Ep #95: OpenAI and Moderna, Microsoft Phi-3, Sam Altman & AI Leaders Join Homeland Security AI Board

Summary

In this episode of the Artificial Intelligence Show, hosts Paul Roet and Mike Kaput delve into the transformative power of AI within businesses. They discuss the concept of 'AI emergent' companies, highlighting case studies of firms like Mna and Asana that are integrating AI across their operations. The hosts also touch on the importance of leadership buy-in and a clear vision for AI adoption. Additionally, they explore Microsoft's introduction of smaller language models, which could make AI more accessible for organizations with limited resources. The episode covers the formation of an AI Safety and Security Board by the US government, featuring tech and business leaders, and the potential implications for AI infrastructure. They also discuss the rapid advancements in AI technology, as exemplified by comments from Sam Altman, OpenAI's CEO, on the iterative deployment and continuous improvement of AI models. The show concludes with updates on AI in wearables, the competitive landscape for AI coding assistants, and the strategic embedding of AI across HubSpot's product suite.

Takeaways

• 🚀 Companies aiming to integrate AI effectively require support from top executives and a visionary leader with a clear plan to transform the organization over the next three to five years.
• 📈 AI adoption is seen across enterprises like madna and Asana, where employees are empowered to find AI applications for their specific roles, leading to innovative use cases and increased efficiency.
• 🌟 AI emergent companies are established organizations that quickly adopt and scale AI across all areas, with visionary leadership and a commitment to building a smarter business through AI and machine learning.
• 📊 Madna's case study shows a successful deployment of AI with 750 custom GPTs created within two months of adopting OpenAI's technology, highlighting the potential for AI to accelerate business processes.
• 🔍 Asana's integration of AI into operations demonstrates a product-first approach, with a focus on building AI literacy and hands-on experience for employees to drive a company-wide transformation.
• 🔑 Small Language Models (SLMs) from Microsoft offer many capabilities of larger models but with reduced size and data training requirements, making AI more accessible for organizations with limited resources.
• 🛡️ An AI Safety and Security Board, including tech and business leaders, will advise the US Department of Homeland Security on safely deploying AI within critical national infrastructure.
• 📱 Meta is enhancing its Ray-Ban smart glasses with new styles and more powerful AI, allowing users to interact with AI through voice commands and access real-time information.
• 💡 The launch of a new AI startup, backed by former Google CEO Eric Schmidt, aims to challenge GitHub Copilot with an advanced AI coding assistant, reflecting growing interest in AI for software development.
• 🤖 Elon Musk's AI company, Neuralink, is reportedly close to raising significant funds, which may be used to train advanced generations of its AI, indicating continued investment in AI's potential for robotics and beyond.
• 📲 Apple's discussions with OpenAI and Google suggest that the tech giant is considering integrating third-party AI technologies into its iPhone features, indicating a strategic approach to leveraging various AI capabilities.

Q & A

What is the significance of having a leader with a vision for AI transformation in a company?

A leader with a clear vision for AI transformation is crucial as they can drive the organization to adopt AI across all areas, ensuring that the company innovates faster, excels at personalization, and can withstand competition from AI-native companies.

Why is it important for companies to have support from the top for AI initiatives?

Support from the top ensures that AI initiatives are aligned with the company's strategic goals, have the necessary resources, and can be effectively implemented across different departments, leading to a more unified and successful transformation.

What does the term 'AI emergent companies' refer to?

AI emergent companies are established organizations that quickly adopt and scale AI across all areas of the business. They are led by visionary leaders who invest in AI capabilities to build a smarter business and have expanding AI and machine learning talent pools.

How does the adoption of AI change the dynamics of a company's operations?

AI adoption can lead to more efficient and effective operations by automating repetitive tasks, providing predictive models for revenue growth, and unlocking new creative possibilities. It can also enable personalized marketing, sales, and services, enhancing customer experiences.

What are some of the challenges faced by companies when adopting AI on a large scale?

Challenges include building a workforce that is literate in AI, managing cultural changes, providing the necessary training, and ensuring that AI tools are properly integrated into existing workflows without disrupting business operations.

Why is it essential for companies to train their employees on AI tools?

Training employees on AI tools is essential to ensure that they can effectively use these tools to enhance their work. Without proper training, the AI tools may not deliver the expected impact, and the company's investment in AI may not yield the desired results.

What is the role of small language models (SLMs) in making AI more accessible to organizations?

Small language models offer many capabilities of larger models but require less computational power and can run on smaller datasets. This makes them more accessible to organizations with limited resources and can be fine-tuned more easily for specific tasks.

How can small language models potentially streamline AI adoption for organizations?

Small language models can perform well on simpler tasks, reducing the need for costly and complex large models. They can be more easily integrated into existing systems and can run locally on devices, making AI adoption more feasible for a wider range of organizations.

What are the potential benefits of having AI models that can run on devices without an internet connection?

The ability to run AI models on devices without an internet connection can lead to more reliable and faster performance, as there is no dependency on cloud connectivity. It can also enhance privacy and security by keeping data local.

Why is it important for AI models to be fine-tuned for specific scenarios?

Fine-tuning AI models for specific scenarios allows them to perform better and more reliably by using high-quality, handpicked data sources. This customization can lead to improved accuracy and effectiveness in tackling particular tasks or problems.

What is the significance of the AI Safety and Security Board being formed by the US government?

The AI Safety and Security Board will advise the Department of Homeland Security on the safe deployment of AI within critical national infrastructure. This includes making recommendations on protecting systems against AI threats and ensuring the security and reliability of infrastructure.

Outlines

00:00

🚀 Transformative AI Leadership and Organizational Support

The first paragraph emphasizes the importance of leadership and organizational support for the successful integration of AI. It discusses the need for a visionary leader with a clear plan to transform the company over the next five years, highlighting a three-to-five-year window of opportunity for industries to adapt to AI. The discussion is set within the context of the 'Artificial Intelligence Show' podcast, hosted by Paul Roet and Mike Kaput, who delve into AI news, its implications for businesses, and the importance of AI literacy.

05:02

🌟 AI Emergence in Business: Case Studies and Strategies

The second paragraph explores the concept of AI Emergence, where established companies reinvent their businesses with AI. It details case studies from MNA and Asana, showcasing how these companies have integrated AI across various business functions, empowering employees to innovate and build AI tools for their roles. The paragraph underscores the commitment to AI, the importance of identifying high-value AI use cases, and the role of leadership in fostering a culture of AI adoption and continuous improvement.

10:02

📈 MADNA's AI Integration and the Impact of Generative AI

The third paragraph focuses on MADNA's aggressive adoption of generative AI, aiming for 100% proficiency within six months. It outlines the company's strategic approach, including change management initiatives, training programs, and the engagement of the CEO and executive committees. The case study illustrates MADNA's belief in the transformative power of AI, with the potential to perform tasks that would otherwise require a significantly larger workforce.

15:03

🤖 AI Transformation vs. Tool Use: Emphasizing the Learning Curve

The fourth paragraph discusses the necessity of viewing AI as a transformative tool rather than just another tool to be used. It stresses the importance of investing time in learning and experimenting with AI tools to achieve desired outcomes. The paragraph also mentions the different approaches taken by MADNA and Asana, highlighting the importance of top-down support and a company-wide commitment to AI integration.

20:04

📱 Small Language Models (SLMs): Microsoft's AI Innovation

The fifth paragraph introduces Microsoft's new family of Small Language Models (SLMs), which offer similar capabilities to larger models but with reduced size and data training requirements. It discusses the potential of SLMs to make AI more accessible and cost-effective for organizations, allowing them to run locally on devices like smartphones. The paragraph also touches on the future of AI adoption, suggesting a shift towards a portfolio of models tailored to specific needs.

25:05

🏛️ AI Safety and Security Board: US Government Initiative

The sixth paragraph covers the formation of an AI Safety and Security Board by the US government, which includes CEOs from major tech companies. The board's purpose is to advise on the safe deployment of AI within critical national infrastructure. The paragraph also notes the absence of certain tech leaders and discusses the importance of having a diverse group of experts to address the complexities of integrating AI into infrastructure.

30:07

🛠️ AI Infrastructure Challenges: TSMC's Arizona Plant

The seventh paragraph delves into the challenges faced by TSMC, a leading AI chip manufacturer, in building and staffing a chip fabrication plant in Arizona. It highlights cultural and operational obstacles, emphasizing the difficulties of replicating TSMC's success in other regions.

Keywords

💡AI Emergent Companies

AI Emergent Companies refers to established organizations that are quickly adopting and scaling AI across all areas of their business. These companies are characterized by visionary leadership that recognizes the rapid advancements in AI capabilities and invests necessary resources to build a smarter business. In the script, companies like madna and Asana are highlighted as examples of AI Emergent Companies, transforming their operations and empowering employees to innovate with AI.

💡Generative AI

Generative AI refers to a category of AI technologies that can create new content, such as text, images, or even code, that is not simply a modification of existing content. The script discusses how companies like madna have achieved significant milestones with generative AI, with 750 custom GPTs created within two months of adoption.

💡AI Adoption

AI Adoption describes the process by which organizations integrate AI technologies into their operations. The script emphasizes the importance of full organizational commitment to AI, with leadership support and a clear vision for transformation, as seen in companies that are successfully adopting AI.

💡AI Literacy

AI Literacy is the knowledge and understanding of AI technologies, their capabilities, and their applications. The script highlights the need for building AI literacy within organizations as a key step in AI adoption, with examples of internal AI communities and workshops that encourage employees to engage with AI technologies.

💡Small Language Models (SLMs)

Small Language Models (SLMs) are AI models that, despite being smaller in size and trained on less data compared to large language models (LLMs), offer many of the same capabilities. The script discusses how SLMs can make AI more accessible for organizations with limited resources and can be fine-tuned more easily, potentially running on devices like smartphones.

💡AI Safety and Security Board

The AI Safety and Security Board is a new advisory group that includes tech and business leaders, formed to guide the US Department of Homeland Security on the safe deployment of AI within critical national infrastructure. The script mentions the formation of this board and discusses the notable absence of certain tech figures, emphasizing the importance of diverse expertise in AI safety.

💡AI Native Companies

AI Native Companies are startups that are building their products and services with AI at the core from the outset. The script contrasts AI Native Companies with AI Emergent Companies, noting that the latter are established organizations that are integrating AI into their existing business models.

💡AI Transformation

AI Transformation refers to the strategic redesign of business processes and functions to leverage AI capabilities, aiming to achieve significant improvements in efficiency, innovation, and competitiveness. The script uses the term to describe the comprehensive approach taken by companies like madna and Asana to integrate AI across their operations.

💡AI Policies and Principles

AI Policies and Principles are the guidelines and ethical standards that organizations establish to govern the use of AI technologies. The script discusses the importance of having generative AI policies and responsible AI principles as part of a company's approach to AI adoption.

💡AI and Infrastructure

AI and Infrastructure relates to the physical and digital frameworks that support the deployment and operation of AI technologies. The script touches on the challenges of building AI infrastructure, such as chip fabrication plants, and the cultural and technical hurdles that must be overcome.

💡AI in Wearables

AI in Wearables refers to the integration of AI capabilities into wearable devices, such as smart glasses or watches, to provide users with real-time information and assistance. The script mentions Meta's Ray-Ban smart glasses, which now include AI-powered features like real-time information access and voice commands.

Highlights

The importance of having support from leadership for AI initiatives and a visionary leader to transform the organization over the next five years.

The concept of 'AI emergent' companies, which are established organizations that quickly adopt and scale AI across all areas of the business.

MDNA's partnership with Open AI to deploy Chat GPT Enterprise, resulting in 750 custom GPTs created within two months of adoption.

Asana's integration of AI into every aspect of their operations, empowering employees to find their own use cases and build tools for specific roles and needs.

The necessity for organizations to view AI adoption as a transformation, not just tool use, and to invest time and resources into training and infrastructure.

Microsoft's announcement of the F3, a family of small language models (SLMs) that offer capabilities similar to large models but with reduced compute requirements.

The potential for small language models to increase AI accessibility and adoption for organizations with limited resources.

The formation of an AI Safety and Security Board by the US government, which will advise on the safe deployment of AI within critical national infrastructure.

The absence of notable figures like Elon Musk and Mark Zuckerberg from the AI Safety and Security Board, indicating a focus on infrastructure and less on AI expertise.

Sam Altman's comments on the iterative deployment of AI models, emphasizing that current models like GPT-4 are the least capable AI we will ever use.

Challenges faced by TSMC in building and staffing a chip fabrication plant in Arizona, highlighting cultural and technical obstacles in AI infrastructure development.

Meta's expansion of its Ray-Ban smart glasses collection with new styles and more powerful AI, allowing for real-time information access and multimodal AI updates.

The emergence of a new AI startup, Augment, backed by former Google CEO Eric Schmidt, aiming to challenge GitHub Copilot with an advanced AI coding assistant.

Elon Musk's AI company, Neuralink, reportedly close to raising $6 billion for the development of its AI assistant, Grok, with a focus on training powerful models.

Apple's discussions with OpenAI about using its AI to power new iPhone features, indicating potential integration of advanced AI capabilities into iOS 18.

HubSpot's unveiling of new AI features across its hub products, including video generation from text, AI-powered reply systems, and predictive AI for sales forecasting.

Transcripts

you can be building your ad counselors

you can be trying to lead within your

department but the companies that

probably win here it's going to come

from the cite you're going to have

support from on high and you're going to

have a a leader who has a vision to

transform the organization over the next

five years because I think that's

probably the the window of opportunity

for most Industries is you got three to

five years to figure this out

basically welcome to the artificial

intelligence show the podcast that helps

your business grow smarter by making AI

approachable and actionable my name is

Paul rer I'm the founder and CEO of

marketing AI Institute and I'm your host

each week I'm joined by my co-host and

marketing AI Institute Chief content

officer Mike kaput as we break down all

the AI news that matters and give you

insights and perspectives that you can

use to advance your company and your

career join us as we accelerate AI

Literacy for all

[Music]

welcome to episode 95 of the artificial

intelligence show I'm your host Paul rer

along with my co-host Mike put we are

coming to you early this week it is 7:20

a.m. eastern time on Monday April 29th

in our world so uh yeah always the

disclaimer of anything crazy happens on

Monday that doesn't make the show that

is why um so you know it it's

interesting week like it didn't seem

like a crazy news week not a lot of

breaking stuff no make you know major

new tech emerging that we saw know major

funding announcements and yet there's

some really fascinating topics to cover

that I think continue to sort of set the

stage about where we are and where we're

going both in terms of like regulations

some evolving models and um you know the

first big thing we'll talk about today

Mike is the you know a couple of

examples of these AI emergent companies

and I think this is the kind of stuff

like I've been anxious to be able to get

to on the podcast like the real

applications of comp companies that are

actually doing this the right way like

full-blown adoption across the

Enterprise so uh again like no major

breaking news yet something happens

while we're talking here but uh I think

some really valuable stuff for people

today um that can help you along your

your path so today's episode is brought

to us again by rosa. rosa. is the

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maaii um and again I I've mentioned this

on one of the previous read throughs on

rosa. but like Mike and I use it as an

internal newsletter so um we don't run

our Institute newsletter through it but

we actually find it helpful just for

internal research purposes because it'll

send us you know links to look at and

things like that so it's one of the

sources that we use to actually Cate The

Weekly News so um just another potential

use case you can think about for smart

newsletters like that um and then the

second is uh we've been mentioning this

our 2024 state of marketing AI survey is

in the field now you can be a part of

that research this is the what did we

said Mike fourth year this is actually

year four I think I misspoke on the last

episode yeah okay so year four of This

research so we've got fascinating

research going back uh four years now

where we take a deep dive into what is

actually going on in uh artificial

intelligence within the marketing

industry look at use cases uh you know

how people are thinking about it

obstacles for adoption within Enterprise

we're asking questions this year around

um do you have generative AI policies do

you have responsible AI principles like

really trying to get deeply into the the

understanding of where the market is

right now with AI adoption so we'd be

appreciative if you have a few minutes

and can be a part of that survey it is

State ofmarketing

a.com uh you can go there you can

download the 20123 report and click the

link at the top that says 2024 survey

and be a part of that research we will

be releasing that in summer of 2024 so

coming up in a few months so again state

ofmarketing ai.com to be a part of that

research all right Mike it's all you all

right Paul so you alluded to our first

big topic today which is we're seeing a

couple new case studies come out that

detail companies becoming what we would

call AI emergent or these are existing

firms that are Reinventing their

businesses with AI the first case study

details a partnership between Mna and

open AI to deploy chat GPT Enterprise to

thousands of employees across that

company according to open AI quote now

every function is empowered with AI

creating novel use cases and gpts that

accelerate and expand the impact of

every team at the company they also say

that within 2 months of chat GPT

Enterprise adoption madna had

750 custom gpts they had created across

the company 40% of weekly active users

were creating gpts and each user had on

average 120 chat GPT conversations per

week now within madna this is happening

across literally hundreds of possible

use cases across the business they've

got a GPT to review clinical data using

some of the data analysis capabilities

that chat GPT has they have gpts that

their legal team uses to summarize

contracts and they have gpts that help

employees get quick answers about

internal policies at the company now as

we heard this we also got an article

that contained a detailed breakdown from

assana co-founder and CEO Dustin Dustin

moscovitz how the about how the company

evolved to integrate AI into every

aspect of their operations that included

building Bots for feedback for reviews

for sales and customer experience and

for Content creation so it sounds like

Asana also took this kind of similar

approach to madna despite them being

very different types of companies in the

sense that they empowered employees to

go find their own use cases and build

their own tools for their specific roles

and needs moscovitz even details how

everyone from sdrs to product marketers

to HR professionals were able to

actually identify high value AI use

cases in the company and build AI tools

themselves to support their work and he

noted multiple times they often didn't

really have significant technical

backgrounds so Paul this caught our

attention for a few reasons but first up

can you kind of maybe talk a little bit

more about what we mean when we say AI

emerging companies and why it's so

important to be looking at these types

of case studies yeah I was so excited to

see this case study I mean I get like

there it's a case study from open AI

it's going to be obviously very

favorable certainly you know adoption in

Enterprises isn't this seamless but you

know the whole point here is just to see

the depth of commitment that was made to

to to integrate Ai and I think it's a

great representation of what

organizations should be thinking about

and pursuing so going back to the AI

emerging concept so I wrote a blog post

we'll link to it in the notes and I had

to go back and look when I wrote this it

was May 16th

20122 so set the stage to may may 2022

mid Journey was a couple months old we

had Dolly had been previewed I don't

think it was readily available yet um

Mike and I were finishing the manuscript

for our book so our book came out in

summer of 22 the artificial marketing

artificial intelligence book um and we

were uh what about seven months prior to

chat gbt being introduced so that's kind

of the stage we were at but we were

already seeing at that point the

inflection point was arriving in our

opinion as to like we really were going

to have this future whereas I wrote you

were going to be AI or obsolete um and

so I'll just like pull a few excerpts

from that post post because I think it's

very relevant and what we're now seeing

is kind of these companies we envisioned

are now coming to life so in that post I

wrote with each day that passes and each

advancement in artificial intelligence

language and vision technology is

becoming more apparent that there will

be three types of businesses in every

industry AI native AI emergent and

obsolete um I keep running through

examples in my mind I said retailers

e-commerce shops marketing agencies

media companies law firms medical

practices keep going and I can't can't

come up with an industry or business

model where this won't be true I went on

to write take any of these or your own

business and simply look for

inefficiencies and repetitive processes

opportunities to drive Revenue through

greater predictive models such as uh

customer acquisition retention and

growth and ways to unlock ideation and

Innovation through previously

unattainable creative possibilities you

could later add degenerative

capabilities to that as those became

readily available to people and then in

that post we went on to describe AI

emergent company as established

organizations that move quickly to adopt

and scale AI across all areas of the

organization they have Visionary leaders

who see the rapid advancements in AI

capabilities and invest the resources

needed to build a smarter business these

emergent companies have expanding Ai and

machine learning Talent pools um and at

the time I referenced Adobe for example

had 300 AI ml employees at that time

according to LinkedIn they innovate

faster than the competition potentially

through Venture Studios or R&D Labs on

building aite Tech and they excel at

personalization across Marketing sales

and service they have the data customer

bases and infrastructures to withstand

the AI native companies so the startups

that are building AI first if they move

fast enough to transform plus they have

the money to acquire the AI native

companies before they grow to dominate

so that just sort of sets the stage of

like going back to 2022 how we were

looking at uh sort of the future of

business then you fast forward to today

with this madna example and the thing I

love about I mean I don't know who I

don't know if gp4 wrote the the case

study but it's a really well done case

study Mike and I used to write case

studies and things like this for clients

back in our you know agency days this is

really well done because what I loved is

they LED with the why so it said madna

is using its platform for developing

mRNA medicines to bring up to 15 new

products to Market in the next 5 years

in order to achieve its Ambitions madna

has ad oped a people Centric technology

forward approach constantly testing new

technology and Innovation uh that can

increase human capacity in clinical

trials so they they basically kind of

lay out the fact that they're having

this um unparalleled growth opportunity

and at one point they actually literally

say that this would take uh thousands of

people to do if they weren't using AI so

the the CEO as quot says we believe very

profoundly at madna the Chad GPT and

what open ey is doing is going to change

the world we're looking at every

business process and this part I love

from legal to research to manufacturing

to Commercial and thinking about how to

redesign them all so this is like again

go back to thei merging definition it's

a it's a Visionary leader like you need

someone who's looking at his head saying

this isn't just like a marketing thing

this isn't every function of the

organization thing and we're going to

like aggressively push this then it went

on to say that their um their objective

was to achieve 100% of adoption and

proficiency of generative AI by all its

people with access to digital Solutions

within 6 months quote we believe in

collective intelligence when it comes to

Paradigm changes it's everyone together

everyone with a voice and no one left

behind then they said they had

individual change man management

initiatives including in-depth research

and listening programs as well as

trainings hosted iners online and with

dedicated AI learning companions um they

had prompt writing competitions to find

the top 100 AI power users they had

local uh office hours with every

business line in geography with over

2,000 actly week participants um

structured change management initiatives

including engaging madna CEO and

executive committees and then there was

a quote in here from their Chief

Information officer Brad Miller um said

90% of companies want to do gen but only

10% of them are successful and the

reason they fail is because they haven't

built the mechanisms of actually

transforming the workforce to adopt new

technology and new capabilities this is

exactly what we've been saying Mike like

that like people complain that it

doesn't have the impact it should have

and the models aren't smart enough or

they make mistakes and the thing we

always say is you're not thinking about

this the right way you're not properly

training everyone like you can't just

buy the tool give it to everyone and a

think it's just going to happen so they

went on to say you cited a couple of

those metrics but um they said within

two months they had 750 gpts 40% of

weekly active users had created a GPT

and each user had 120 chat gbt

Enterprise conversations per week on

average um and then there was a section

the the final section I want to call out

is this idea that said a team of a few

thousand can perform like a team of a

100,000 and this is where the quote came

from said if we um with an ambitious

plan to launch multiple products in the

next few years madna AI as a key

component to their success quote if we

had to do it the old biopharmaceutical

ways we might need a 100,000 people

today we really believe we can maximize

our impact on patients with a few

thousand people using technology and AI

to scale the company so I did you I

don't know if anything else caught your

attention mik but this is like again the

thing I keep saying and keep thinking

when we talk to these big Enterprises is

like you you this goes everywhere it's

every function of the business and you

have to be educating people first and

foremost and then you have to give them

like the resources to actually explore

what's possible with this technology

then encourage all the sharing so I just

I love everything about what they

feature in this case study yeah like you

like you mentioned obviously it's never

as seamless as it appears just operating

on a single case study but I think

people would be wise to look at just

look at the numbers again look at how

many conversations your average employee

is having if you are

coming away dissatisfied from these

tools because you had three or four

conversations and it didn't cut it

they're having 120 per week it's going

to take hundreds of conversations to

even begin to get towards what you want

is that kind of what you took away from

this in terms of not only the education

piece but people do need to actually

invest a large amount of time and trial

and error to get these tools to work

well yeah it's viewing it as

transformation not just tool use you I

think that's the companies that go and

saying we believe this is going to

transform our our Workforce our

strategies our our um you know our

technology stack and we're going to

approach it as such we're going to put

the right resources we're going to build

the right infrastructure put the right

governance in place like not just go get

five tools to be testing and and hope

people figure out how to get some value

out of the $30 a month license that

they're using so yeah I think it's just

the organizations that take that

approach and then you mentioned assana

it is a different approach because

they're coming at it most likely from a

product first standpoint a is a project

management system platform if you're not

familiar um we're big fans we've been

using it since I don't know we started

using her for uh the agency so probably

a decade or so been using Asana um we

use it to run the Institute from a

project management perspective um so

Dustin mosovich who you mentioned if

people aren't familiar he was actually

one of the co-founders of Facebook and

then he left and found at ass sa in 2018

he you may see his news his name in the

news otherwise because he's currently

has beef with Elon Musk

um I will I will not disclose like go

check elon's Twitter thread from Friday

I was messaging Mike when I was sitting

at dinner I was like what what happened

what did I miss I was in a talk at Ohio

University Friday afternoon and Elon

Musk put this really inappropriate tweet

up about Dustin and I was like what what

happened and Mike's like man I got no

idea so I found out what happened um

there's been a lot of beef emerging

between the two but basically he he he

said uh Tesla is the next Enron that

that it's a the whole thing is a big

scam and Elon is basically just like

selling this Future Vision of full self-

driving that's never going to happen and

so he basically just called Elon out and

said it was Enron so Elon was not very

happy with that so Dustin and news for

other reasons you may hear about um but

back to assana so the way they're

approaching it similar thing you have a

CEO with a vision for how this is going

to transform the company so they're

looking at it from a product perspect

perspective but the thing I liked about

this example is it was a letter from

Dustin so it was very clear that this is

coming from the top um and they're a

company that has been investing in AI

for a long time like we've talked about

this before AI isn't new like Chad GPT

wasn't the beginning of AI there were

companies like Assa like Salesforce like

Adobe that had hundreds of AI and ml

Engineers way before chbt emerged but

they were working on machine learning

they were working on predictive models

they were making sure predictions about

outcomes and behaviors that could be

applied to like forecasting models and

recommendation engines and um

optimization of pricing and like things

like that and products well what they're

doing now is he's saying like Okay we

were already in AI but now we're really

in and like we're really seeing the

potential of gener AI not just within

our product but within our own company

and so there was one point he said

internally reactions to AI range from

exhilaration to skepticism we knew we

needed to build literacy and hands-on

experience to unite every around this

transformation again transformation not

just tools so we launched an internal AI

community and immersive workshops

encouraging all employees to Tinker with

the technology we stood up stack slack

channels we started evangelizing the

power of AI through new internal use

cases and stories of our personal

breakthroughs that company all hands and

in team meetings and then it just goes

on to say kind of how they got everyone

involved and they began to glimpse of

future where AI moves Beyond isolated

chats to embedded contextual

collaboration kind of their own use

within this

so definitely another one to just keep

an eye on they do say they have a work

Innovation Summit on June 5th that we'll

track um where they said quote we be

showcasing in the future of human AI

teamwork and then he ends with together

let's build a future where every team

has the power of AI at their fingertips

where human creativity and machine

intelligence combined to solve The

World's Greatest challenges and where

work is not just more efficient but more

fulfilling more impactful and more

profoundly human that is like something

I would have like that is exactly what

we've been saying all these years more

intelligent more human is the tagline

we've been using um so I I love the

vision I love what they're trying to do

um we're not using any of the AI

features with venisa to my knowledge

today so you know I think we need to

probably dive back in and see if there's

anything going on um yeah but certainly

you know two good examples today of just

this whole vision of you got to have

it's got to come from the top like you

can be building your ad counselors you

can be trying to lead within your

department

but the companies that probably win here

it's going to come from the seite you're

going to have support from on high and

you're going to have a a leader who has

a vision to transform the organization

over the next five years because I think

that's probably the the window of

opportunity for most Industries is you

got three to five years to figure this

out

basically so in our next big topic today

Microsoft has announced the F3

ph-3 family of open models and these are

noteworthy because they're actually

small language models or slms according

to Microsoft slm's quote offer many of

the same capabilities found in large

language models but are smaller in size

and are trained on smaller amounts of

data now because these models are much

smaller than llms they consume far less

compute and they can run locally or have

the potential to on devices like an

iPhone so one of the main models in the

family

5-3 mini has 3.8 billion parameters it

was trained on 3.3 trillion tokens and

Microsoft says it performs better than

models twice in size there are some

other 53 models coming soon uh with more

parameters 7 billion and 14 billion have

been mentioned by Microsoft and while

large language models are basically

unmatched right now for complex tasks

and the most advanced use cases the

reason this is kind of a big deal is

that these small language models can

perform really well on a lot of simpler

things that can make AI more accessible

and easier to use for organizations with

limited resources small language models

can also be more easily

fine-tuned uh senali yav who Microsoft's

principal product manager for generative

AI said the following about this trend

quote what we're going to start to see

is not a shift from large to small but a

shift from a singular category of models

to a portfolio of models where customers

get the ability to make a decision on

what is the best model for their

scenario so Paul I found this trend

really interesting in terms of what it

could mean for AI adoption like how do

you see small language models

potentially increasing or streamlining

how organizations actually adopt

AI for just some macr level context here

again the reason why these models are

important is because it costs a lot of

money to run the big models so you know

we're building GPT 4 GPT 5 or Gemini 1.5

or you know Claude 3 Opus like the

biggest Frontier models to use those

models so training them costs hundreds

of millions or billions of dollars

that's one thing but then the inference

cost so when you and I go to use the

tool um for whatever it is to write our

email or to generate our images or

eventually to generate our you know

10-second videos that's the inference

cost so if you're using these massive

models every time you need to do a

discrete task it can get insanely

expensive so for Microsoft who's

licensing open ai's technology to do say

365 co-pilot it's built on open ai's gbt

4 4.5 whatever it is technology it costs

them a bunch of money to pay open AI to

to use it every time you or I go in and

use co-pilot so there's a lot of

motivation to build these smaller models

that don't cost as much money and what

the research seems to be showing

recently is as you alluded to you can

train these things a little easier

there's been a number of research

reports just in the last few weeks that

shows that the quality of data matters a

lot and that if you can take a smaller

model that costs less to train and costs

less to run and you can give it more

kind of handpicked data sources that

these things can perform like a much

larger model in terms of their quality

and re and reliability if the data is

really good that goes into them um Apple

actually has released a number of

research papers showing this is the

direction they're going where you build

these smaller models that don't cost as

much to run don't cost as much to train

and they can run on device so you don't

have to go to the cloud every time you

want to do something so again every time

you or I go to chat gbt or perplexity or

whatever we're going and pulling from

compute in the cloud somewhere which

costs money what they're saying is in

the future you're you could be on your

device on do not disturb or or airplane

mode and you could be running a model

doing something on your phone that's

what this means and so that's why you're

going to hear a lot more about this as

we lead into the June developer

conference for Apple um this on device

models that can do things for you on

your phone without an internet

connection is going to be a really big

unlock and I think it will lead to a lot

more adoption and it's probably going to

be much more like the adoption we had in

the 201 to 2020 range where we were all

using AI in Netflix and Spotify and

YouTube and Facebook like AI was part of

our lives and we didn't realize it

that's what this will enable is like

that these models can just be underlying

functions on your phone and you don't

even know you're using AI it's not like

you're going to a AI app to use AI it's

just going to be embedded within

everything

so in our third big topic today we got

news that Sam Alman Microsoft CEO Sachi

Adella and alphabet CEO synar pachai are

joining something called an AI Safety

and Security Board that's being run by

the US government now this board is

going to advise the Department of

Homeland Security on how it can safely

deploy AI within critical National

infrastructure so it's going to do

things like make recommendations on how

operators of core infrastructure like

power grids for instance can protect

their systems against AI threats those

kinds of topics and advisory

recommendations now almost two dozen

political Tech and Business Leaders are

on this board so some of the other

notable names uh include nvidia's Jensen

hang anthropic CEO Dario amade the CEO

of Northrup Grumman the mayor of Seattle

who's also the chair of the us

Conference of Mayors Tech and Innovation

committee the governor of Maryland and

noted AI researcher fay F Lee now Paul

when we talked about this before the

episode you mentioned that some there

were some notable omissions from this

group that stood out to you who who was

that yeah so I mean the obvious thing is

meta is not on there so no Zuckerberg no

Yan laon uh I you could say Elon Musk um

who's certainly in investing a lot to

build AI so there was some obvious ones

there and then the biggest one that

initially jumped out to me is what were

the open source people like so a lot of

the the people on here from the AI

perspective are are you know not the big

ones pushing for open- Source

acceleration um so that seemed to be U

one thing there was I did see a tweet

from Gavin Baker who you know I follow

pretty closely uh he's a managing

partner and CIO at an investment firm um

but heavily involved in AI and he

tweeted can't decide whether it is funny

ridiculous or sad that the CEOs of

ocidental petroleum and Delta Airlines

are on the new AI safety board less than

half of the 22 members have any real AI

knowledge um also odd that it's not even

vaguely bipartisan has zero open- Source

AI CEOs and excludes Elon um so that was

my initial like that was actually one of

the first times I saw this list and then

when I went back in and read about it it

actually made a lot more sense like what

his critique I think didn't necessarily

hold up at least from the business

perspective because this is all about

critical infrastructure such as the

power grid and transportation so it

makes perfect sense that someone from a

petroleum company someone from an

airline company like you would want

diversity they don't have to be AI

experts to be able to explain how

transportation in the in the United

States works or how the the energy grid

Works things like that so it's actually

smart to have a diverse group of people

that caning bring that kind of knowledge

to the table that doesn't mean that

there shouldn't be open source people on

there someone like a you know a

Zuckerberg shouldn't be on there but I

think they're basically probably looking

at which companies are we working with

on the infrastructure of the the US

economy and the government and then what

businesses are represented that need to

be there so yeah I mean My overall take

was um I I think it's good that these

conversations are happening the

infrastructure is something we don't

talk about on this show much we probably

will more it is a major problem um there

are lots of things that can go wrong

with the infrastructure and in many

cases the infrastructure is 70 years old

or more and that's a problem and it's an

attack Vector for people to use um so

yeah and then the other thing we'll come

back to this later on but there was a uh

there's a new bill um

sb147 in California safe and secure

Innovation for Frontier intelligence

models act that is uh apparently moving

pretty quickly in California and this

seems to be a bit of an attack on the

open- source world as well at least it's

being positioned by some open source

Advocates as such um Jeremy Howard who

is someone to follow on Twitter um is uh

posted an article about like his

thoughts on this so he's an AI

researcher and entrepreneur CEO of

answer. um and so he goes on as to kind

of like do a Tak down of why open source

is so important so I think again we'll

kind of come back to this maybe next

week I think it's worth talking a little

bit more about this battle between open

source and closed source as we start

moving into regulations um but it's

going to be a really important uh topic

as as we move forward especially as we

start getting into the elections and as

the government starts looking at ways to

um you know protect infrastructure and

things like that so yeah really critical

topics uh keep an eye on SP 1047 in

California it seems like it's moving

real

fast all right so let's dive into some

rapid fire topics this week the first up

is a pretty interesting public comment

from Sam Alman he recently made a public

appearance at Stanford's entrepreneurial

thought leaders seminar called ETL uh

and he had some harsh words to say about

chat gbt at one point in the discussion

he said quote chat PT is mildly

embarrassing at best gp4 is the dumbest

model any of you will ever have to use

again by a lot but it's important to

ship early and often and we believe in

iterative deployment now Paul this isn't

some groundbreaking secret knowledge

interview that he's dropping here but it

it was something I wanted to highlight

because I think many people who are

relatively new to AI don't always grasp

just how much leaders like Alman believe

existing AI technology is going to

improve and soon and you also mention

this point in your talks all the time

saying this is the least capable AI

you'll ever use could you kind of unpack

this idea a little more for us yeah you

know I think it's it is just a good

reinforcement that you know people look

at gp4 today and maybe they are

disillusioned maybe there are some

organizations or some leaders out there

who just don't see the real potential

for transformation they're not doing

what madna or assana are doing and

they're not you know really driving

because they just don't see the value so

one I think it's a really good reminder

that it's only going to get better from

here and it's probably going to get

significantly better from here and the

other thing that I thought he gave some

good perspective on is he said this in

many many interviews that it's better

that they don't just go build so they

again their mission is artificial

general intelligence AI that is at or

above human level at all cognitive tasks

generally speaking um and so his feeling

is like we don't we don't want to just

go away and build AGI and then just drop

it on the world in you know 2 years 5

years whatever and then all of a sudden

it's like oh oh my God like where did

this come from like they if we had never

seen chap chbt if we'd never experienced

these tools as they were evolving so

open AI does iterative deployment so

they're a big believer in let's ship

this stuff early ship it often and give

people a chance to react to it now if we

rewind back to I think it was gpt2 when

it first came out they put out a paper

said hey we're not releasing this into

the world because it it might be too

dangerous so they were worried about

gbt2 being misused um now people are

worried about gbt and oh my gosh what's

going to happen to society and so Sam's

whole point is like listen there's this

initial shock when when new things come

out like Chad GPT comes out everyone

freaks out and so then he said GPT 4

which came out a year ago was met with

two weeks of freaking out and people

believed it was this crazy thing and the

world had changed forever now people are

like oh it's horrible where is gbt 5 so

his whole point is like as a species we

adapt change is weird um I've referenced

the one that Andre karpathy said at one

point where he was talking about like

whmo like most people don't realize like

self-driving is actually a thing like

there are taxies basically in California

that don't have drivers and you can

order them on your phone and you can get

in them well the first time you see that

you're just like what is that and then

you continue walking down the street

like you go about your life and so I

think like as much as AI is going to

transform everything like it's going to

be this process where things just start

to be weird and you're going to look

back two years be like oh my God I can't

believe gbd4 was like what we were using

that was such a terrible tool um but

right now it feels very impactful and so

I think that's the the path we're on

with this technology but Sam's Point is

listen we we'll keep figuring it out now

I don't necessarily buy into this when

it comes to like the jobs and the

workforce and the economy conversation

which is a whole another topic um but

this is the approach of most of these

technology leaders is like hey we adapt

we figure things out it'll all be okay

and like we'll have time to solve this

so yeah that was that was the uh there

was a a few quote worthy things from the

interview at Stanford for sure it

certainly sounds like he does not think

progress on AI will be slowing down any

time no he does not yeah and he's been

very clear that the leap to five is

going to be massive uh um but yeah we we

will see hopefully well I don't know

hopefully but we'll probably see sooner

than later

see all right another topic on the

docket this week we got a new in-depth

report from a publication called rest of

world and it details some really

significant challenges that are being

seen building out AI infrastructure in

the US the article does a deep dive into

tsmc the Taiwan semiconductor

manufacturing company this is one of the

top AI chip makers on the planet that

makes AI Hardware basically possible and

it's currently engaged in efforts to

build and staff a chip fabrication plant

in Arizona so this plant has right now

about 2200 employees it's kind of seen

as this leading indicator of efforts to

diversify AI chip manufacturing away

from Taiwan just given the geopolitics

of that region and kind of great power

competition between the US and China but

this appears to be a lot easier said

than done because this report which is

well worth a read um details tons of

obstacles that tsmc is running into

trying to get this plant up and running

and primary among these aren't just you

know technical hurdles there's serious

cultural clashes between American and

Taiwanese ways of working the report

details how Americans had to go train at

tsmc and iwan for like a year but all of

the training were in Taiwanese and

Mandarin Chinese so they were basically

hacking it together with Google

Translate to learn what they were

supposed to learn there were also some

significant clashes with tsmc's work

culture which is quote notoriously

rigorous even by Taiwanese standards and

Taiwanese Engineers were also coming to

the US and then very critical of their

American counterpart work ethic and

technical skills all of this is to say

you know Paul we hear a lot of talk from

AI leaders about the need to invest

billions or even trillions into AI

infrastructure like this in the coming

years especially in the US but it just

sounds like there's some serious human

obstacles here I mean could this type of

thing hold back AI

Innovation yeah and it's totally

predictable like so if this is an

interesting topic you one it probably

should be um just if nothing else you

understand understand the supply chain

that powers your smartphones your cars

all the AI tools that we rely on today

all the ones we rely on in the future

they are all dependent upon tsmc and the

supply chain of building these chips um

also your retirement portfolio

potentially if you invest in Nvidia um

now I did hear an interview with Jensen

Hong where he said that they are relying

on tsmc in Taiwan but of the 35,000

parts that go into each chip only eight

of them are made by tsmc so it's not

like whole chip is fabricated like in in

Taiwan but it is a critical part for

sure so this affects everyone the

article is insane like it's a long read

but it is like it's comical at times but

um also sad because it's just as you

said like I I don't know how you've

solve this like and there are the the

CEO of tsmc has basically said for years

like you can't do this in America like

this isn't going to work but I'll take

your1 billion dollar and we'll try and

try and build this in Arizona for you

and we'll see how it goes and it's

mainly a culture and and labor issue

like they're just very different in

Taiwan than in in America uh and that

becomes extremely apparent when you read

this article that it is probably more um

misaligned than you could imagine to try

and do what they do in Taiwan and in the

US um so two quick recommendations Chip

War by Chris Miller great book on the

topic and then there's an article from

Forbes by Rob TOS called the geopolitics

of AI chips will Define the future of AI

um that article is from May of 2023 and

I believe the book came out in 2023 as

well so again if you're if you're

fascinated by this thread of AI um start

with the article on Forbes from Rob TOS

great insight into it he also had a Ted

Talk on the topic I believe and then if

you want to keep going read chipboard by

Chris Miller uh

it is a fascinating topic it's one of

those like I try and not spend too much

time in because it's like I have no

control of this whatsoever and there's

times where you read this stuff you're

like oh this is going to go awh like

this is not going to work out and I feel

kind of helpless so I like it's kind of

like cyber security like I'll dip in

every once a while read about it like I

got to get out of here like I got too

many other things to worry about than

the supply chain for AI chips but it's a

fascinating

topic all right next up meta has

announced that it's expanding its

Rayband meta smart glasses collection to

include a bunch of new Styles but more

importantly new more powerful AI so we

had talked last week about meta AI the

company's intelligent assistant and now

in the US and Canada you'll be able to

use that right within your smart glasses

so you just say hey meta and you can

then prompt the assistant with voice

commands now meta AI also gives the

glasses the ability to access realtime

information information and the company

began testing a multimodal AI update so

you can actually ask your glasses about

what you're seeing now that update is

now rolling out to users in the US and

Canada so Paul it definitely seems like

meta's Rayband smart glasses could be

like an actually useful way to engage

with AI in the real world we've talked a

couple times in uh the last couple weeks

about AI wearables and that whole Trend

like how do you see these stacking up

compared with some of the other

unfortunately Rocky releases of AI

wearables uh definitely a better form

factor lot more positive Buzz regarding

these than say our our Humane AI pin or

the rabbit which we won't get into but

the rabbit if you recall we talked about

I don't know 10 episodes ago 15 episodes

ago when it was first previewed it's

this device that supposedly does AI on

it at the time I was kind of skeptical

that it was one needed and two would

work it is real bad so far so they just

started getting into the hands of people

and it is it's not I don't know if it's

as bad as the a AI pin reviews but it's

real bad um probably just because it

only cost $99 instead of $799 so people

are more like accepting of the fact that

they just wasted $200 but um the rabbit

device is not going well the rayb bands

however seem like right form factor

obviously meta has endless money to

throw at this and they seem to be being

really smart I tried to find some sales

data on these things and the only thing

I came up with was August

20123 uh at that time said they' sold

300,000 of the devices but only had

27,000 monthly active users so adoption

wasn't real High um but again it's it's

not wildly expensive product so it can

be somewhat disposable if you're a

higher income person it's like you know

throw 300 bucks at something you don't

like it it's okay um I could see these

being more heavily adopted for sure in

in the months and years ahead and I

think more people will get into this

space I would be shocked if Apple

doesn't at some point apply their Vision

Pro technology um to this and by the way

we should come back to the Vision Pro at

some point yeah uh that's not going well

either I mean awesome Tac like at least

they have good Tac but it's not being

supported like there's I have like no

new apps no new immersive experiences

like I don't know what they're doing and

apparently they just cut production like

down to 400,000 units instead of 700,000

so yeah I think there's just going to be

it's going to be a rocky go with these

um kind of immersive experiences and and

AI devices um even for the big companies

but meta seems to be on track here more

than

most so we just also got some news that

a new AI startup being backed by former

Google CEO Eric Schmidt has just emerged

from stealth with a whopping $252

million in funding this company is

called augment and it aims to challenge

GitHub co-pilot by offering a better

version of an AI coding assistant to

help programmers be more productive and

effective so essentially AI assistant

that will generate code for you help you

debug code and help you create programs

much faster now the company was actually

co-founded by an ex Microsoft developer

and a former AI research scientist at

Google they are entering however a

pretty crowded and competitive market

GitHub co-pilot has 1.3 million paying

individual customers and 50,000

Enterprise customers Amazon and Google

have their own coding assistance and

there's a ton of other startups

competing in this space as well now Paul

while we don't have a ton of details

just yet on this company it has raised a

significant amount of money has some

notable investors its Founders have

interesting and relevant backgrounds

these kind of tick all these boxes we

look for when it comes to paying

attention to certain companies like what

are your first impressions of this yeah

so we on a reev recent episode we talked

about you know what startups get our

attention um the investors the amount of

investing the founding team sort of like

check check check and and this one Eric

Schmidt if if you're not familiar he was

the CEO and chairman of Google

from1 so just a couple years after they

were founded he was sort of the adult

brought into the room basically to kind

of guide um Google and so he was the CEO

till 2011 and then he stayed on as the

chairman of the board I think until

200 what 15 or 18 um and so he's you

know a billionaire and uh he invests

heavily he advises the government and I

think the Department of Defense he's

like an adviser for AI there so he's

heavily involved uh still and so to see

him uh leading this kind of round for a

company to come out of Health with a

quar billion dollars that that's going

to get your attention and certainly the

co-pilot is the GitHub co-pilot is one

of the very early seemingly highly

reliable uses of AI like we're seeing it

really impacting the coding world and so

you're going to see some competition

flow into this space so yeah just again

things that kind of perk our ears up is

when you hear a quarter billion in

funding and see Eric Schmid's name tied

to something and and the founders from

Google and Microsoft definitely a up

worth paying attention

to all right you know it wouldn't be AI

news without Elon Musk being in the news

again Elon musk's AI company xai which

makes grock the AI assistant that he has

is reportedly close to raising $6

billion from investors that include

Sequoia Capital this raise would value

the company at $18 billion and according

to the information it's expected to

close in in the next 2 weeks now

according to their reporting the company

is currently training the second

generation of grock on 20,000 Nvidia

h100 chips and musk has also indicated

that the company needs 100,000 gpus to

train grock 3.0 so that's likely where

at least some of this money is going but

really kind of big question I have here

Paul is like you've been I mean we both

have been pretty underwhelmed but with

grock like in our initial test T is this

kind of valuation Justified for this

company or this tool I mean Elon sells

Visions like no gr is useless right now

still again I if someone has a use for

grock like please reach out to me and

tell me how you're using the thing I I I

just don't understand like what what

it's supposed to be doing um but he he

sells Visions like we're going to go to

Mars we're going to Electrify the world

with cars and um you know that that's

his thing and so I'm sure he's just in

some Grand Vision for AGI and and its

embodiment into robots and how it's

going to accelerate you know getting to

Mars and um you know saving the planet

so and he can get money from people

whether it's seoa or um you know other

government uh funds um is certainly a

place he tends to tap he which creates

some of the friction between him and the

US government is elon's very friendly

with other governments that the US

doesn't necess necessarily want him to

be friendly with and because it's a

source of money for him so yeah I don't

know I keep keep watching grock maybe

it'll uh do something at some point I

don't know but yeah I I wouldn't be

shocked if he raised this I wouldn't be

shocked if it was more money than this

like he can raise whatever he wants

probably

yeah all right next up we have gotten

news that Apple has started backup

discussions with open AI about using its

AI to power new iPhone features coming

later this year this comes from some

reporting by Bloomberg these discussions

revolve around the terms of a possible

agreement between the two companies that

would integrate open aai features into

iOS 18 which is the next iPhone

operating system now earlier this year

apple and open aai apparently had been

talking but it looks like those talks

had stalled until now apple is also in

discussions with Google to potentially

license their Gemini models free use in

their products so Paul these are still

discussions and rumors not any actual

deals yet but you're a longtime Apple

Watcher like why is Apple engaged in

trying to use AI from other companies

rather than building their own I think

they're doing all the above I I mean who

hasn't Apple had talks with at this

point or at least reported to have talks

with so I don't know I mean but Apple's

done deals with everybody for the iPhone

at different times so nothing would

shock me if they do a deal with open a

or Microsoft or Google like I mean

they're Frenemies they're sometimes they

do deals together and other times

they're competing against each other so

I'm not surprised by any of it I have no

idea what they're going to do it seems

like it's going to be a mix of their own

models so again if you read the recent

research reports coming out of apple

which they're not historically like one

to put out a bunch of research yeah so

they've been talking a lot about their

their investements internally with AI so

I I think there's just going to be a mix

and it may be that they're not fully

baked yet with their internal models and

so they're going to do deals with other

companies until they feel that their

models are you know ready for prime time

I don't know but they're going to do

something like they're they're going to

make some major announcements in June

and I would expect that by this fall the

way your phone works is probably going

to start to evolve like we're going to

experience AI on device

um as soon as this fall so one way or

the other definitely something to keep

keep an eye

on all right our last story this week is

about HubSpot because HubSpot just

unveiled its most recent Spotlight so

this is a product showcase the company

says it's rolling out twice a year to

kind of highlight new parts of the its

various hubs and other products and AI

played a starring role in this one

because HubSpot says it has started to

embed AI across each one of its hub

products across Marketing sales and

service and this Spotlight showcases

some of the latest AI features and these

include things like a feature called

clip Creator which generates videos from

text prompts an AI powered automatic

reply system in their service Hub the

ability to turn written content into

audio with AI powered blog post naration

the ability to create reports

automatically using generative Ai and

text prompts there's AI powered brand

voice which generates content that

sounds like your company they have now

predictive AI to forecast sales

performance and much much more now Paul

we've obviously got a very long history

with

HubSpot you know this isn't the first

time they've released AI features but

how do you see these updates kind of

fitting into hubspot's overall AI

trajectory they're they're pushing out a

lot of updates

um they they're really logical use cases

it's uh good to see them keep making

Innovation I I saw some chatter just in

like the partner Channel I'm still like

observe I'm not involved in it anymore

but just that like people are struggling

to keep up with the update I think there

was like over a hundred things in in

this Spotlight and the way they released

it is like this this page that's just

like this infinite scroll page of

updates I don't know if there's like a

way to download this like I was having

trouble honestly like kind of even

figuring out what was going on and what

what all the updates were and what we

have and don't have as a company um

because again we as you said we use it

and I'd love to know like okay which are

the things that actually matter to us

I'm not going through a hundred things

and figure out what to actually use here

maybe they could build an AI bot that

advises you on what to use um that's

connected to your Hub that says hey

you're because I know I mean God going

back to like 2008 they used to track

like individual usage of individual apps

it's how they knew how you know their

happiness factor for a customer is based

on your app usage within the platform

and that was reported to Partners like

they know what we're using what we're

not like maybe I could talk to their

little AI chat bot and it could tell me

like here's ways you could use AI like

that would be cool um because this is a

lot to process but good to see them

continuing to push out AI updates for

sure all right Paul that's a wrap for

this week we really appreciate as always

you breaking down what's going on in AI

this week um just some final notes here

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highly encourage it Paul until next week

thank you again for breaking it all down

for us I have a feeling this week's

going to like make up for last week I

think there's G to be a lot happening

this week so we will be back with you

next week reporting all the news uh that

matters to you um yeah have a great week

everyone we'll talk to you again soon

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