LegalOpsHezzle

@scottastevenson I explained vibe coding to some lawyers from one of my teams in the pub yesterday. I don’t think they’ll invite me next week.

Vibe coding is hilarious. I love saying things that would get you murdered at as a product manager.

@scottastevenson I like the idea of comparing to athletic fundamentals that are essential standards and abilities needed to be in the game at the requisite level you want to compete at, and then rest is your special skills and abilities that allow you to play a certain way.

TLDR; hustle culture needs a rebrand as something as respectable and measured as athletic training

“High Performance Knowledgework” is not as much of a meme as it should be. Getting really good at the basics of daily work takes years, and the skill ceiling is incredibly high. For instance, in my role I need to *action* around 300 emails per week, and that is really just to keep the ship from sinking. That doesn’t get us anywhere. I need to be able to do that extremely fast so that I can get onto important things. This would have been unimaginable to me when I started my career, sometimes it used to take me 2 days to draft a single email. There really isn’t much general understanding of the kind of athletic dedication it takes to get good at this kind of thing

too powerful in this time zone. everything I needed done and getting ready to get a pint before the yanks awake. europe transformation almost complete

@_M155Y_ @BBCWatchdog @British_Airways Dreadful - i've been waiting since September, but when I check Twitter periodically I can see people chasing up their claims from 6+ months ago. Same old replies. They're prime for an investigation by BBC and by the regulator. It's shocking service. I think I'll just sue in 2024.

@ZachAbramowitz GenAI tools are in my daily arsenal of tools and save me lots of time on certain tasks. I was just interested if you had some specific examples of what you thought they were great at? (I hate twitter for always reading cold and sarcastic - I’m genuinely interested)

@LegalOpsHezzle I literally use it for everything I do. I have 10xed my productivity and you can see it clearly in our P&Ls. Could be my workflows are drastically different than everyone else.

LLMs are good at drafting, but GREAT at a lot of other things.

@jackwshepherd Enjoyed this - really nicely laid out. The 7 capabilities scribbled down for reference.

@sobedominik Whoa.

🔴 THE MOVIE IS LIVE 🎉 x.com/sobedominik/st…

For every 10 likes this gets, I will ask ChatGPT to make this cat work harder.

@SkyNews Typical. You pray for one religion and three turn up at once.

Bristol Airport has been mocked after unveiling a "multi-faith area" that social media users say looks more like a bus shelter 👇 trib.al/SLhLnaI

@Nicola_Shaver Oh dear my ChatGPT effort kicked this out…maybe it was my prompt “Draw me a picture of a thanksgiving turkey being stuffed with legal tech products / apps by a maniacal woman obsessed with both legal tech and turkeys”

Most unusual instruction I’ve ever given anyone in a work context: “can you please create a graphic that combines a turkey with Legaltech?”

At the risk sharing a GenAI first world problem, but god I'm sick to death of waiting for the response to be typed out.

Is anyone here using Co-Pilot in the legal space yet - do you have a standout use case that you've adopted out of the box? Got my licence today.

@HBurness @BamLegal @alexgsmith @Nicola_Shaver @nickyleijtens Logo on a power bank. Done!

Where are my legal tech crew at? Please help me. Which legal tech brands do you think are playing the strongest, most creative, marketing game. And why? I have my thoughts, but what do you think? @BamLegal @alexgsmith @Nicola_Shaver @nickyleijtens

@damienriehl @ReenaSenGupta Interesting. Damien / Reena any data for the corp law depts lagging on GenAI behind law firms comment or more anecdotal?

@ReenaSenGupta is also correct: On #GenAI, Corporate #LawDepartments are trailing (slower than) #LawFirms.

Truth bomb about #LLMs from @jaesunum and @edsohn80 — #GenerativeAI will capture 80% of corporate wallet (#legal spend). 🤯🤖⚖🏛

your kidney can go for like $300k and you only need one to survive god gave us all startup capital. you just have to want it bad enough

Long Post: Why Generative AI is Currently Doomed We're so used to technology getting better. Every year there's a new iPhone with a faster processor. It's the way of the world... or so it seems. Sometimes, bigger doesn't mean better. Take, for example, LLMs like ChatGPT. If you keep scaling them up, they eventually become worse. This inverse scaling leads to them becoming actively bad: (youtube.com/watch?v=viJt_D…) (When @OpenAI were developing the highly anticipated ChatGPT-4, it seems they may have hit this problem already. Because instead of scaling up their training sets, like previous iterations of their model, leaks indicate that ChatGPT-4 may actually be 8 x ChatGPT3 models tethered together (thealgorithmicbridge.substack.com/p/gpt-4s-secre…), explaining why it was delayed and why the dataset size was not revealed.) But even if you believe they'll find a way around that problem, there's still plenty of others waiting for us. What if I told you that language model technology isn't actually new? What if I told you it's largely the same as it was in the 1980s, but the only thing that's changed is the transformer technology, allowing for more efficient training, and the sheer size of the training data: The public internet. Yes, the thing that gives ChatGPT (and other LLMs) their "magic" is the fact that the internet exists now and can be scraped. (After it's being manually catalogued by hundreds of thousands of foreign workers, of course (theglobeandmail.com/business/artic…).) 300 billions words from the internet were used to train ChatGPT-4. It's the scale of this training dataset that allows it to sound so human and knowledgable. There's nothing else like it. Not only are major companies preventing their content from being used in future AI training datasets (deadline.com/2023/10/bbc-wi…) but there's a lingering question on whether or not it was even legal for them to use their original datasets in the first place (theverge.com/2023/9/11/2386…). But worse than that, the internet is increasingly being polluted with error-ridden AI generated content. So much so that it's infecting search results (wired.com/story/fast-for…) And in models that talk to the internet, these mistakes are now being fed back to us through AIs (futurism.com/google-search-…). (m.facebook.com/story.php?stor…). Sometimes even sharing conspiracy theories as fact, oops (washingtonpost.com/technology/202…). And now that AI generated content is everywhere, that means it's in the next training dataset. Except you can't train AI on AI generated content (arxiv.org/pdf/2307.01850…). And no, you can't reliably detect AI generated content either (x.com/atoosachegini/…). So, even if bigger meant better, and there wasn't an inverse scaling issue, where is the next dataset going to come from? How can we keep AIs up to date if they are polluting their own learning pool? There is no other conclusion than the future looks bleak for generative AI...

@SavageFridays Cheers, Jenn - enjoyed the live CLOC Talk at the end!

@LegalOpsHezzle Thanks for these abridged notes! The consistency point is absolutely critical here.

Here are my paraphrased notes from Jim Wagner's comments in the "Generative AI: How to Find the Perfect Fit" session. It was an interesting session with some other speakers (hence notes jumping around topics):

@nwaisb Maybe there might be more success for vendors in the second wave, where other inspired orgs / competitors are looking for something similar but can sell it a bit more out of the box in a more scalable and sustainable way.

@nwaisb These phases seemingly being done with a sandbox GenAI tool, consultants and SMEs building and testing on the fly. No real space for tool vendors unless they've already hit on and have successfully marketed their solution in that very specific space.

Similar strand at #ILTACON & #LVNx: more sophisticated customers seem more inclined to build their own gen AI tools (eg, via OpenAI API) rather than licensing vendor-built ones Reasons heard: cost, sense most gen AI tools aren't far along, uncertainty over which products'll win