Danielle Benecke

Building AI systems for complex global legal, regulatory & compliance risk. Founder, Applied AI practice @BakerMcKenzie. Tech lawyer & board member. @Stanford

[Danielle Benecke @DanielleBenecke]

https://t.co/66ji3aO9n1

[Danielle Benecke @DanielleBenecke]

The enterprise AI question has changed.
Not "what tool do we roll out."
"Where does our harness live" - and which parts you own vs rent.
Went deep with @MaxJunestrand, CEO of @WeAreLegora on @aparnabsinha's Enterprise Aligned AI. Full episode below 👇

[Danielle Benecke @DanielleBenecke]

@ZachAbramowitz AI systems that support complex, strategic work is the far bigger opportunity - for both the clients and firms

[Sonya Huang 🐥 @sonyatweetybird]

On our new @sequoia AI podcast Training Data, @langchain founder @hwchase17 talked to us about the idea of a middle ground for AI agents that are in between hard-coded chains and fully-autonomous agents that fly off the rails. Constraints are key to a “goldilocks” zone that are allowing next-gen agents to radically uplevel performance. Here are some thoughts inspired by the conversation.

[Eric Schmidt @ericschmidt]

To build the future you need to think outside the box, to be successful you need to put it back in the box— Bob Metcalfe inventor of the Ethernet

[TechCrunch @TechCrunch]

In case you missed today's #GoogleIO keynote presentation, we summed it up for you

[Danielle Benecke @DanielleBenecke]

Global law firms stocking up on AI talent. Leaders say vendor market falling short of needs. Nice piece from @jstnhenry87 of @AmericanLawyer spotlighting some of what’s really going on inside of firms right now.

As it always has been, all about finding the right balance of internal capability (enterprise tech + talent) and an ecosystem of vendor partnerships - all focused on clients and services.

[ronfriedmann @ronfriedmann]

Big Law's AI Talent War Aims to Influence Software Development
ALM https://t.co/Ntxn5vVyFf
#LLM
#BigLaw firms are hiring AI directors

[Michael Chang @mmmbchang]

Gemini and I also got a chance to watch the @OpenAI live announcement of gpt4o, using Project Astra! Congrats to the OpenAI team, super impressive work!

[Brian Halligan @bhalligan]

2004: Software as a Service

2024: Service as a Software

h/t - everyone

[Artificial Lawyer @ArtificialLawya]

DISCO Launches GenAI Cecilia Doc Summary Capability

> AL takes a look + considers what all this #genAI development means for #legaltech tools.

#lawtwitter HT @csdisco

https://t.co/ZwrirdS6lU

[Aaron Levie @levie]

Everyone knows Moore’s law, the idea that CPU performance doubles roughly every two years. This has enabled essentially every innovation we’ve had with computers since the 60’s and 70’s. But remarkably, driven by GPU performance and AI model breakthroughs, we’ve seen a very different law emerge with respect to the rate at which AI model performance improves — in many cases improving by orders magnitude in the past decade. This introduces massive implications in world of AI Agents.

For the past five plus decades, in any domain that computers touch, we’ve generally seen massive efficiency gains that bring more tech to more people. For instance, scaling of servers in the cloud with AWS, accepting payments with Stripe, or powering communications with Twilio — in all of these cases something that once was extremely expensive and complex is now affordable and ubiquitous. And that affordability (or value delivered) generally only gets better as each year goes on. Yet the same has not been possible for essentially any non-digital services.

Historically, anytime you want to solve a non-computerized business problems (i.e. most knowledge work), we have seen the cost to solve a problem — say from healthcare services to legal work — tend only go up over time. Due to a mix of inflation, specialization in certain fields, regulation, decreased competition in markets, or other added costs, we’ve seen the price of most services generally monotonically increase over time. 

Of course, much of this is perfectly fine, natural, and good. But this also puts a burden on driving economic activity in general, especially when creating new ideas from scratch, or starting and running a new business. Yes, I can get going with the cloud inexpensively, but that doesn’t matter if all other areas of my business are still hard to scale. Of course this is fine for a hyper growth idea in a mature market with access to the right resources, but that’s a small percentage of the world.

Yet, in a world of AI Agents, anyone — from a small business or large enterprise — has access to automated work that can let them address problems with variable capacity. When you decrease the cost of entry for getting knowledge work done, you dramatically increase the use cases for that work. That new sales program can be tested instantly instead of waiting weeks or months or never happening at all; you can test a broader surface area of your product for bugs or security issues; legal work and reviews becomes affordable on the seemingly insignificant transactions that otherwise produce risk; or you can launch a new marketing campaign in markets you didn’t otherwise serve.

AI lowers the barrier to doing all of these activities and more (and as I’ve shared before, ironically by doing so you actually will generate *more* job growth due to the productivity generated). 

Even in a world of AI costs remaining stable this is a super compelling proposition, but in a world of constantly improving technology the implications are vast. 

Just as Moore’s steadily lowered the cost of software, increased its sophistication, and made it more ubiquitous, we can imagine a world where knowledge work regularly gets more affordable, sophisticated, and ubiquitous at a constant rate. This means AI Agents presents the ability for any unit of work to continuously drop overtime, enabling us to solve greater and greater problems for a wider set of people and businesses. Of course, a large portion of these efficiency gains will quickly get “eaten up” by solving increasingly more sophisticated problems, but those problems will be solved at the cost of a previously simpler problem. 

The implications of this are enormously interesting, and it’s going to create incredible opportunities all around.

[Danielle Benecke @DanielleBenecke]

Nice to see this quick shipping from the @eBrevia team. <6 mths since @HelloAdamio and @jakemundt bought back the co. Great to see from one of the orig + leading AI-enabled contracting cos we + others have been working w/ from the mid-2010s 👏

[Legaltech©news @Legaltech_news]

Contract Analysis Company eBrevia Launches Gen AI-Powered DraftPro Months After Co-Founders' Buyback https://t.co/eXArdGtJsx

[Danielle Benecke @DanielleBenecke]

No surprises here. @Microsoft continues to pursue independent AI paths, in parallel w/ its @OpenAI and other partnerships - nice reporting from @aaronpholmes at @theinformation

[aaron holmes @aaronpholmes]

Scoop: Microsoft is training its own large language model, internally labaled MAI-1, with Mustafa Suleyman leading the effort. The model is around 500B parameters and could compete directly with LLMs from Google, OpenAI, etc. Details: https://t.co/V99mpKS9hh

[Jean-Rémi King @JeanRemiKing]

Really cool paper from @FabianGloeckle, @syhw et al @AIatMeta showing the importance of learning to predict beyond the next word:
https://t.co/qdgM4DIYXh

I'm real pleased as we showed last year that this objective better accounts for brain activity: https://t.co/dnofVxQiLw

[Olivia Moore @omooretweets]

There is only one enduring consumer acquisition strategy and it is…

An amazing product

[Danielle Benecke @DanielleBenecke]

@casetext @LegalweekShow @Jacob_Heller @tweetatpablo Congrats team 👏

[Danielle Benecke @DanielleBenecke]

Assembling the dream team for @LegalweekShow’s GenAI IRL workshop - Steve Gong (@Google), @JulianTsisin (@Meta), @leomurgel (@salesforce), Dr Zonghui Wei (@bakermckenzie), Jeff Reihl (@LexisNexis) - get ready for some real talk :) #Legalweek24

[Danielle Benecke @DanielleBenecke]

What does that mean concretely for @bakermckenzie and in-house teams? Join me, @JulianTsisin (@meta), @leomurgel (@salesforce) and Steve Gong (@google) at this year’s @LegalweekShow AI workshop (Monday January 29, 1:30 pm) for some legal GenAI #realtalk

[Danielle Benecke @DanielleBenecke]

Flashback to last yr’s @LegalweekShow when I caught up w/ @a_lancaster3 and @ZackNeedlesALM of @lawdotcom 👇 Looking to #LegalWeek24, and w/ exponentially more #BakerML client work under our belt, I’m going to double down on these early observations.

[Law.com @lawdotcom]

ICYMI: #LegalSpeak podcast: In this clip Danielle Benecke, founder & global head of @bakermckenzie’s machine learning practice, shares thoughts on how technologies like Generative AI have changed the law firm business model. Listen to the full episode » https://t.co/TSKtB6vv3u