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welcome to money is freedom

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a podcast

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exploring how finance and freedom connect in our lives

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created from thoughtful research

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and narrated by Notebook LM AI

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this series brings you clear

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meaningful insights into finance and beyond

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welcome to Episode 11 where we're diving into

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the transformative power of generative AI

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in 2024

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from redefining enterprise workflows

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to sparking a wave of startup innovation

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this technology is no longer a concept of the future

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it's happening now

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join us as we unpack the latest trends

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challenges and predictions shaping the AI

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revolution in the business world

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let's get started

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hey everyone

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welcome back ready to dive into something really cool

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today we're gonna take a deep dive into the world of AI

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oh yeah

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specifically how it's changing the game for businesses

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absolutely

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and we've got this fantastic report from menloventures

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it's called 2024

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The State of Generative AI in the enterprise

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it's a great report and it's packed with insights

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I think you're gonna find really fascinating

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definitely and it's not just about the future anymore

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right this is about what's happening now

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right now this is all happening so fast

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exactly the shift is happening incredibly quickly

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okay so let's unpack this

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the report highlights a massive increase in AI

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spending just this year huge

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we're talk $13.8 billion yeah

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that's six times what it was in 2023

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I mean that's a huge jump

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what's driving that massive investment

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well it's a clear sign that companies are moving beyond

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just experimenting with AI

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you know they're starting to see real potential for AI

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to become a core part of their business strategy

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so they're

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not just dipping their toes in the water anymore

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no they're diving in headfirst

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they're going all in absolutely

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and it's not just hype either

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the report shows that

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40% of that spending is coming from permanent budgets

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not just innovation funds

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so it's not just like a little side project

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no it's becoming a core part of how they do business

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okay so they're serious about this

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hmm but where's all this money going

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well that's where it gets really interesting

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the report digs into the specifics of how

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companies are actually using AI

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and some of them might surprise you

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okay late on it okay

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one of the hottest areas is what they call code

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co pilots oh yeah

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I've heard about this it's like having an AI

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partner that helps you write code right

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exactly it's like having an AI

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pair programmer right there with you

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like Github co pilot yeah

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Github co pilot's a great example

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and it's already a massive success

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it's got a 300 million dollar revenue run rate

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wow so they're making serious bank

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oh yeah

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developers are really embracing these tools

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it makes sense it's like

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it frees you up to focus on the bigger picture

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exactly and it's not just about general coding either

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companies are building AI

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tools for specific tasks within software development

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okay so give me an example

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how are these specialized AI

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tools actually changing the game for developers

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well think about things like automating code

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testing or generating code for specific tasks

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you know

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those repetitive tasks that developers used to spend

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hours on right

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so harness for example

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they created an AI Devops engineer and a Q a assistant

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wow those AI

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tools

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are taking care of all those time consuming tasks

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so the AI is doing the GRET work

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and the developers can focus on

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the more strategic stuff the creative stuff

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absolutely I like it

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what else are companies using AI for

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well another major area is customer support okay

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you know think about those 2047 chat bots

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they can actually understand your questions

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and troubleshoot your problems

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okay but

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are we talking about those basic jackpots

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that just kind of frustrate you more than they help

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no no no

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we're talking about something much more advanced

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like actual intelligence exactly

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companies like Isara Decagon Sierra

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they're creating AI chat bots that are so advanced

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they can actually interact with customers in a

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personalized way

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okay so they understand context

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they understand the context of your questions

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they learn from pass interactions

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and they can even adapt

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their communication style to different customer

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personalities that's wild

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yeah

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and it's not just about customer facing support either

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observe AI is using AI

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to support the human agents themselves

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giving them real time guidance during calls

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oh that's interesting

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so they're like AI coaches

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exactly wow

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so we've got AI helping developers write code

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we've got AI supporting customer service agents

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what other tasks are companies handing over to these AI

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assistants we'll think about

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all

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the information that's locked away in company's data

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right you know emails

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documents spreadsheets

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it's a gold mine of knowledge

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it is

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but it but it's often really hard to find what you need

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it's like trying to find a needle in a haystack

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exactly and that's where AI

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is proving to be incredibly powerful

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companies like glean and Sanna

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they're using AI to completely change how we search

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and retrieve information so how does that work

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what makes these AI powered search tools so different

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well instead of just looking for keywords

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these AI

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tools can actually understand what you're looking for

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the meaning and the context of your search

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that's cool they connect to all sorts of data sources

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and then they use natural language processing to find

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the most relevant information

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so it's like having an AI research assistant

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that's a great way to put it

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who just goes and digs through all that stuff

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and pulls out exactly what you need

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and they can even

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extract data and transform it into a format

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that's easy to analyze and understand

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so it's about turning raw data into actional insights

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that's really cool yeah

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this is all so impressive

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it seems like AI is becoming essential

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for any business that wants to stay

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ahead of the curve it absolutely is

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yeah and we've only just scratched the surface

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there's so much more to explore

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okay well

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I'm ready to keep going alright

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how about AI powered meetings

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okay you've got my attention

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tell me about this AI meeting magic

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imagine this

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no more struggling to take notes during meetings

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uh huh or missing key information

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I like where this is going

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there are tools like Fireflies Dot AI

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Otter Dot AI okay

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they can record and transcribe your meetings

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then they use AI to generate concise summaries

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highlight action items

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and even flag important decisions

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okay so no more going back and forth

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trying to remember who said what exactly

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and you can be sure that everyone's on the same page

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about next steps this is great

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I love it and it's not just for like business meetings

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right right

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it can be used in all sorts of contexts

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there's even a company Ilayo's Health

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that's using AI to automate documentation for doctors

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wow that's amazing

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doctors can finally spend less time on paperwork

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and more time with their patients

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it's a win win it really is

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so we've got AI helping developers write code

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we've got AI supporting customer service agents

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we've got AI revolutionizing meetings

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what's right what else

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well we've talked a lot about AI augmenting human work

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but what about AI taking over entirely

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oh yeah that's a big question

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is that something we're actually seeing happening

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well it's still early days okay

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but the report suggests that we're on the brink of a

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transition to more independent AI system okay

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so AI

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actually making decisions

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and taking actions on its own exactly

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it's a big shift what would that even look like

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well there are some fascinating examples popping up

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in areas like financial back office operations

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companies like Ford and simafor are developing AI

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that can handle complex financial tasks

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like reconciliation and reporting

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with very little human intervention

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exactly so it's like having an AI CFO running the show

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it's getting close

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but that brings up some interesting questions right

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definitely

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like how much control are we willing to give AI

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yeah for sure

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it's something we need to think carefully about

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definitely

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but before we start worrying about robots taking over

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let's focus on what's happening right now okay

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you mentioned this trend toward services as software

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yes Menlo Ventures is calling it

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the services as software era

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can you tell me more about that

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sure so basically

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it means that AI solutions

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can deliver

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the same capabilities as traditional service providers

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but with the speed and efficiency of software okay

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it's a bold prediction it is

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but considering how quickly AI is developing

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it's not out of the realm of possibility

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no it's not

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uh huh

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so with all these incredible AI solutions popping up

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are companies building these in house

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or are they buying them from vendors

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what's the trend there that's a great question

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and it turns out it's almost an even split

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47% are building their own AI solutions

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while 53% are going with vendors

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that's really interesting

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I would have guessed that

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most companies would prefer to buy ready

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made solutions well

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that was the trend last year

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80% were relying on vendors

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oh wow

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but this shift toward building inhouse

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shows that companies

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are

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getting more comfortable with developing their own AI

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tools so they realizing that they have the talent

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expertise to do it themselves

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exactly

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it's a sign of growing confidence in the enterprise AI

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space yeah

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that's really cool

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and regardless of whether their building or buying

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companies are laser focused on ROI right

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so they wanna make sure that their AI

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investments are paying off exactly

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they're not just jumping on the AI bandwagon

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they're choosing solutions

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that will actually make a difference

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for their business and you know what's surprising

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what's that

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price isn't as big of a factor as you might think

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hmm

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only 1% of companies mentioned price as a major concern

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that's right they're willing to invest in quality

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solutions that deliver real value

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even if it cost a bit more up front

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exactly it's a long term investment

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it's not a quick fix odd at all

280
00:09:29,533 --> 00:09:31,400
but even with the best intentions

281
00:09:31,433 --> 00:09:33,766
there are still some potential pitfalls

282
00:09:33,766 --> 00:09:36,233
when implementing AI oh absolutely

283
00:09:36,333 --> 00:09:37,533
it's not all smooth sailing

284
00:09:37,533 --> 00:09:39,433
what kind of challenges are companies facing

285
00:09:39,433 --> 00:09:40,300
well one of the biggest

286
00:09:40,300 --> 00:09:43,533
traps is underestimating implementation costs okay

287
00:09:43,533 --> 00:09:44,833
you know it's easy to get caught up in

288
00:09:44,833 --> 00:09:47,400
the initial price tag of an AI solution

289
00:09:47,566 --> 00:09:47,900
but then

290
00:09:47,900 --> 00:09:49,966
you forget about the hitting costs of integration

291
00:09:49,966 --> 00:09:51,233
training ongoing support

292
00:09:51,233 --> 00:09:53,600
what else does extra expenses exactly

293
00:09:53,600 --> 00:09:56,566
and in fact 26% of failed AI

294
00:09:56,566 --> 00:09:57,600
pilot programs

295
00:09:57,600 --> 00:10:00,366
cited implementation costs as a major factor

296
00:10:00,366 --> 00:10:01,866
wow so it's a real issue

297
00:10:01,966 --> 00:10:02,733
it is

298
00:10:02,733 --> 00:10:05,100
and then there are data privacy issues to think about

299
00:10:05,100 --> 00:10:05,566
right you know

300
00:10:05,566 --> 00:10:08,433
AI systems often need access to sensitive data

301
00:10:08,433 --> 00:10:10,700
so companies need to make sure they have strict

302
00:10:10,700 --> 00:10:12,700
data governance policies in place

303
00:10:12,700 --> 00:10:14,433
to comply with regulations

304
00:10:14,433 --> 00:10:16,266
and protect customer privacy

305
00:10:16,900 --> 00:10:18,966
especially with all the data breaches happening

306
00:10:18,966 --> 00:10:21,133
these days it's a serious consideration

307
00:10:21,133 --> 00:10:22,133
it is and then

308
00:10:22,133 --> 00:10:23,700
there's always the chance that AI

309
00:10:23,700 --> 00:10:26,733
projects just don't deliver the expected results right

310
00:10:26,733 --> 00:10:28,800
right 18% of failed AI

311
00:10:28,800 --> 00:10:31,733
pilots were attributed to disappointing ROI

312
00:10:31,733 --> 00:10:34,133
it's a reminder that AI isn't a magic bullet

313
00:10:34,133 --> 00:10:34,766
no it's not

314
00:10:34,766 --> 00:10:35,966
it needs to be implemented

315
00:10:35,966 --> 00:10:37,833
strategically to be successful

316
00:10:37,833 --> 00:10:39,833
so what's the key takeaway here

317
00:10:39,833 --> 00:10:43,233
what can companies do to avoid these pitfalls

318
00:10:43,400 --> 00:10:45,733
well planning is crucial okay

319
00:10:45,733 --> 00:10:47,400
businesses need to approach AI

320
00:10:47,400 --> 00:10:49,966
implementations strategically

321
00:10:49,966 --> 00:10:51,433
not impulsively exactly

322
00:10:51,433 --> 00:10:52,433
so you gotta do your research

323
00:10:52,433 --> 00:10:54,500
you gotta do your homework before you just dive in

324
00:10:54,500 --> 00:10:56,400
right assess your needs

325
00:10:56,400 --> 00:10:58,766
thoroughly evaluate different solutions

326
00:10:58,766 --> 00:10:59,833
carefully consider

327
00:10:59,833 --> 00:11:02,100
all the potential costs and challenges before

328
00:11:02,100 --> 00:11:04,466
making any big decisions that's great advice

329
00:11:04,633 --> 00:11:06,766
now earlier we talked about the rise of startups

330
00:11:06,766 --> 00:11:08,433
in the AI space uh huh

331
00:11:08,433 --> 00:11:09,833
is there any data

332
00:11:09,833 --> 00:11:11,433
on how they're stacking up against the big

333
00:11:11,433 --> 00:11:13,000
establish players yeah

334
00:11:13,000 --> 00:11:14,300
that's a great question

335
00:11:14,366 --> 00:11:17,000
and the report offer some fascinating insights

336
00:11:17,000 --> 00:11:18,600
remember how last year

337
00:11:18,633 --> 00:11:20,766
incumbent vendors were pretty much dominating

338
00:11:20,766 --> 00:11:21,400
the market yeah

339
00:11:21,400 --> 00:11:23,133
I felt like they were just kind of adding AI

340
00:11:23,133 --> 00:11:25,500
features onto their existing products right

341
00:11:25,500 --> 00:11:27,133
more incremental steps than

342
00:11:27,133 --> 00:11:29,100
truly groundbreaking innovations

343
00:11:29,100 --> 00:11:30,200
exactly but Menlo

344
00:11:30,200 --> 00:11:31,600
Ventras predicted that startups

345
00:11:31,600 --> 00:11:33,000
would start gaining ground

346
00:11:33,000 --> 00:11:35,900
okay and the data now supports that prediction

347
00:11:35,900 --> 00:11:38,400
interesting while 64% of businesses

348
00:11:38,400 --> 00:11:40,700
still prefer to buy from established vendors

349
00:11:40,700 --> 00:11:40,966
there

350
00:11:40,966 --> 00:11:43,533
are signs that this dominance is starting to waver

351
00:11:43,533 --> 00:11:44,600
really what are the signs

352
00:11:44,600 --> 00:11:46,700
well 18% of decision makers

353
00:11:46,700 --> 00:11:49,000
reported being disappointed with offerings from

354
00:11:49,000 --> 00:11:50,233
incumbent vendors okay

355
00:11:50,233 --> 00:11:52,000
and a whopping 40% question

356
00:11:52,000 --> 00:11:52,533
whether their

357
00:11:52,533 --> 00:11:55,033
current solutions were actually meeting their needs

358
00:11:55,433 --> 00:11:56,000
wow so

359
00:11:56,000 --> 00:11:57,633
that's a pretty big chunk of the market

360
00:11:57,633 --> 00:11:59,133
that's open to new solutions

361
00:11:59,133 --> 00:11:59,800
exactly

362
00:11:59,800 --> 00:12:02,100
it's a prime opportunity for startups to come in

363
00:12:02,100 --> 00:12:03,600
and disrupt the status quo

364
00:12:03,600 --> 00:12:04,133
okay so

365
00:12:04,133 --> 00:12:05,500
we've established that AI

366
00:12:05,500 --> 00:12:07,900
is impacting businesses across the board

367
00:12:08,233 --> 00:12:11,500
but is it really being adopted by all departments

368
00:12:11,700 --> 00:12:15,100
or is it mainly concentrated in certain areas like it

369
00:12:15,200 --> 00:12:16,900
that's a great question and

370
00:12:16,900 --> 00:12:17,133
one of

371
00:12:17,133 --> 00:12:19,733
the most remarkable findings in this report is that AI

372
00:12:19,733 --> 00:12:23,200
adoption is happening across all departments really

373
00:12:23,200 --> 00:12:25,466
it's not just a tech thing anymore

374
00:12:25,700 --> 00:12:28,133
every part of the business is feeling the impact

375
00:12:28,133 --> 00:12:29,633
so it's not just the it in

376
00:12:29,633 --> 00:12:31,800
engineering departments leading the charge

377
00:12:31,966 --> 00:12:33,100
well those departments

378
00:12:33,100 --> 00:12:36,233
definitely get the lion's share of the AI budget okay

379
00:12:36,233 --> 00:12:38,100
with it at 22%

380
00:12:38,100 --> 00:12:42,300
product in engineering at 19% and data science at 8%

381
00:12:42,300 --> 00:12:43,033
okay but

382
00:12:43,033 --> 00:12:45,500
the remaining budget is spread across a wide range of

383
00:12:45,500 --> 00:12:47,166
functions so give me some examples

384
00:12:47,166 --> 00:12:50,200
where else are companies investing in AI

385
00:12:50,233 --> 00:12:50,400
well

386
00:12:50,400 --> 00:12:52,800
you've got customer facing departments like support

387
00:12:52,800 --> 00:12:54,333
sales and marketing okay

388
00:12:54,333 --> 00:12:55,533
they're all investing in AI

389
00:12:55,533 --> 00:12:57,833
supports at 9% sales at 8%

390
00:12:57,833 --> 00:12:59,800
marketing at 7% uh huh

391
00:12:59,800 --> 00:13:01,600
then there are the back office teams

392
00:13:01,600 --> 00:13:04,866
like HR and finance also at 7% each

393
00:13:05,100 --> 00:13:07,700
and even smaller departments like design at 6%

394
00:13:07,700 --> 00:13:10,533
and legal at 3% are getting in on the action

395
00:13:10,533 --> 00:13:13,733
so AI really is becoming an organizationwide phenomenon

396
00:13:13,733 --> 00:13:16,166
it's not just a niche technology anymore

397
00:13:16,166 --> 00:13:18,233
it's something that every department is thinking about

398
00:13:18,233 --> 00:13:19,533
exactly and it makes sense right

399
00:13:19,533 --> 00:13:19,800
yeah

400
00:13:19,800 --> 00:13:22,833
AI has the potential to transform how work gets done

401
00:13:22,833 --> 00:13:25,133
regardless of the specific industry or department

402
00:13:25,133 --> 00:13:26,333
I think that's a great point

403
00:13:26,333 --> 00:13:28,900
and that's why we're seeing this shift towards more

404
00:13:28,900 --> 00:13:31,133
specialized AI solutions

405
00:13:31,433 --> 00:13:34,200
tailor to the needs of different industries okay

406
00:13:34,200 --> 00:13:35,233
I'm intrigued

407
00:13:35,733 --> 00:13:38,800
tell me more about these vertical AI applications

408
00:13:38,800 --> 00:13:38,966
what

409
00:13:38,966 --> 00:13:41,500
are some of the most exciting developments happening

410
00:13:41,500 --> 00:13:43,166
in specific industries well

411
00:13:43,166 --> 00:13:45,400
one of the most surprising findings in

412
00:13:45,400 --> 00:13:47,800
the report is that health care is actually leading

413
00:13:47,800 --> 00:13:49,933
the charge when it comes to AI adoption

414
00:13:50,300 --> 00:13:53,500
you're pouring a massive $500 million into

415
00:13:53,500 --> 00:13:55,433
enterprise AI spending this year

416
00:13:55,433 --> 00:13:57,400
wow that's a huge investment

417
00:13:57,400 --> 00:13:59,100
for an industry that's not always known

418
00:13:59,100 --> 00:14:01,333
for being quick to embrace new technology

419
00:14:01,700 --> 00:14:04,300
what's driving this spending spree in healthcare

420
00:14:04,300 --> 00:14:04,600
well

421
00:14:04,600 --> 00:14:06,700
one area that's really taking off is what they call

422
00:14:06,700 --> 00:14:08,800
ambient scribes ambient scribes

423
00:14:08,800 --> 00:14:10,400
these are AI powered tools

424
00:14:10,400 --> 00:14:13,166
that can listen in on conversations between doctors

425
00:14:13,166 --> 00:14:16,500
and patients and automatically generate clinical notes

426
00:14:16,500 --> 00:14:19,166
so doctors can finally ditch the tedious paperwork

427
00:14:19,166 --> 00:14:20,700
right and focus more on their patients

428
00:14:20,700 --> 00:14:22,300
that's a win win for everybody

429
00:14:22,300 --> 00:14:25,533
it really is companies like Abridge Ambience

430
00:14:25,533 --> 00:14:28,333
Heidi Alayos Health they're all developing ambient

431
00:14:28,333 --> 00:14:29,566
scribe solutions that

432
00:14:29,566 --> 00:14:32,000
are gaining popularity in doctors offices

433
00:14:32,000 --> 00:14:33,533
and that's just one example right

434
00:14:33,533 --> 00:14:36,366
and then there's this whole area of clinical workflow

435
00:14:36,366 --> 00:14:37,366
automation uh huh

436
00:14:37,366 --> 00:14:40,100
where AI is being used to streamline everything

437
00:14:40,100 --> 00:14:41,933
from patient intake in triage

438
00:14:41,933 --> 00:14:44,066
to filling encoding

439
00:14:44,100 --> 00:14:46,366
so it's really revolutionizing healthcare

440
00:14:46,366 --> 00:14:47,800
absolutely from top to bottom

441
00:14:47,800 --> 00:14:50,433
yeah it's not just about improving efficiency either

442
00:14:50,433 --> 00:14:52,666
it's about enhancing the quality of care

443
00:14:52,733 --> 00:14:55,500
and making healthcare more accessible and affordable

444
00:14:55,500 --> 00:14:57,233
I think those are really important goals

445
00:14:57,233 --> 00:15:00,533
absolutely and those are just a few examples of how AI

446
00:15:00,533 --> 00:15:02,700
is transforming the healthcare landscape

447
00:15:03,033 --> 00:15:04,966
another industry that's right for disruption

448
00:15:04,966 --> 00:15:06,800
is the legal profession okay

449
00:15:06,800 --> 00:15:09,200
you know they're traditionally resistant to change

450
00:15:09,300 --> 00:15:12,700
but they're starting to embrace AI in a big way really

451
00:15:12,700 --> 00:15:14,900
so how's AI actually being used in law

452
00:15:14,900 --> 00:15:15,200
well

453
00:15:15,200 --> 00:15:18,400
lawyers deal with massive amounts of unstructured data

454
00:15:18,800 --> 00:15:20,800
you know contracts

455
00:15:20,833 --> 00:15:22,666
legal briefs case files

456
00:15:22,766 --> 00:15:24,000
and AI is proving

457
00:15:24,000 --> 00:15:26,900
to be incredibly helpful in managing and analyzing

458
00:15:26,900 --> 00:15:28,000
all that information

459
00:15:28,233 --> 00:15:31,133
we're talking $350 million in enterprise spending

460
00:15:31,133 --> 00:15:32,300
this year so it's like AI

461
00:15:32,300 --> 00:15:33,900
is coming in to help them

462
00:15:34,000 --> 00:15:35,433
sift through all those documents

463
00:15:35,433 --> 00:15:37,633
exactly and find the key information they need

464
00:15:37,633 --> 00:15:39,133
and it can also help them with those

465
00:15:39,133 --> 00:15:40,766
really tedious tasks right

466
00:15:40,766 --> 00:15:43,533
like contract review and legal research

467
00:15:43,533 --> 00:15:45,200
so it's freeing up the lawyers too

468
00:15:45,233 --> 00:15:46,966
to focus on the more strategic

469
00:15:46,966 --> 00:15:49,133
and creative aspects of their work exactly

470
00:15:49,133 --> 00:15:51,933
so it's the same story we're seeing across industries

471
00:15:52,300 --> 00:15:54,333
AI taking care of the grunt work

472
00:15:54,333 --> 00:15:56,633
so humans can focus on the high value task

473
00:15:56,633 --> 00:15:57,400
a common theme

474
00:15:57,400 --> 00:15:59,900
what other industries are jumping on the AI

475
00:16:00,100 --> 00:16:00,566
well

476
00:16:00,566 --> 00:16:03,233
financial services is another hot bed of AI innovation

477
00:16:03,233 --> 00:16:05,700
they're investing $100 million in enterprise AI

478
00:16:05,700 --> 00:16:06,700
this year okay

479
00:16:06,700 --> 00:16:09,733
so what are the big AI plays in the world of finance

480
00:16:09,733 --> 00:16:11,366
finance is all about data right

481
00:16:11,366 --> 00:16:12,766
accounting research compliance

482
00:16:12,766 --> 00:16:14,300
risk management AI

483
00:16:14,300 --> 00:16:17,600
is automating a lot of those data intensive processes

484
00:16:17,600 --> 00:16:20,166
giving financial professionals more time to focus on

485
00:16:20,166 --> 00:16:23,433
strategic thinking and high level decision making

486
00:16:23,700 --> 00:16:25,400
so less number crunching exact

487
00:16:25,400 --> 00:16:26,900
more big picture thinking right

488
00:16:26,900 --> 00:16:27,633
so for example

489
00:16:27,633 --> 00:16:30,033
you have companies like Numerican Clarity

490
00:16:30,033 --> 00:16:33,900
revolutionizing accounting with AI powered tools okay

491
00:16:34,000 --> 00:16:35,000
Aki Fienrogo

492
00:16:35,000 --> 00:16:37,033
are accelerating financial research through

493
00:16:37,033 --> 00:16:39,300
advance data extraction and analysis

494
00:16:39,566 --> 00:16:41,733
and companies like arch are using AI

495
00:16:41,733 --> 00:16:44,366
to shake things up in back office operations

496
00:16:44,366 --> 00:16:46,033
for investment firms so

497
00:16:46,033 --> 00:16:46,333
AI

498
00:16:46,333 --> 00:16:48,966
is having a pretty significant impact on how financial

499
00:16:48,966 --> 00:16:50,900
institutions operate absolutely

500
00:16:51,033 --> 00:16:52,966
and then there's this whole area of compliance

501
00:16:52,966 --> 00:16:54,333
and risk management right

502
00:16:54,333 --> 00:16:55,100
keeping up with

503
00:16:55,100 --> 00:16:57,433
ever changing regulations is a huge challenge

504
00:16:57,433 --> 00:16:58,800
for financial institutions

505
00:16:58,800 --> 00:17:02,000
it is but AI is helping those

506
00:17:02,333 --> 00:17:04,200
companies like Greenlight and Norm

507
00:17:04,200 --> 00:17:06,566
AI are developing real time compliance

508
00:17:06,566 --> 00:17:08,633
monitoring solutions that use AI

509
00:17:08,633 --> 00:17:10,566
to flag potential violations

510
00:17:10,566 --> 00:17:11,366
it was like AI

511
00:17:11,366 --> 00:17:13,966
is becoming the compliance officer's new best friend

512
00:17:13,966 --> 00:17:15,400
yeah you could say that

513
00:17:15,833 --> 00:17:17,700
and then there's media and entertainment

514
00:17:17,700 --> 00:17:20,600
which is also seeing a surge in AI adoption

515
00:17:21,000 --> 00:17:23,733
we're talking $100 million in enterprise spending

516
00:17:23,733 --> 00:17:24,700
this year okay

517
00:17:24,700 --> 00:17:26,500
so AI is making its way to Hollywood

518
00:17:26,500 --> 00:17:28,400
that's right tools like runway

519
00:17:28,400 --> 00:17:30,433
have become essential for things like video

520
00:17:30,433 --> 00:17:31,700
editing and special effects

521
00:17:31,700 --> 00:17:34,633
uh huh and platforms like Black Forest Labs

522
00:17:34,766 --> 00:17:36,200
Higgsfield ideogram

523
00:17:36,200 --> 00:17:37,233
mid journey pika

524
00:17:37,233 --> 00:17:38,333
wow that's a lot

525
00:17:38,333 --> 00:17:40,633
they're all pushing the boundaries of AI

526
00:17:40,633 --> 00:17:42,133
powered content creation

527
00:17:42,133 --> 00:17:45,833
so we're talking about AI helping to generate scripts

528
00:17:45,900 --> 00:17:48,833
compose music even create entire movies

529
00:17:48,833 --> 00:17:50,633
it's not quite at that level yet

530
00:17:50,633 --> 00:17:51,966
but it's getting there

531
00:17:51,966 --> 00:17:54,600
and it's not just about big budget productions

532
00:17:54,733 --> 00:17:57,533
AI is also empowering individual creators with tools

533
00:17:57,533 --> 00:17:58,966
like captions and descript okay

534
00:17:58,966 --> 00:18:00,966
which make it easier to create high quality

535
00:18:00,966 --> 00:18:02,433
audio and video content

536
00:18:02,433 --> 00:18:04,900
it's like AI is democratizing content creation

537
00:18:04,900 --> 00:18:05,566
exactly

538
00:18:05,566 --> 00:18:08,533
giving everyone access to tools that were once only

539
00:18:08,533 --> 00:18:09,900
available to professionals

540
00:18:09,900 --> 00:18:12,600
I love that it's really exciting to see how AI

541
00:18:12,600 --> 00:18:15,333
is changing the game for creators of all levels

542
00:18:15,333 --> 00:18:16,800
we've covered so much ground already

543
00:18:16,800 --> 00:18:18,566
but I have one more question before we move on

544
00:18:18,566 --> 00:18:20,633
we've talked about all these amazing applications

545
00:18:20,633 --> 00:18:21,933
but I wanna step back

546
00:18:22,166 --> 00:18:24,700
and look at the tech behind it all

547
00:18:24,933 --> 00:18:27,366
what's the infrastructure that makes all this AI

548
00:18:27,366 --> 00:18:29,200
magic happen that's a great point

549
00:18:29,200 --> 00:18:30,500
all these incredible AI

550
00:18:30,500 --> 00:18:33,166
applications wouldn't be possible without a solid

551
00:18:33,166 --> 00:18:36,600
foundation so let's talk about the modern AI stack

552
00:18:36,600 --> 00:18:38,600
what are the key components that power

553
00:18:38,600 --> 00:18:40,100
all of this innovation well

554
00:18:40,100 --> 00:18:42,100
after a year of rapid evolution

555
00:18:42,133 --> 00:18:44,800
the modern AI stack has stabilized somewhat

556
00:18:44,933 --> 00:18:45,566
businesses are

557
00:18:45,566 --> 00:18:47,833
converging around a core set of building blocks that

558
00:18:47,833 --> 00:18:50,800
form the foundation of most production AI systems

559
00:18:50,800 --> 00:18:51,400
so there's like

560
00:18:51,400 --> 00:18:52,966
a common blueprint emerging

561
00:18:52,966 --> 00:18:55,333
for how to build and deploy AI solutions

562
00:18:55,333 --> 00:18:58,500
exactly n at the heart of this blueprint

563
00:18:58,500 --> 00:19:00,400
are what we call foundation models

564
00:19:00,400 --> 00:19:03,466
okay these are large language models or L LMS

565
00:19:03,500 --> 00:19:05,400
it has been trained on massive datasets

566
00:19:05,400 --> 00:19:06,933
and can do a wide range of things

567
00:19:06,933 --> 00:19:09,533
from generating text and translating languages

568
00:19:09,533 --> 00:19:11,733
to writing code and analyzing images

569
00:19:11,733 --> 00:19:11,933
so

570
00:19:11,933 --> 00:19:14,166
foundation models are like the brains of the operation

571
00:19:14,166 --> 00:19:15,900
wow that's a great way to put it

572
00:19:16,133 --> 00:19:17,566
and these brains are getting bigger

573
00:19:17,566 --> 00:19:19,300
and more powerful all the time

574
00:19:19,433 --> 00:19:21,433
and businesses are pouring a massive

575
00:19:21,433 --> 00:19:24,900
$6.5 billion into this LLM layer

576
00:19:25,433 --> 00:19:26,833
but they're also learning that it takes

577
00:19:26,833 --> 00:19:28,766
more than just a powerful LLM

578
00:19:28,766 --> 00:19:31,133
to create successful AI solutions

579
00:19:31,133 --> 00:19:31,900
what else do they need

580
00:19:31,900 --> 00:19:33,700
they need what's called data scaffolding

581
00:19:33,700 --> 00:19:35,400
okay it's about building the right

582
00:19:35,400 --> 00:19:37,366
infrastructure around the LLM

583
00:19:37,366 --> 00:19:40,700
to make sure it can access and process data effectively

584
00:19:40,900 --> 00:19:42,700
and generate reliable outputs

585
00:19:42,700 --> 00:19:43,100
so it's like

586
00:19:43,100 --> 00:19:45,800
having the right support system to help that AI

587
00:19:45,800 --> 00:19:47,033
brain function at its best

588
00:19:47,033 --> 00:19:50,366
exactly that includes things like data pipelines

589
00:19:50,366 --> 00:19:53,833
data cleansing tools integration with existing systems

590
00:19:53,833 --> 00:19:56,533
it's a complex ecosystem working together

591
00:19:56,633 --> 00:19:58,400
it sounds like there are a lot of moving parts

592
00:19:58,400 --> 00:20:00,500
are there any clear trends emerging

593
00:20:00,500 --> 00:20:03,200
in how companies are using these foundation models

594
00:20:03,200 --> 00:20:04,766
to build solutions yeah

595
00:20:04,766 --> 00:20:07,133
one interesting trend is the rise of multi

596
00:20:07,133 --> 00:20:08,800
model strategies okay

597
00:20:08,800 --> 00:20:11,533
businesses are realizing that it's often better to use

598
00:20:11,533 --> 00:20:13,000
multiple foundation models

599
00:20:13,000 --> 00:20:14,333
instead of relying on just one

600
00:20:14,333 --> 00:20:16,133
so it's like having a team of experts

601
00:20:16,133 --> 00:20:18,333
that's a great analogy each with their own specialty

602
00:20:18,333 --> 00:20:20,000
exactly companies might use

603
00:20:20,000 --> 00:20:22,300
one foundation model for text generation

604
00:20:22,300 --> 00:20:23,800
another for image analysis

605
00:20:23,800 --> 00:20:26,166
and yet another for co generation right

606
00:20:26,166 --> 00:20:28,300
so it's about choosing the right tool for the job

607
00:20:28,400 --> 00:20:29,700
exactly and in fact

608
00:20:29,700 --> 00:20:30,900
the report shows that companies

609
00:20:30,900 --> 00:20:32,533
are using an average of three or more

610
00:20:32,533 --> 00:20:34,933
foundation models in their AI stacks

611
00:20:35,000 --> 00:20:37,033
wow so is really mixing and matching

612
00:20:37,033 --> 00:20:39,433
they are and this multimodel approach

613
00:20:39,433 --> 00:20:42,000
probably plays into this whole debate about

614
00:20:42,333 --> 00:20:45,100
open versus closed source AI right

615
00:20:45,366 --> 00:20:47,400
and despite all the buzz around open stores

616
00:20:47,400 --> 00:20:48,900
AI closed source solutions

617
00:20:48,900 --> 00:20:50,233
are still dominating the market

618
00:20:50,233 --> 00:20:52,433
with 81% market share

619
00:20:52,600 --> 00:20:54,500
so companies are still playing it safe

620
00:20:54,500 --> 00:20:57,100
opting for the control and security of closed systems

621
00:20:57,100 --> 00:20:58,233
that seems to be the case

622
00:20:58,233 --> 00:21:01,400
but open source is holding strong at 19% okay

623
00:21:01,400 --> 00:21:04,766
LED by metas Lama two so it's not a complete shutout

624
00:21:04,766 --> 00:21:06,866
so the competition is still on hmm

625
00:21:07,033 --> 00:21:09,033
are there any other interesting shake UPS happening

626
00:21:09,033 --> 00:21:10,300
in the closed source world

627
00:21:10,300 --> 00:21:11,166
oh definitely

628
00:21:11,166 --> 00:21:14,566
open AI the creator of Chat GPT was the early leader

629
00:21:14,566 --> 00:21:14,833
right

630
00:21:14,833 --> 00:21:18,533
but their market share has dropped from 50% to 34% wow

631
00:21:18,533 --> 00:21:21,300
that's a pretty big drop what's behind that

632
00:21:21,366 --> 00:21:22,000
philanthropic

633
00:21:22,000 --> 00:21:24,533
a company that's focused on safe and ethical AI

634
00:21:24,533 --> 00:21:26,900
seems to be the main beneficiary of this shift

635
00:21:27,000 --> 00:21:30,600
their market share has doubled from 12% to 24%

636
00:21:30,600 --> 00:21:32,366
thanks in part to their new model

637
00:21:32,366 --> 00:21:35,000
Claude 3.5 sonnet okay

638
00:21:35,000 --> 00:21:38,600
which is seen as a strong contender to open AI's GPT4

639
00:21:38,833 --> 00:21:42,166
so businesses are really prioritizing safety in ethics

640
00:21:42,166 --> 00:21:44,200
when it comes to choosing their AI tools

641
00:21:44,200 --> 00:21:46,166
it's a trend that's likely to continue

642
00:21:46,166 --> 00:21:46,733
as AI

643
00:21:46,733 --> 00:21:49,500
becomes more powerful and integrated into our lives

644
00:21:49,500 --> 00:21:50,700
yeah definitely

645
00:21:50,733 --> 00:21:53,233
okay so we've got multi model strategies

646
00:21:53,333 --> 00:21:55,933
and a growing emphasis on safe and ethical AI

647
00:21:55,933 --> 00:21:59,033
any other major trends shaping the Lom landscape

648
00:21:59,166 --> 00:22:00,766
the landscape is constantly evolving

649
00:22:00,766 --> 00:22:02,033
but one of the most recent

650
00:22:02,033 --> 00:22:03,333
and interesting developments is

651
00:22:03,333 --> 00:22:06,300
Anthropic's release of Claude 3.5 sonnet

652
00:22:06,333 --> 00:22:08,733
this new model has some groundbreaking capabilities

653
00:22:08,733 --> 00:22:11,033
including the ability to use computers

654
00:22:11,300 --> 00:22:12,633
wait AI using computers

655
00:22:12,633 --> 00:22:13,966
that sounds a little too SCI fi for me

656
00:22:13,966 --> 00:22:15,800
I know it's kind of mind blowing right

657
00:22:15,800 --> 00:22:18,333
but anthropic is very focused on responsible AI

658
00:22:18,333 --> 00:22:19,166
development and has

659
00:22:19,166 --> 00:22:21,900
built in safeguards to ensure Claude 3.5

660
00:22:21,900 --> 00:22:24,566
Sonnet uses computers safely and ethically okay

661
00:22:24,566 --> 00:22:25,900
that makes me feel a little better

662
00:22:26,000 --> 00:22:29,233
but what does it even mean for an AI to use a computer

663
00:22:29,233 --> 00:22:30,400
can you give me an example

664
00:22:30,400 --> 00:22:31,766
sure imagine an AI

665
00:22:31,766 --> 00:22:34,700
that can not only access information on the internet

666
00:22:34,700 --> 00:22:37,233
but also interact with software applications

667
00:22:37,233 --> 00:22:40,100
run calculations and even generate its own code

668
00:22:40,233 --> 00:22:41,000
so it's like an AI

669
00:22:41,000 --> 00:22:42,933
that can basically do everything a human can do

670
00:22:42,933 --> 00:22:44,733
on a computer exactly

671
00:22:44,833 --> 00:22:47,566
and that opens up a whole new world of possibilities

672
00:22:47,566 --> 00:22:49,133
for what AI can accomplish

673
00:22:49,133 --> 00:22:50,500
this is getting pretty meta

674
00:22:50,766 --> 00:22:51,833
it's definitely exciting

675
00:22:51,833 --> 00:22:53,500
but also a little scary to think about

676
00:22:53,500 --> 00:22:55,300
I understand the apprehension

677
00:22:55,300 --> 00:22:56,966
it's a powerful technology

678
00:22:56,966 --> 00:22:58,433
and it's crucial that we develop

679
00:22:58,433 --> 00:22:59,900
and deploy it responsibly

680
00:23:00,300 --> 00:23:03,300
but the potential benefits are truly groundbreaking

681
00:23:03,300 --> 00:23:03,633
okay

682
00:23:03,633 --> 00:23:07,533
so the LLM landscape is clearly a hot pit of innovation

683
00:23:07,533 --> 00:23:10,000
and it's evolving at an incredible pace

684
00:23:10,300 --> 00:23:11,700
but let's shift gears for a moment

685
00:23:11,700 --> 00:23:13,533
and talk about design patterns okay

686
00:23:13,533 --> 00:23:13,833
what are

687
00:23:13,833 --> 00:23:15,100
the different ways that businesses

688
00:23:15,100 --> 00:23:17,500
are actually using these foundation models

689
00:23:17,500 --> 00:23:19,200
to build their AI solutions

690
00:23:19,200 --> 00:23:19,566
well

691
00:23:19,566 --> 00:23:21,900
the report highlights some interesting trends in AI

692
00:23:21,900 --> 00:23:23,066
design patterns

693
00:23:23,100 --> 00:23:25,500
and one of the most popular is called RGA

694
00:23:25,600 --> 00:23:28,566
which stands for Retrieval Augmented Generation

695
00:23:28,566 --> 00:23:29,500
R JAG

696
00:23:29,833 --> 00:23:31,033
that doesn't sound very high tech

697
00:23:31,033 --> 00:23:32,233
I know right

698
00:23:32,433 --> 00:23:32,633
but

699
00:23:32,633 --> 00:23:35,100
it's actually a really powerful technique that allows

700
00:23:35,100 --> 00:23:35,533
AI

701
00:23:35,533 --> 00:23:38,933
systems to access and use external knowledge sources

702
00:23:39,166 --> 00:23:41,833
okay so how does RGA actually work

703
00:23:41,833 --> 00:23:42,500
we'll think about

704
00:23:42,500 --> 00:23:44,400
building a chat bot that needs to answer

705
00:23:44,400 --> 00:23:47,333
customer questions about a specific product

706
00:23:47,333 --> 00:23:48,800
okay with RG

707
00:23:48,800 --> 00:23:51,733
you can connect that chat bot to a database of

708
00:23:51,733 --> 00:23:53,133
product information

709
00:23:53,433 --> 00:23:55,933
then when a customer asks a question

710
00:23:56,000 --> 00:23:56,600
the chat bot

711
00:23:56,600 --> 00:23:59,000
can instantly pull up the relevant information

712
00:23:59,000 --> 00:24:01,166
and use it to generate a helpful response

713
00:24:01,166 --> 00:24:01,600
oh okay

714
00:24:01,600 --> 00:24:02,000
so it's like

715
00:24:02,000 --> 00:24:02,833
giving the chat bot

716
00:24:02,833 --> 00:24:05,166
access to a giant library of knowledge

717
00:24:05,166 --> 00:24:07,366
exactly making it much smarter and more accurate

718
00:24:07,366 --> 00:24:09,833
that's right and it's becoming increasingly popular

719
00:24:09,833 --> 00:24:13,233
our egg adoption is up to 51% this year

720
00:24:13,233 --> 00:24:15,400
compared to just 31% last year

721
00:24:15,400 --> 00:24:17,366
that's a pretty big jump it is

722
00:24:17,366 --> 00:24:18,600
it shows that businesses

723
00:24:18,600 --> 00:24:20,166
are really starting to understand

724
00:24:20,166 --> 00:24:22,400
the value of giving their AI

725
00:24:22,400 --> 00:24:25,433
systems access to those external information sources

726
00:24:25,433 --> 00:24:27,900
it's about giving them the tools to truly learn

727
00:24:27,900 --> 00:24:29,000
and grow absolutely

728
00:24:29,000 --> 00:24:30,433
yeah and then there's fine tuning

729
00:24:30,433 --> 00:24:32,300
another design pattern that gets a lot of attention

730
00:24:32,300 --> 00:24:32,733
yes

731
00:24:32,733 --> 00:24:35,733
so fine tuning is like tweaking the settings on your AI

732
00:24:35,733 --> 00:24:36,966
that's a good way to think about it

733
00:24:36,966 --> 00:24:38,766
to make it work exactly the way you want

734
00:24:38,766 --> 00:24:41,200
you're taking a pre trained foundation model

735
00:24:41,200 --> 00:24:43,800
and training it further on a specific dataset

736
00:24:43,800 --> 00:24:46,200
to make it even better at a particular task

737
00:24:46,200 --> 00:24:49,633
so it's about customizing the AI to your specific needs

738
00:24:49,633 --> 00:24:52,300
exactly but what's interesting is that fine

739
00:24:52,300 --> 00:24:55,200
tuning is actually less common than you might think

740
00:24:55,200 --> 00:24:55,566
really

741
00:24:55,566 --> 00:24:59,133
only 9% of production models are being fine tuned

742
00:24:59,400 --> 00:25:00,633
that's surprising I would

743
00:25:00,633 --> 00:25:02,400
thought that most companies would wanna fine

744
00:25:02,400 --> 00:25:05,133
tune their AI models to get that optimal performance

745
00:25:05,200 --> 00:25:06,766
it's a common misconception

746
00:25:06,766 --> 00:25:08,933
but it turns out that for many applications

747
00:25:08,933 --> 00:25:10,600
fine tuning isn't necessary

748
00:25:10,600 --> 00:25:11,400
oh okay

749
00:25:11,400 --> 00:25:13,733
the pre trained foundation models are often good enough

750
00:25:13,733 --> 00:25:14,900
right out of the box well

751
00:25:14,900 --> 00:25:17,100
it's about choosing the right approach for the job

752
00:25:17,100 --> 00:25:17,833
exactly

753
00:25:17,833 --> 00:25:20,933
sometimes fine tuning is needed and sometimes it's not

754
00:25:21,133 --> 00:25:21,433
and then

755
00:25:21,433 --> 00:25:23,833
there's another design pattern that's gaining momentum

756
00:25:23,833 --> 00:25:27,233
okay and it's probably the most disruptive of all

757
00:25:27,766 --> 00:25:29,966
lay it on me egentic architectures

758
00:25:29,966 --> 00:25:33,333
egentic architectures that sounds intense

759
00:25:33,433 --> 00:25:34,633
what are we talking about here

760
00:25:34,633 --> 00:25:35,833
imagine an AI system

761
00:25:35,833 --> 00:25:38,866
that's not just capable of performing a single task

762
00:25:38,933 --> 00:25:41,033
but can actually interact with the world

763
00:25:41,033 --> 00:25:42,533
uh huh make decisions

764
00:25:42,533 --> 00:25:44,033
take actions on its own

765
00:25:44,033 --> 00:25:46,900
so it's like an AI that has its own agency

766
00:25:46,900 --> 00:25:48,900
exactly it can actually act on its own

767
00:25:48,900 --> 00:25:51,300
that's what agentic architectures are all about

768
00:25:51,300 --> 00:25:52,033
that's a big deal

769
00:25:52,033 --> 00:25:54,566
they're still in the early stages of development okay

770
00:25:54,566 --> 00:25:56,966
but they're already being used in 12% of AI

771
00:25:56,966 --> 00:25:58,500
implementation wow

772
00:25:58,500 --> 00:25:59,566
that's a pretty big number

773
00:25:59,566 --> 00:26:01,200
considering it such new concept

774
00:26:01,200 --> 00:26:02,166
it is what

775
00:26:02,166 --> 00:26:03,966
are some real world examples of

776
00:26:03,966 --> 00:26:06,233
how agentic architectures are being used

777
00:26:06,233 --> 00:26:08,800
well imagine a personal assistant AI

778
00:26:08,833 --> 00:26:11,500
that can not only manage your calendar

779
00:26:11,500 --> 00:26:12,733
and book appointments

780
00:26:12,933 --> 00:26:16,600
but it can also proactively find opportunities for you

781
00:26:16,966 --> 00:26:18,766
negotiate deals on your behalf

782
00:26:18,766 --> 00:26:20,533
and even manage your finances

783
00:26:20,533 --> 00:26:21,800
so it's not just an assistant

784
00:26:21,800 --> 00:26:24,833
it's like a true agent exactly

785
00:26:24,833 --> 00:26:26,366
representing you in the world

786
00:26:26,366 --> 00:26:27,400
that's incredible

787
00:26:27,400 --> 00:26:29,500
and that's just the tip of the iceberg

788
00:26:29,700 --> 00:26:31,000
egenic architectures have

789
00:26:31,000 --> 00:26:34,033
the potential to completely revolutionize everything

790
00:26:34,033 --> 00:26:36,566
from customer service and logistics

791
00:26:36,566 --> 00:26:39,166
to healthcare and education

792
00:26:39,166 --> 00:26:40,033
this is exciting

793
00:26:40,033 --> 00:26:42,833
but also a little intimidating to think about AI

794
00:26:43,433 --> 00:26:44,833
having that level of autonomy

795
00:26:44,833 --> 00:26:48,000
it's a lot to process I understand the apprehension

796
00:26:48,100 --> 00:26:50,200
it's crucial that we approach this technology

797
00:26:50,200 --> 00:26:51,033
cautiously

798
00:26:51,033 --> 00:26:53,700
and ensure that we develop and deploy it responsibly

799
00:26:53,700 --> 00:26:56,433
for sure but the potential benefits are immense

800
00:26:56,433 --> 00:26:58,033
they are okay

801
00:26:58,100 --> 00:27:01,166
so we've got orlegs for accessing external knowledge

802
00:27:01,166 --> 00:27:03,600
fine tuning for customizing AI models

803
00:27:03,600 --> 00:27:06,566
and agentic architectures for creating AI agents

804
00:27:06,566 --> 00:27:08,033
they can act autonomously

805
00:27:08,033 --> 00:27:09,666
yeah it's a lot to take in

806
00:27:09,966 --> 00:27:10,900
but before we move on

807
00:27:10,900 --> 00:27:12,400
I wanna circle back to RR for a moment

808
00:27:12,400 --> 00:27:14,133
uh huh you mention that it lets AI

809
00:27:14,133 --> 00:27:16,600
systems tap into external knowledge

810
00:27:17,133 --> 00:27:19,033
but how's that knowledge stored in access

811
00:27:19,033 --> 00:27:20,566
what's the infrastructure behind that

812
00:27:20,566 --> 00:27:21,566
that's a great question

813
00:27:21,566 --> 00:27:23,600
and it's an area that seeing a lot of innovation

814
00:27:23,600 --> 00:27:25,366
right now to make our work

815
00:27:25,366 --> 00:27:27,433
businesses need efficient ways to store and

816
00:27:27,433 --> 00:27:29,100
access relevant knowledge

817
00:27:29,133 --> 00:27:31,600
traditional databases like Postgras and Mungo d

818
00:27:31,600 --> 00:27:33,000
B are still commonly used

819
00:27:33,000 --> 00:27:36,133
yeah they account for 15% and 14% of deployments

820
00:27:36,133 --> 00:27:37,200
respectively yeah

821
00:27:37,200 --> 00:27:40,000
but AI specific solutions are gaining traction

822
00:27:40,000 --> 00:27:40,800
so like

823
00:27:40,800 --> 00:27:43,033
databases that are specifically designed for AI

824
00:27:43,033 --> 00:27:44,633
applications exactly

825
00:27:44,633 --> 00:27:47,333
and one of the leaders in this space is pinecone

826
00:27:47,366 --> 00:27:49,100
an AI native vector database

827
00:27:49,100 --> 00:27:51,666
that's already captured 18% of the market

828
00:27:51,900 --> 00:27:54,933
okay so what makes a vector database so special

829
00:27:55,233 --> 00:27:57,533
how is it different from a traditional database

830
00:27:57,533 --> 00:27:58,433
well think of it this way

831
00:27:58,433 --> 00:28:00,766
a traditional database organizes data into

832
00:28:00,766 --> 00:28:03,166
rows and columns like a spreadsheet right

833
00:28:03,166 --> 00:28:06,733
but a vector database stores data as vectors

834
00:28:06,733 --> 00:28:10,166
which are mathematical representations of data points

835
00:28:10,166 --> 00:28:12,566
okay this makes it much more efficient

836
00:28:12,566 --> 00:28:14,800
for searching and retrieving information

837
00:28:14,800 --> 00:28:16,366
based on similarity so

838
00:28:16,366 --> 00:28:17,400
it's like a database that

839
00:28:17,400 --> 00:28:19,000
understands the meaning of the data

840
00:28:19,000 --> 00:28:21,033
it's storing not just the raw data itself

841
00:28:21,033 --> 00:28:22,466
that's a great way to put it

842
00:28:22,633 --> 00:28:25,166
and it's crucial for RJ because it allows AI

843
00:28:25,166 --> 00:28:26,200
systems to

844
00:28:26,200 --> 00:28:28,966
quickly and easily find the most relevant information

845
00:28:28,966 --> 00:28:30,100
from huge amounts of data

846
00:28:30,100 --> 00:28:30,900
that's really cool okay

847
00:28:30,900 --> 00:28:32,166
so we got vector databases

848
00:28:32,166 --> 00:28:33,933
for storing and retrieving knowledge

849
00:28:34,433 --> 00:28:36,933
but what about getting that data into the database

850
00:28:36,933 --> 00:28:39,200
in the first place what does that process look like

851
00:28:39,200 --> 00:28:40,966
well that's where data ETL comes in

852
00:28:40,966 --> 00:28:42,300
extra transform load

853
00:28:42,300 --> 00:28:44,900
traditional ETL platforms are still widely used

854
00:28:44,900 --> 00:28:47,100
accounting for 28% of deployments

855
00:28:47,500 --> 00:28:48,933
but specialized tools

856
00:28:48,933 --> 00:28:51,933
designed for the nuances of unstructured data

857
00:28:52,033 --> 00:28:55,233
like pdfs in HTML are becoming more popular

858
00:28:55,233 --> 00:28:57,033
so tools that can actually understand

859
00:28:57,033 --> 00:28:59,233
the context of the data there processing

860
00:28:59,233 --> 00:29:01,300
not just the raw text exactly

861
00:29:01,300 --> 00:29:03,933
and one of the leading companies in this space is

862
00:29:03,933 --> 00:29:07,433
unstructured which has a 16% market share

863
00:29:07,433 --> 00:29:08,533
it seems like the entire

864
00:29:08,533 --> 00:29:10,166
ecosystem is evolving to support

865
00:29:10,166 --> 00:29:13,166
the unique needs of AI applications absolutely

866
00:29:13,166 --> 00:29:14,900
and that's what's so exciting about this field

867
00:29:14,900 --> 00:29:17,133
we're seeing innovation at every level

868
00:29:17,133 --> 00:29:18,766
from the foundation models themselves

869
00:29:18,766 --> 00:29:20,500
to the infrastructure that supports them

870
00:29:20,500 --> 00:29:22,166
and it's all happening so fast

871
00:29:22,166 --> 00:29:24,200
okay I'm definitely feeling the energy here

872
00:29:24,233 --> 00:29:25,400
we've covered a lot of ground today

873
00:29:25,400 --> 00:29:27,733
from the booming enterprise AI market

874
00:29:27,766 --> 00:29:30,833
to the nuts and bolts of the modern AI stack

875
00:29:31,300 --> 00:29:33,533
before we wrap things up I'm curious to hear what Menlo

876
00:29:33,533 --> 00:29:35,133
Ventures is predicting for the future

877
00:29:35,133 --> 00:29:37,766
well they've actually outlined three key predictions

878
00:29:37,766 --> 00:29:39,366
okay and they're all pretty bold

879
00:29:39,366 --> 00:29:42,000
I'm all ears with those predictions on me

880
00:29:42,000 --> 00:29:43,800
okay prediction No. 1

881
00:29:43,800 --> 00:29:47,033
is that agent driven transformation is going to be

882
00:29:47,033 --> 00:29:49,033
the next big wave in AI

883
00:29:49,200 --> 00:29:51,833
so those agentic architectures we talked about earlier

884
00:29:52,000 --> 00:29:54,033
they're gonna be even bigger than we thought

885
00:29:54,033 --> 00:29:55,333
that's what they're predicting

886
00:29:55,333 --> 00:29:56,633
they believe that AI

887
00:29:56,633 --> 00:29:58,900
agents will move beyond simple tasks

888
00:29:58,900 --> 00:30:00,600
and start tackling more complex

889
00:30:00,600 --> 00:30:02,200
multi step processes

890
00:30:02,566 --> 00:30:05,900
imagine AI agents that can manage entire supply chains

891
00:30:05,900 --> 00:30:07,733
optimize marketing campaigns

892
00:30:07,733 --> 00:30:10,500
or even provide personalized financial advice

893
00:30:10,500 --> 00:30:12,366
it sounds like something out of a SCI fi movie

894
00:30:12,366 --> 00:30:13,800
it's pre mind blowing okay

895
00:30:13,800 --> 00:30:16,466
so egetic architectures are prediction number one

896
00:30:16,900 --> 00:30:18,000
what else do they see happening

897
00:30:18,000 --> 00:30:19,166
well prediction No. 2

898
00:30:19,166 --> 00:30:22,200
is that more incumbent companies are gonna fall to AI

899
00:30:22,200 --> 00:30:24,300
native challengers so

900
00:30:24,533 --> 00:30:26,966
we're talking about startups with AI powered solutions

901
00:30:26,966 --> 00:30:28,966
coming in and disrupting established players

902
00:30:28,966 --> 00:30:31,366
yeah like what happened with Cheg and Stack Overflow

903
00:30:31,366 --> 00:30:33,433
exactly those are just the beginning

904
00:30:34,166 --> 00:30:36,800
menloventures believes that other categories are right

905
00:30:36,800 --> 00:30:38,200
for disruption as well

906
00:30:38,200 --> 00:30:39,900
they specifically mention it

907
00:30:39,900 --> 00:30:41,900
outsourcing firms like cognizant

908
00:30:41,900 --> 00:30:44,600
and legacy automation players like UI Path

909
00:30:44,600 --> 00:30:47,500
as being vulnerable to AI native challengers

910
00:30:47,500 --> 00:30:50,300
so companies that haven't fully embraced AI

911
00:30:50,500 --> 00:30:52,433
are at risk of being left behind

912
00:30:52,533 --> 00:30:55,233
it's a case of adapt or die right

913
00:30:55,233 --> 00:30:57,033
that's the reality of the situation

914
00:30:57,033 --> 00:30:58,633
and they believe that over time

915
00:30:58,633 --> 00:31:02,333
even software giants like Sales Force and Autodes

916
00:31:02,366 --> 00:31:05,800
could face serious competition from AI native startups

917
00:31:05,800 --> 00:31:08,900
so no one is safe no one is immune to disruption

918
00:31:08,900 --> 00:31:10,700
okay so that's prediction number two

919
00:31:11,233 --> 00:31:13,633
incumbent companies being disrupted by AI

920
00:31:13,633 --> 00:31:16,033
native startups what's the third prediction

921
00:31:16,033 --> 00:31:18,533
their third prediction is perhaps the most concerning

922
00:31:18,533 --> 00:31:20,200
and it has to do with talent

923
00:31:20,600 --> 00:31:23,566
they're predicting an intensifying AI talent drought

924
00:31:23,566 --> 00:31:25,333
a talent trout what do they mean by that

925
00:31:25,333 --> 00:31:27,433
they're basically saying that the demand for AI

926
00:31:27,433 --> 00:31:29,866
talent is gonna far out strip the supply

927
00:31:30,166 --> 00:31:33,166
as AI systems become more sophisticated and widespread

928
00:31:33,166 --> 00:31:35,566
there's gonna be a huge need for people who can build

929
00:31:35,566 --> 00:31:37,200
deploy and manage these systems

930
00:31:37,200 --> 00:31:39,700
so it's not just about data scientists anymore right

931
00:31:39,700 --> 00:31:41,533
it's gonna require a whole range of skills

932
00:31:41,533 --> 00:31:43,366
from AI engineers and architects

933
00:31:43,366 --> 00:31:45,800
to ethicists and product managers

934
00:31:45,800 --> 00:31:47,766
who understand how to integrate AI

935
00:31:47,766 --> 00:31:49,466
into products and services

936
00:31:49,566 --> 00:31:51,766
companies are gonna be battling it out to

937
00:31:51,766 --> 00:31:54,533
attract and retain top AI talent

938
00:31:54,533 --> 00:31:57,233
absolutely and that's gonna push salaries higher

939
00:31:57,233 --> 00:31:59,633
and create a very competitive job market

940
00:32:00,233 --> 00:32:02,266
Minlove Ventures predicts that we'll see

941
00:32:02,333 --> 00:32:05,600
two to three times salary premiums for AI

942
00:32:05,600 --> 00:32:07,200
skilled enterprise architects

943
00:32:07,200 --> 00:32:09,100
who are already well compensated

944
00:32:09,100 --> 00:32:09,600
so the AI

945
00:32:09,600 --> 00:32:11,566
talent crunch is a serious issue

946
00:32:11,566 --> 00:32:13,133
that businesses need to be aware of

947
00:32:13,133 --> 00:32:15,033
it's not just about the technology itself

948
00:32:15,033 --> 00:32:17,833
it's about having the right people to make it all work

949
00:32:17,833 --> 00:32:18,433
exactly

950
00:32:18,433 --> 00:32:20,366
it's something they need to start planning for now

951
00:32:20,366 --> 00:32:21,833
if they haven't already okay

952
00:32:21,833 --> 00:32:23,600
so we've got three bold predictions

953
00:32:23,600 --> 00:32:25,933
the rise of agent driven transformation

954
00:32:26,333 --> 00:32:29,266
the disruption of incumbents by AI native startups

955
00:32:29,433 --> 00:32:32,100
and an intensifying AI talent drought

956
00:32:32,233 --> 00:32:34,166
it peeds a pretty dramatic picture of the future

957
00:32:34,166 --> 00:32:36,166
it does but it's also an incredibly

958
00:32:36,166 --> 00:32:38,400
exciting time to be involved in AI

959
00:32:38,566 --> 00:32:41,300
we're on the verge of a major technological revolution

960
00:32:41,300 --> 00:32:43,633
and the possibilities are truly limitless

961
00:32:43,633 --> 00:32:44,600
I completely agree

962
00:32:44,600 --> 00:32:46,200
it's mind boggling to think about where

963
00:32:46,200 --> 00:32:48,566
all of this is headed speaking of exciting developments

964
00:32:48,566 --> 00:32:50,200
I'm curious to hear about some of the specific

965
00:32:50,200 --> 00:32:51,133
companies that Menlove

966
00:32:51,133 --> 00:32:52,566
enters is backing in this space

967
00:32:52,566 --> 00:32:54,366
they've highlighted several portfolio

968
00:32:54,366 --> 00:32:55,833
companies that are using AI

969
00:32:55,833 --> 00:32:57,833
to make a real difference in the world

970
00:32:57,933 --> 00:32:59,233
I'd love to hear about some of them

971
00:32:59,233 --> 00:33:01,100
it's always inspiring to see how AI

972
00:33:01,100 --> 00:33:03,966
is being used to solve real world problems

973
00:33:03,966 --> 00:33:06,166
one company that stands out is Vilia co

974
00:33:06,166 --> 00:33:09,366
founded by Nobel Prize winning scientist David Baker

975
00:33:09,366 --> 00:33:13,633
wow they're using AI to revolutionize drug discovery

976
00:33:13,633 --> 00:33:16,266
using AI to develop new drugs

977
00:33:16,433 --> 00:33:17,900
how does that even work well

978
00:33:17,900 --> 00:33:18,833
they're using AI

979
00:33:18,833 --> 00:33:21,433
to design potential new drugs on a computer

980
00:33:21,500 --> 00:33:24,000
okay and to predict their structure and function

981
00:33:24,033 --> 00:33:26,533
this is helping to speed up the development of new

982
00:33:26,533 --> 00:33:28,600
life saving treatments for patients

983
00:33:28,766 --> 00:33:30,733
that's incredible it sounds like AI

984
00:33:30,733 --> 00:33:33,600
is playing a key role in advancing medical research

985
00:33:33,600 --> 00:33:36,200
in bringing new treatments to market faster

986
00:33:36,400 --> 00:33:38,333
are there any other companies using AI

987
00:33:38,333 --> 00:33:39,800
in innovative ways like that

988
00:33:39,800 --> 00:33:41,766
absolutely there's squint

989
00:33:41,766 --> 00:33:43,933
a company that's transforming manufacturing

990
00:33:43,933 --> 00:33:45,600
training with AI oh

991
00:33:45,600 --> 00:33:47,766
they're working with big names like Continental

992
00:33:47,766 --> 00:33:48,933
Michelin and Nestle

993
00:33:48,933 --> 00:33:51,533
to capture the expertise of experienced workers

994
00:33:51,533 --> 00:33:52,933
okay and create immersive

995
00:33:52,933 --> 00:33:53,933
augmented reality

996
00:33:53,933 --> 00:33:56,100
training experiences for new employees

997
00:33:56,100 --> 00:33:57,300
so it's like having an AI

998
00:33:57,300 --> 00:33:58,900
powered mentor right there with you

999
00:33:58,900 --> 00:34:00,733
guiding you through the training process

1000
00:34:00,800 --> 00:34:04,733
exactly these AR experiences provide real time guidance

1001
00:34:04,733 --> 00:34:06,766
and verifications step by step

1002
00:34:06,766 --> 00:34:10,133
uh huh this helps to improve safety or reduce errors

1003
00:34:10,133 --> 00:34:11,933
and ensure consistent quality

1004
00:34:11,933 --> 00:34:12,533
that's amazing

1005
00:34:12,533 --> 00:34:14,600
what other exciting companies are they working with

1006
00:34:14,600 --> 00:34:17,266
another company worth mentioning is Code Signal

1007
00:34:17,366 --> 00:34:18,333
they're using AI

1008
00:34:18,333 --> 00:34:20,933
to help businesses find and hire the best

1009
00:34:20,933 --> 00:34:22,166
engineering talent

1010
00:34:22,166 --> 00:34:24,600
so AI powered recruiting for engineers

1011
00:34:24,600 --> 00:34:25,533
how does that work

1012
00:34:25,533 --> 00:34:27,566
they've developed a platform that uses AI

1013
00:34:27,566 --> 00:34:29,433
to automate coding assessments

1014
00:34:29,433 --> 00:34:32,133
personalized question difficulty based on skill level

1015
00:34:32,133 --> 00:34:33,733
and even detect plagiarism

1016
00:34:33,733 --> 00:34:34,333
that's cool

1017
00:34:34,333 --> 00:34:36,633
this helps businesses ensure they're hiring the most

1018
00:34:36,633 --> 00:34:38,666
skilled and qualified engineers

1019
00:34:38,700 --> 00:34:40,566
it makes sense it's like having an AI

1020
00:34:40,566 --> 00:34:42,033
powered recruiter who can quickly

1021
00:34:42,033 --> 00:34:44,133
inaccurately assess technical skills

1022
00:34:44,133 --> 00:34:44,966
exactly

1023
00:34:44,966 --> 00:34:47,733
and it helps to reduce bias in the hiring process

1024
00:34:47,766 --> 00:34:49,300
making sure that everyone has a fair

1025
00:34:49,300 --> 00:34:50,866
chance to show off their skills

1026
00:34:50,933 --> 00:34:52,033
that's a really important point

1027
00:34:52,033 --> 00:34:53,766
yeah it's great to see AI

1028
00:34:53,766 --> 00:34:57,133
being used to create a more equitable hiring process

1029
00:34:57,200 --> 00:34:59,200
what other companies are they excited about

1030
00:34:59,200 --> 00:35:01,366
while they're also highlighting abnormal security

1031
00:35:01,366 --> 00:35:02,766
a company that's using AI

1032
00:35:02,766 --> 00:35:04,833
to protect businesses from email attacks

1033
00:35:04,833 --> 00:35:08,333
like fishing and business email compromise

1034
00:35:08,333 --> 00:35:10,733
so AI is a cybersecurity watchdog

1035
00:35:10,733 --> 00:35:11,400
exactly

1036
00:35:11,400 --> 00:35:14,300
protecting businesses from those nasty email scams

1037
00:35:14,433 --> 00:35:17,166
Abnormal Security's AI powered platform

1038
00:35:17,166 --> 00:35:19,966
can spot suspicious patterns in email traffic

1039
00:35:19,966 --> 00:35:22,633
and stop attacks before they can do any damage

1040
00:35:22,633 --> 00:35:24,166
and that's so crucial these days

1041
00:35:24,166 --> 00:35:26,833
with cybertext becoming more common and sophisticated

1042
00:35:26,833 --> 00:35:28,366
absolutely and they claim

1043
00:35:28,366 --> 00:35:29,133
their platform

1044
00:35:29,133 --> 00:35:31,966
can prevent up to $4 million in potential losses

1045
00:35:31,966 --> 00:35:33,433
each year for their clients

1046
00:35:33,433 --> 00:35:35,000
that's a significant ROI

1047
00:35:35,000 --> 00:35:36,733
it sounds like a worthwhile investment

1048
00:35:36,733 --> 00:35:39,100
for any business concerned about cybersecurity

1049
00:35:39,100 --> 00:35:41,300
yeah what other companies are on their radar

1050
00:35:41,533 --> 00:35:43,566
one last company I wanna mention is typeface

1051
00:35:43,566 --> 00:35:46,600
which is using AI to help brands create personalized

1052
00:35:46,600 --> 00:35:48,366
on brand content at scale

1053
00:35:48,366 --> 00:35:50,200
so AI is a marketing assistant

1054
00:35:50,333 --> 00:35:51,833
you could say that Typeface's AI

1055
00:35:51,833 --> 00:35:52,766
powered platform

1056
00:35:52,766 --> 00:35:55,333
lets brands generate a wide range of content

1057
00:35:55,333 --> 00:35:57,633
from social media posts in email campaigns

1058
00:35:57,633 --> 00:35:59,566
to product descriptions and blog articles

1059
00:35:59,566 --> 00:36:01,500
that sounds like a dream for any marketing team

1060
00:36:01,500 --> 00:36:03,933
struggling to keep up with the demand for content

1061
00:36:03,933 --> 00:36:06,233
it frees up their time to focus on strategy

1062
00:36:06,233 --> 00:36:08,100
and creativity absolutely

1063
00:36:08,300 --> 00:36:10,966
and they're working with some big names including Nike

1064
00:36:10,966 --> 00:36:13,033
Starbucks and LG Electronics

1065
00:36:13,033 --> 00:36:14,666
they've got some impressive clients

1066
00:36:14,733 --> 00:36:16,400
wow sounds like

1067
00:36:16,400 --> 00:36:19,700
Menlo Ventures has a really diverse portfolio of AI

1068
00:36:19,700 --> 00:36:21,466
powered companies they do

1069
00:36:21,500 --> 00:36:22,000
and

1070
00:36:22,000 --> 00:36:25,066
they're clearly enthusiastic about the potential of AI

1071
00:36:25,300 --> 00:36:28,233
to transform a wide range of industries

1072
00:36:28,233 --> 00:36:28,766
okay so

1073
00:36:28,766 --> 00:36:30,400
we've reached the end of our deep dive

1074
00:36:30,400 --> 00:36:32,033
into a world of generative AI

1075
00:36:32,033 --> 00:36:34,433
in the enterprise been an incredible journey

1076
00:36:34,433 --> 00:36:36,366
and I feel like we've only scratched the surface

1077
00:36:36,366 --> 00:36:39,433
I agree it's a vast and rapidly evolving landscape

1078
00:36:39,500 --> 00:36:41,800
but I think we've captured some of the key trends

1079
00:36:41,800 --> 00:36:44,433
and insights that are shaping the future of AI

1080
00:36:44,433 --> 00:36:46,100
in business absolutely

1081
00:36:46,100 --> 00:36:47,533
and I'm sure our listeners are eager to hear

1082
00:36:47,533 --> 00:36:49,166
your final thoughts on this topic

1083
00:36:49,166 --> 00:36:50,866
what's the biggest takeaway for them

1084
00:36:50,933 --> 00:36:52,800
given the rapid pace of AI

1085
00:36:52,800 --> 00:36:54,633
advancement and the trends we've discussed

1086
00:36:54,633 --> 00:36:57,033
it's crucial for everyone to start thinking about

1087
00:36:57,033 --> 00:36:59,700
how generative AI will impact their industry

1088
00:36:59,700 --> 00:37:01,533
or field in the next few years

1089
00:37:01,533 --> 00:37:02,166
it's a great point

1090
00:37:02,166 --> 00:37:04,166
this isn't just something that's happening in Silicon

1091
00:37:04,166 --> 00:37:06,733
Valley AI is going to affect all of us

1092
00:37:06,733 --> 00:37:08,633
regardless of our profession or industry

1093
00:37:08,633 --> 00:37:09,633
exactly

1094
00:37:09,966 --> 00:37:12,100
the sooner we start thinking about the implications

1095
00:37:12,100 --> 00:37:14,433
the better prepare will be to navigate this exciting

1096
00:37:14,433 --> 00:37:16,366
new world and for our listener

1097
00:37:16,366 --> 00:37:17,100
I'm curious

1098
00:37:17,100 --> 00:37:19,933
what were your biggest takeaways from this report

1099
00:37:20,000 --> 00:37:21,533
what are you most excited about

1100
00:37:21,533 --> 00:37:23,733
or maybe a little apprehensive about

1101
00:37:23,733 --> 00:37:26,600
when it comes to AI's impact on your world

1102
00:37:26,900 --> 00:37:28,200
it's interesting you say that

1103
00:37:28,200 --> 00:37:28,833
because

1104
00:37:28,833 --> 00:37:31,433
what really stood out to me was this whole concept of

1105
00:37:31,433 --> 00:37:33,400
egentic architectures yeah

1106
00:37:33,400 --> 00:37:35,800
it's both fascinating and a bit unnerving

1107
00:37:35,800 --> 00:37:37,666
to think about AI systems

1108
00:37:37,733 --> 00:37:39,600
they can act autonomously

1109
00:37:39,600 --> 00:37:41,633
making decisions and taking actions on their own

1110
00:37:41,633 --> 00:37:43,000
yeah I get that

1111
00:37:43,000 --> 00:37:45,633
it's definitely a big leap forward from the AI

1112
00:37:45,633 --> 00:37:47,300
tools we're used to seeing today

1113
00:37:47,300 --> 00:37:47,733
for sure

1114
00:37:47,733 --> 00:37:49,633
and it raises some really interesting questions about

1115
00:37:49,633 --> 00:37:52,033
the future of work and how we interact with technology

1116
00:37:52,033 --> 00:37:54,466
exactly and it makes me wonder

1117
00:37:54,733 --> 00:37:58,366
how do we even begin to prepare for a world where AI

1118
00:37:58,366 --> 00:38:00,800
is not just assisting us but actually

1119
00:38:00,800 --> 00:38:03,333
acting as our agent in various aspects of our lives

1120
00:38:03,333 --> 00:38:04,566
it's a key question

1121
00:38:04,566 --> 00:38:06,566
and one that we'll need to grapple with as AI

1122
00:38:06,566 --> 00:38:08,000
continues to evolve

1123
00:38:08,366 --> 00:38:10,933
but even beyond agentic architectures right

1124
00:38:10,933 --> 00:38:13,333
there's so much to unpack about how AI

1125
00:38:13,333 --> 00:38:15,500
is changing the landscape of business

1126
00:38:15,500 --> 00:38:18,233
right from revolutionizing customer service

1127
00:38:18,233 --> 00:38:19,900
in streamlining software development

1128
00:38:19,900 --> 00:38:21,200
to transforming healthcare

1129
00:38:21,200 --> 00:38:23,100
and disrupting entire industries

1130
00:38:23,200 --> 00:38:27,100
it's clear that AI is already having a profound impact

1131
00:38:27,333 --> 00:38:28,433
and it's only gonna become more

1132
00:38:28,433 --> 00:38:29,833
pervasive in the years to come

1133
00:38:29,833 --> 00:38:31,566
absolutely the intelligent

1134
00:38:31,566 --> 00:38:34,100
enterprise is no longer a futuristic vision

1135
00:38:34,100 --> 00:38:36,066
it's becoming our reality

1136
00:38:36,133 --> 00:38:37,733
and the companies that embrace AI

1137
00:38:37,733 --> 00:38:38,400
strategically

1138
00:38:38,400 --> 00:38:40,533
are the ones that will thrive in this new world

1139
00:38:40,533 --> 00:38:42,900
so for anyone listening who's feeling overwhelmed

1140
00:38:42,900 --> 00:38:46,300
or maybe a little intimidated by all this talk about AI

1141
00:38:46,533 --> 00:38:48,900
what's your advice where should they start

1142
00:38:49,033 --> 00:38:50,366
I'd say the first step is to

1143
00:38:50,366 --> 00:38:52,533
simply start learning and exploring okay

1144
00:38:52,533 --> 00:38:54,766
there are so many resources available online

1145
00:38:54,766 --> 00:38:58,333
from introductory courses to in depth research reports

1146
00:38:58,566 --> 00:39:00,400
and don't be afraid to experiment with different AI

1147
00:39:00,400 --> 00:39:01,733
tools and applications yeah

1148
00:39:01,733 --> 00:39:04,133
dive in the more you understand about AI

1149
00:39:04,133 --> 00:39:06,600
the better equipped you'll be to harness its power

1150
00:39:06,600 --> 00:39:08,500
and navigate the changes it brings

1151
00:39:08,500 --> 00:39:10,633
that's great advice and remember

1152
00:39:10,633 --> 00:39:13,233
you don't have to become an AI expert overnight

1153
00:39:13,300 --> 00:39:14,800
just take it one step at a time

1154
00:39:14,800 --> 00:39:15,900
and focus on learning

1155
00:39:15,900 --> 00:39:17,400
the things that are most relevant

1156
00:39:17,400 --> 00:39:19,033
to your work and your interests

1157
00:39:19,033 --> 00:39:20,033
exactly

1158
00:39:20,100 --> 00:39:23,000
and don't hesitate to reach out to experts in the field

1159
00:39:23,400 --> 00:39:26,733
attend industry events or join online communities

1160
00:39:26,800 --> 00:39:28,200
there's a whole world of people

1161
00:39:28,200 --> 00:39:29,733
who are passionate about AI

1162
00:39:29,733 --> 00:39:32,500
and eager to share their knowledge and insights

1163
00:39:32,500 --> 00:39:34,433
yeah it's really a collaborative effort

1164
00:39:34,433 --> 00:39:36,900
to understand and shape the future of AI

1165
00:39:36,900 --> 00:39:38,233
we're all in this together

1166
00:39:38,233 --> 00:39:39,900
absolutely and for our listener

1167
00:39:39,900 --> 00:39:41,733
we'd love to hear your thoughts on all of this

1168
00:39:41,733 --> 00:39:43,400
hmm what are you most excited

1169
00:39:43,400 --> 00:39:44,900
or maybe a little apprehensive about

1170
00:39:44,900 --> 00:39:47,700
when it comes to AI hit us up on social media

1171
00:39:47,700 --> 00:39:49,733
we'd love to keep this conversation going

1172
00:39:49,733 --> 00:39:51,566
because this is just the beginning

1173
00:39:51,566 --> 00:39:54,400
it is the future of AI is full of possibilities

1174
00:39:54,400 --> 00:39:55,766
and it's up to all of us

1175
00:39:55,766 --> 00:39:58,300
to explore those possibilities responsibly

1176
00:39:58,300 --> 00:40:01,333
and create a future where AI benefits everyone

1177
00:40:01,333 --> 00:40:03,033
well said and as always

1178
00:40:03,033 --> 00:40:04,800
thanks for taking this deep dive with us

1179
00:40:04,800 --> 00:40:06,766
it's been a pleasure we'll catch you next

1180
00:40:06,766 --> 00:40:09,633
time for another fascinating exploration

1181
00:40:09,633 --> 00:40:11,600
of the trends shaping our world