansa my flight today is cancelled - please help with rebooking as your website and app dont provide any help

[Christopher Clarey 🇺🇸 🇫🇷 🇪🇸 @christophclarey]

Swiatek was up 6-3, 4-1 and two breaks of serve vs Kovinic

4 games later, it is now 5-4 Kovinic in the 2d set

[Chris Sheehan @c_sheehan]

Tennis channel app not working following the update. Please fix. Terrible customer experience @TennisChannel

[Chris Sheehan @c_sheehan]

And of course, the phone doesnt ring when the call back happens...now another hour...sigh @Delta

[Chris Sheehan @c_sheehan]

@Delta Platinum Medallion member and it take 1hr 7 min for a call back on customer service?

[Chris Sheehan @c_sheehan]

Agree - in my 10th day and still no delivery. The delivery status each day is not accurate. Poor service @NespressoUSA

[Christopher Clarey 🇺🇸 🇫🇷 🇪🇸 @christophclarey]

Poll just about closed. More than 2300 votes. Unscientific of course but a landslide for the HawkEye Live candidacy. The fans, at least these fans, want accuracy and clarity all the time. No more line judges or challenges. All electronic

Food for thought

[Christopher Clarey 🇺🇸 🇫🇷 🇪🇸 @christophclarey]

Often his own worst enemy, Novak Djokovic was at it again on Sunday, and when someone from the next generation wins the US Open, he'll know who to blame.

My latest column
https://t.co/zDVnMBCojM

[Christopher Clarey 🇺🇸 🇫🇷 🇪🇸 @christophclarey]

One of these men will win a 1st Slam singles title at this US Open

Pablo Carreño Busta
Denis Shapovalov
David Goffin
Borna Coric
Alexander Zverev
Matteo Berrettini
Andrey Rublev
Frances Tiafoe
Daniil Medvedev
Vasek Pospisil
Alex de Minaur
Felix Auger-Aliassime
Dominic Thiem