Why AI Is Tech's Latest Hoax

Tech is a sector unlike any other it's an industry where individuals can turn into billionaires overnight technical ideas supersede business fundamentals and leaders are rewarded for Showmanship over competence in today's Silicon Valley Innovation is crowned not earned to those who can tell a story and look the part of an eccentric genius Venture capitalists and Founders are symbiotic VCS need radical ideas and Founders want to start businesses on someone else's dime unprofitable companies are kept alive with injections of Capital game valuations and manufactured media hype with the goal of surviving long enough to IPO or be

acquired the inspirational speeches motivational essays generous salaries and magazine spreads are no accident it's all thereby designed to keep the talent pipeline of new grads flowing to the sector Founders lean on VC's for access to Capital talent and operational mentorship while VCS lead on to the founders to educate them on technology but VCS aren't profits most are former Wall Street Bankers or celebrities who have never worked in Tech he I don't know if know the story but his initial decision to invest in we work took approximately 28 minutes including when he got in left and

drove in the car minute that you start having to report publicly you have to start playing games with your numbers you have to start playing games with your growth and and usually the person that loses in that situation is the consumer so if we're trying to create extraordinary experiences for consumers over time the longer those companies can stay private and by Masa coming in and enabling that the more unbelievable the experience and more life-changing the the experience will be for people it's the blind leading the blind and both sides must perform to reach the same

payday when you win in Tech you win big hence you only need to win once this is why there is a near infinite pool of aspiring Founders and VCS and why both parties are so quick to forgive reconcile and work together on the next big thing yet whenever investors turn bearish on Tech it doesn't take long for the sector to come up with a new growth Story the most recent was in 2022 when the public market soured on big data and SAS starting in the early 2010s Silicon Valley had championed big data as a revolutionary

technology that could unearth deep insights hidden patterns and Innovation from massive amounts of data Big Data promised a new sophisticated datadriven world where one could precisely predict demand before it existed Trends before they started and behavior before it occurred and there was immense potential for the public and private sector police could prevent crime before it happened researchers could detect cancer before it spread and companies could optimize products make correct decisions and gain a powerful Edge understanding and innovating with data has the potential to change the way we do almost anything for the better there's a

waterfall of information waiting to be tapped in your business's production data log data workflows and more you can unlock new patterns Drive New insights and reinvent the way you do business the the Big Data ecosystem is real it's in the first inning of a nine inning game and in the next 5 years there's virtually no aspect of Our Lives that isn't going to be affected now wouldn't it be great if you had a crystal ball where you could play back historical events and understand what happened in the past what were some of the symptoms what

were some of the data points to be able to predict what could happen so that's what today's topic is all about how do we find insights and also foresight to make better well informed decisions think what happens when we collect all of that data and we can put it together in order to find patterns we wouldn't see before this I would suggest perhaps it will take a while but this will drive a revolution all of a sudden there's a lot of data about people that comes from cell phones comes from credit cards comes from other

things like that and of course people drive Society their wants their habits their fads and so all of a sudden you get to the point where you can begin understanding people in a way we've never been able to before imagine a world where we can predict storms and natural disasters with a much higher degree of accuracy and get people out of Harm's Way much sooner the opportunity to be able to have a significant impact on mankind is is huge and quite frankly it's why I'm so passionate and why I spend all my time um working

in this area of big data and analytics industry after industry is becoming more intense more competitive nastier place to do business basically and this is only going to increase as we move deeper into the era of big data when the better answer comes along stop listening to the hippos and start listening to the Geeks Big Data was a movement as much as a technology yet the market started to question if any of these promises were real as the vast majority of consumer startups and SAS companies were still bleeding years after their IPO even in the

most favorable low-interest business conditions in history there were barely any winners to point to out of nowhere chat GPT was released and AI became silicon Valley's New Growth story in less than 2 years everyone has forgotten about big data and SAS every tech company is now an AI company every Fortune 500 needs an AI strategy VCS are only investing in AI startups everyone's title on LinkedIn mentions Ai and every product is an AI product to maintain hype AI was brought to the public sector Silicon Valley figureheads put on a performance in front of Congress begging

for regulation urging protection for workers whose jobs would be displaced and fear-mongering about an apocalypse this song and dance was done over and over again until the White House was spooked we've done it for other industry I mean it the iea did it uh and I think this is a technology that we should treat with that level of seriousness so although difficult uh I think it's important to try to start the conversation on it I mean an AI that could like help design novel biological pathogens an AI that could hack into computer systems I think

these are all scary but these systems can become quite powerful which is why I was happy to be here today and why I think this is so important more importantly the public was convinced of the immense potential of AI and people continued to speculate to the stay with great conviction that artists animators translators and programmers are all next in line to lose their jobs this episode is not a technical debate but rather a deep dive into how AI is just the latest tal spun by Silicon Valley to keep valuations High and the Outlook positive before

AI there was crypto web 3 blockchain virtual reality augmented reality big data iot and wearables all supposedly revolutionary technologies that have never lived up to the hype in this episode we'll dive into into the real market dynamics that push companies and individuals to jump head first into these Trends how this all started with big data and why AI is ultimately just another pump and dump this episode is sponsored by kajabi the ultimate all-in-one platform that helps entrepreneurs build successful online businesses by unlocking predictable reoccurring Revenue as a Creator it's difficult to build a brand as

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kajabi you also get robust analytics easy payment options email marketing tools and customizable website templates all built in you don't need a huge audience to make sustainable income there are thousands of creators on kajabi making six and seven figures with less than 50,000 followers Right Now kajabi is offering a free 30-day free trial to start your business if you go to kajabi.com Modern MBA that's K Aja ai.com NBA kajabi.com MBA and join the creators and entrepreneurs who have made over $6 billion the late 2000s was a period of genuine Innovation the introduction of smartphones and

tablets had created new markets it was the Advent of the mobile internet geolocation 3G and the App Store more could be accomplished in this online world than ever before you could now track the location of users which was data that had never been available before and if you designed for mobile users would organically flock to you for that Superior experience numerous startups emerged eager to be the first movers on these platforms one was Groupon an online platform that that sent out coupons for local businesses their pitch was that they were amassing unprecedented data on consumers

and driving paying customers into the door for merchants which was more than what Google or Facebook had to offer Groupon knew where people lived their age interest behavior and they fed all that data into algorithms to send only the most relevant enticing coupons as the volume of coupons and Merchants increased the more data that was collected and the faster Groupon could push customers to a business and the more money that it would make Pandora story was identical to to Groupon they scientifically abstracted every song in existence down to 480 technical attributes they collected data on

each listener to figure out what they would like they used proprietary algorithms to predict what music they'll like next and they generated personalized playlists the more listeners the more data the more accurate its algorithms would get at playing the right song at the right time for each listener since Pandora knew the age gender and location of every user they could sell to advertisers another popular app was Yelp who asserted user data and Network effects as their MO because Yelp knew who was looking for what and where like Pandora they sold these eyeballs to advertisers GrubHub

went in a different direction and focused on the takeout experience restaurants didn't need to build apps instead they could just pay GrubHub and support online and mobile ordering overnight and GrubHub had the algorithms and data to personalize feeds and predict what users wanted to eat when other first movers in this period were Etsy for square Twitter and WhatsApp by the early 2010s the narrative had broadened smartphones had gone mainstream apps became common and mobile experiences were the norm it was at this time when Mark andreon published his famous essay why software is eating the world

the thesis was simple that data was gold in this new digital world data could be applied to unlock business value product optimization and personalization in any industry users generated data data powered algorithms and algorithms produced Innovation Zinga the studio behind Farmville proclaimed that they were innovators because of data they looked at mon ization retention and engagement every day to quantify what exactly got people hooked and to keep the whal spending as Zinga amassed more users and more data they alleged that their analytics would only become more precise and profitable as an online furniture Storer Wayfair

offered a near infinite amount of furniture Styles and Brands they talked up their real-time proprietary personalization inventory and pricing algorithms that would maximize profits on every piece of furniture CH looked to do the same in education with a social network that collected data on high school students while that product never succeeded it was clear that ch's leaders understood the narrative quote there is a Playbook followed by many companies such as Facebook LinkedIn Netflix or Spotify which is to take a giant category build products and services that consumers value use technology to deliver them at scale

and leverage the data it generates in the case of Facebook it's social LinkedIn professional Netflix entertainment and Spotify music the advantages these companies have built through their data makes each of them very hard to compete against if you own your customer the channel of distribution you collect data you own that data and you're able to use that data to improve your product to monitor what people do to deliver better products and services you should be in the best position to provide overwhelming value to your customer base and to build a giant Mo thousands of new

consumer startups flooded the market backed by venture capital and seeing the same narrative that data was power but these startups did not have the organic adoption and network effects that the first movers like yel Pandora LinkedIn and Facebook had as a result they needed to spend millions of dollars on Advertising just to acquire users this Chase for users and their data was used to justify the unsustainable marketing spend and broken unit economics the spin at the time was that all these startups were unprofitable by Design as they needed to acquire the necessary data to supercharge

their product and business there was wish who boasted that its algorithms could get customers hooked on buying $ junk online an Open Door who bragged that they had so much data that their algorithms could value real estate better than and humans and flip properties anywhere in the country for profit Casper proclaimed that data powered their disruption of the mattress industry and door Dash asserted that it was data that enabled fast delivery order routing and strong earnings for Gig workers and then there was a firm who contended that they had the data to issue microloans for

luxury purchases these are just the startups that we've covered in the past on Modern MBA there were many more like Blue Apron who claimed that they had so much data that they knew every individual's taste profiles they could algorithmically predict demand for any any given meal kit and Achieve production efficiencies that no one else could Warby Parker pushed that they had data that no other Optical company possessed and that they were so data driven that every pair of glasses would be a bestseller Al Birds pounded that they knew more about customers than traditional sneaker Brands

and over time would have higher margins and as many loyal customers as Nike Stitch fix declared that they had more data on shoppers than any department store or fashion brand and that its algorithms could accurately predict what clothes someone would buy sight unseen true promised to transform car buying with algorithms that ingested every sale in history and could generate the most accurate price for any vehicle with the make model and year alone lemonade build itself as a disruptor whose algorithms crunch so much data around the clock that they could process claims and underwrite policies faster

and cheaper than traditional insurance companies Lending Club crowed that it was their data that unlocked business efficiencies and in turn allowed them to issue lower interest loans online within minutes Sofi trumpeted that they were collecting all the behavioral data on customers that conventional Banks did not get which would translate to better accuracy and greater profits as a lender Fitbit disrupted health and fitness by quantifying and visualizing every individual's physical activity Roku and Netflix both asserted that they had so much data as streamers that they understood viewers better than any Film Studio or cable network they

knew what people wanted to watch and by extension had the secret sauce to make any show or movie a hit these are just a few of the thousands of consumer startups that emerged in this period if you if you look at the S1 filing of any consumer Tech IPO of the past 15 years and search for the word data you will see the same narrative spelled out it was a convincing story in what world would data not be useful yet contrary to what Silicon Valley advertises Tech is not a magical Frontier where everyone can be

a winner the reality is that in every industry there can only be a few winners yet no one could argue against the prosperity of Facebook LinkedIn Amazon Google Netflix and Microsoft who were all making a killing with data their earnings were snowballing in ways that the public markets had never seen before the only thing more impressive than Revenue was margins which were the Envy of the entire private sector there was no other stock as reliable valuable and still high potential as Fang if data was the moop for these Tech leaders why couldn't it work for

a startup that was nimbler faster and more concentrated if a startup could apply the same data Playbook to a smaller industry and Achieve just one tenth of what Facebook or Netflix had accomplished it would still be enough to IPO with such high-flying results and thousands of VCS and startups all shouting the same story data became fashionable every one of these consumer startups was crowned as an innovator based on their user growth alone which was misleading given how much of it had been attained through heavy advertising and artificial subsidies data was built as the means to

unlock Innovation but beyond selling data to advertisers no real business value had actually been discovered by the mid-2010s many of these consumer startups were starting to flame out the few that had gone public were losing just as much money if not more than they had been at IPO years before the premise of innovating through data seemed less convincing by the quarter in response Silicon Valley moved the goalpost once again data was still valuable the problem was you just didn't have enough of it or the means to interpret it basic analytics and personalization was no longer

enough what you needed now was terabytes of data sophisticated tools and data scientists to get to the promised land this new trend was called Big Data to maintain valuations and reputations these consumer startups embraced silicon Valley's latest narrative still The Fortune 500 companies were all spooked no CEO wanted to be caught with their pants down no executive dared to say that they weren't data driven and every Wall Street analyst wanted to know how they were going to stop Tech from eating their lunch and the conversation quickly devolved into a pissing match of who had the

most data and best culture Groupon's newest CEO went Allin quote Groupon is in the data stream for every business transaction we see every bit that comes through these businesses which gives us really critical insights we're rewriting our basic personalization and relevance with Advanced Techniques and machine learning the secret to our methodology is a datadriven approach we have more than nine pedabytes of data and we AB test every single feature how many companies do this not a lot none of this would stop Groupon from flaming out in just 5 years Zinga quote we have a Zinga

we have a really strong team of data scientists who look at the relationship between the as we're serving the number type unit and impact on player engagement we have developed specialized algorithms and machine learning we're never going to have the same user data as Facebook but we can get close that's how we stand out when we're talking to advertisers with data science focused engagement Revenue improved but Zinga remained as unsustainable as ever Wayfair Wayfair quote we capture 4 terabytes of data every day and 40 billion customer actions a year we have a depth of data

rare within the home category if you don't have the ability to take advantage and manipulate the data for deep analysis it's tough over the last four years we have built a team of 1900 engineers and data scientists data science and machine learning influences our personalization Dynamic pricing algorithmic merchandising demand forecasting and advertising as a result we have been able to build multiple platforms at a strong Roi deep analysis of data is Central to our business and how we win customers 6 years later Wayfair has not stopped the bleeding and continues to lose nearly a billion

dollars every year Blue Apron quote our direct to Consumer platform is our most valuable asset which provides extensive behavioral insights to drive Innovation we have touched millions of customers and have a lot of data from our six-year history we use machine learning to give a sense of what customers are likely to order this allows us to produce the proper amount of protein and produce which directly impacts food costs and margins none of this helped pull Blue Apron out of its downward spiral rubhub quote we have data on over 100 million orders our algorithms will get

smarter about the most popular dishes in every neighborhood in 900 plus cities there is no other company in the US that has this level of transactional data the velocity of high quality High Fidelity data that we're aggregating is incredible at 70,000 points per day the learnings from our massive tropes of data and our datadriven insights will position us well for years to come grubhub's losses have deepened and its owners are still struggling to find anyone willing to take the company off their hands Lending Club quote we've issued over $40 billion of personal loans in 10

years so the data we have generated is really massive that is a big data Advantage we simply have this big scale that allows us to slice and dice customer profiles create unique experiences and underwriting processes we deploy the latest machine learning to derive more than 100 customized and behavioral attributes half of which are proprietary and based on our unique data assets Lending Club has since cratered in valuation cut back its lending business and is worth less than its Revenue Pandora our biggest strength is the wealth of data and data science capabilities we have built our

product on Rich data and algorithms with 6 billion stations and 76 million users listening to 5 billion hours of Music we built this amazing product because we have access to data and it's what fuels the competitive Advantage Pandora has continued to lose money and users year after year Sofi Sofi we use data and machine learning to iterate and learn which ultimately leads to innovation we built Technologies and processes that enable us to iterate and innovate at a much faster pace and a much lower cost which provides us more access to data and allows us to

provide a better service to members sofi's valuation has since plummeted as the company's quote unquote Innovation is just selling the same lending products that banks have done for Generations albeit with nicer UI and Stitch fix our whole business model is predicated on this amazing data that we have fundamentally data helps us buy more of the right product and get into more of the right people people we have a personalization engine that gives us the ability to deeply understand clients and products and generate powerful recommendations on what products will be successful and with whom to millions

of clients individualized preferences we believe our data science insights offer a significant competitive advantage that will grow over time Stitch fix today is a penny stock who has laid off thousands of employees attempted to push even more on its algorithms to cut costs and still has continued to bleed money and users year after year and we already know how things turned out for Open Door affirm wish Casper and door Dash it took until the early 2020s for the Big Data narrative to die out no consumer startup had demonstrated anything meaningful with big data and no

one was going to wait another 10 years for Progress yet even as the walls closed in these startups held on screaming about machine learning deep learning insights and Innovation to anyone still willing to listen in their last breaths for the sake of what little remained of their stock and reputation these Founders Executives and VCS could never admit the truth yet when the Big Data hype train was at its peak between the early 2010s and the early 2020s Fortune 500 leaders were in crisis mode investors were loving Fang and souring on big corporations as dinosaurs trapped

in the Stone Age Walmart Exxon Mobile Home Depot Comcast Disney PepsiCo ch and other big corporations each committed hundreds of millions of dollars some of them even billions to build out such technical capabilities for themselves they were all eager to signal that they could be just as Cutting Edge as Tech startups and publicly flaunted their investments in Big Data yet their adoption was fueled by fear rather than Merit it's hard to imagine an industry that's not substantially altered by this data Revolution Industries as diverse as medical where the ability to make better diagnostic decisions with

the aid of data or to bring more transparency to issues of cost and quality can be transformative we're definitely a deeply data driven business uh from the data that's coming from our products our airplanes and other products uh from the data that we use to manage our business and it's going to be the key differentiator we are on the Forefront of a revolution right so today the biggest and most competitive companies in the world will be the one harnessing data the team that I'm on directly is a team leading the the the headlights of the

organization so that's something I'm super excited about we've really been building backend big data analytic capabilities now for a couple of years and you know data is a data is a huge asset for us it's surprising to me that more people in our space are talking about it and especially with us two billion visits a year between our online and our stores using that big data against our best customers it's a huge asset and structurally because we have multiple Brands and multi- channels we've got something not a lot of other apparel companies data could help

drill down to see if specific products are leading to infant deaths so if if we start to see these high mortality rates and we're seeing lots of canant tuna being purchased by these families then we'll try to look is there something with the can tuna that may be uh uh helping or causing some of those higher rates in infant mortality when the airplane is in Flight we know exactly what they need and we know exactly what part is located if the next destination do not have the part then we had to find the weight of

flying the part coming in so the key thing is fixing the airplane at the right place at the right time with the right part and if we can't do that then we don't have to worry about airplane grounded unen lever is one of the world's largest consumer products goods company our products touch 2 and a half billion consumers every day with Brands like helman's Ben and& Jerry's and Dove we're on a journey to become a Data Insights driven company for our employees that means providing insights for them to make better decisions to help them collaborate

develop better products to innovate and we're finding that instead of looking in the rear view mirror with analytics people are now being able to predict the future the whole raft of initiatives around big data and machine learning um that will actually help insurers and Brokers to increase the power through which they um use their own data as I see that as being something that is an area that really needs to be um improved as an industry there's more and more and more data available but actually how do you use that link it all together join

it all up and turn it into something that is actually actionable in real time data and making that data available analyzing it are all ways that we can think about crafting a unique experience for our customers at jebl we like to say that we're a customer service company that just happens to fly planes now it almost seems as though we're also a technology company that happens to fly planes no CEO wants to be the one who screwed the pooch and it was safer to have an iron in the fire than to walk against Silicon Valley

and smaller companies got just as wrapped up in the narrative with the best example being under arour who dropped over half a billion dollars on fitness apps with the dream of achieving Fang margins and uncovering business insights and user data it's said that during a gold rush you should sell shovels for the handful of startups that we've named there were thousands more of these startups writing the same narrative that never went public and yet once the corporations joined the Big Data Revolution the demand for talent skyrocketed these were Greenfield Technologies and it was believed that

the more bodies you could throw at complexity the faster you would arrive at a solution job opportunities and salaries for data scientists and software Engineers reached record highs as the private sector competed for talent but this only addressed the problem of who and not how the majority of consumer startups and big corporations lack the technical means to extract store manipulate analyze and visualize data and it would take too long to build everything themselves it would be faster and cheaper to Simply buy the tools if they existed this demand spawned a stream of Enterprise startups who

rushed into to provide ready to go out of the box software tools made for Big Data because thousands of consumer startups had Stak their existence on these Technologies and the Fortune 500 were now willing to spend big to catch up billions of dollars floa in year after year towards these B2B startups unlike the Venture Capital backed consumer startups these corporations had the appetite and the runway to spend 7 to eight figures in perpetuity on any vendor who could help them get to the promised land if we revisit the tech IPOs of the past decade and

we look at the companies with the greatest appreciation in valuation since IPO the winners are nearly all Enterprise startups data dog and Splunk are two leaders in Telemetry that to this day have reached billion dollar valuations selling table Stakes for capturing and monitoring dat data Tableau and apogee both benefited dramatically from the Big Data Trend through the 2010s snowflake Sumo logic Horton works and clera are all startups that quickly surpassed hundreds of millions of dollars in Revenue selling Essentials for big data and all those pedabytes needed to be stored someware which led to storage startups

like Rackspace back Blaze box and database startups like mongodb the demand for user data meant greater emphasis on SMS and email which srid ring central and twilio were all happy to provide services is for for a price the volume of information sensitivity of data and complexity of application logic made companies targets for hackers security vendors like Cloud strike Barracuda OCTA and paloalto networks have all thrived under the promise of helping Enterprises and startups secure their data and to accelerate execution startups like atashin service now jfrog Asana and slack all build themselves as critical tools for

boosting worker productivity and collaboration being B2B by default doesn't make for a better business as snowflake and many other tech companies to this day are still chasing profitability but their valuations are significantly more resilient than those of consumer startups given the widespread demand the exceptional deal size and the slow turn that's unique to Enterprise software once a tool or vendor is ingrained at a big company it's extremely difficult to rip out yet not every Fortune 500 had the talent to execute on big data and most needed to pay an outside firm to perform the implementation

the money flowed not just to tooling but also to Consulting Accenture HP IBM Oracle boo Allen Hamilton and Gardner all pledged that they had the expertise to pull off any big data project it's no surprise that these same Consultants are now tooting their horns about AI yet ultimately the companies that profited the most from Big Data were the cloud players AWS gcp and Azure saw their greatest growth during this 10-year period the consumer startups were spending their funding building their products in the cloud the fortune 500s were using big data as a forcing function to

adopt cloud in their organizations and B2B startups were also building their tools in the cloud to the cloud providers it doesn't matter if it's big data machine learning iot augmented reality or AI as long as people are using the cloud and whatever trending technology drives them to use more of it Amazon Google and Microsoft all win software Engineers have always been in high demand but the Paradigm shifted in this time period with big data across the private sector companies were on the hunt for practitioners offering the highest salaries to the few available with real world

experience and Big Data since the technology was so new there were no best practices everyone was figuring it out as they went along and doing it all in the open on GitHub if you're an engineer you could get experience in Big Data through your day job or at home through open source if you knew your way around the most popular tools you could declare that proficiency on your resume and get rewarded with a higher paying job elsewhere within months Big Data Amplified resumed driven software development where Engineers now are incentivized to learn and even evangelize

Technologies for the sole purpose of maximizing compensation and hireability developers today are no longer coding grunts but instead vocal visible rock stars who can make demands and spearhead change at their organizations engineers get to decide on the behalf of their companies what clouds they want to use what tools they get to adopt and what vendors they want to partner with this is why Enterprise startups and Cloud vendors each spend millions of dollars every year on conventions free pizza and beer just to court developers and to try to convert them into champions of their products this

shift to Bottoms Up decisionmaking with Engineers leading the way is almost like a battle of religions where every Enterprise startup is trying to convert the most possible developers to their faith at any given time as a result software engineering has become progressively more tribal as developers have hitched their paychecks and career prospects to the popularity of the technologies that they adopt rather than their business contribution because when you think about like getting to those cool kids building Trust getting up to say hey I'm going to bet my company's infrastructure on a product You released 3

months ago that barely works that's a very high level of trust and that person needs to have very high conviction that they're not making a career ruing mistake and companies keep investing because their technical teams are all incentivized to play up their work even when there's nothing to show that's the only way these Engineers their managers and their VPS can justify budgets get raises and climb the ladder the people that worked on Big Data had a vested interest in keep keeping the technology trendy despite the lack of results and are now doing the same with

AI whether it's bottoms up or top down adoption Money Talks even for those who are simply implementing the technology itself this is why there's never any end to the online debates between react versus angular kubernetes versus ECS and so on you can see that same tribalism in AI where there's no consensus on what tool is best tensor flow or pie torch or which model is most accurate the premise of big data was that you could unearth hidden Innovation business value customer insights and Market patterns from data that was simply too overwhelming for a human to

digest and analyze these days everyone has seemingly forgotten about big data's failed promises but the current premise of AI is even more confusing the new narrative is that data is inherently too complex instead what we're supposed to believe is that all that promised Innovation buried insights and hidden business value is still in this data it's just that humans can't pull it out instead our only solution is to trust these artificial models where only a few people have a true understanding of what's really happening under the hood for perspective and insights we should be told the

answers and not seek it ourselves and we should value the digestibility and presentation above the accuracy of information itself for Big Data every company had its own data timelines politics and priorities which made execution completely unique every implementation was a snowflake deployment since no one has achieved any value with big data no one really really knows even now nearly a decade later how to actually Implement and derive value no one can authoritatively say this is how you execute and this is how you can replicate step for step what we did for your own business Engineers

essentially are Reinventing the wheel but never finishing managers and Executives push for vanity updates and exaggerate their progress for the sake of growing their organizational influence appeasing higher ups and securing their own promotions the market dynamics that propelled Big Data are now playing out again with AI with thousands of new consumer startups arriving on the market with AI products with only the promise of business value Enterprise vendors who are now pedaling AI tools to sell to these consumer startups The Fortune 500 are Running Scared once again the cloud providers and chipmakers are laughing from their

Ivory Towers at the money that's raining down from the heavens Engineers are jumping into the latest open- Source projects to pad resumés and improve their career prospects and Tech is once again the darling of Wall Street because the changes the transformation in compute is accelerating at such a pace and the implications on productivity and efficiency in business are going to be enormous so what just happened here is we're actually using our deep Brew AI platform to be able to suggest uh optimal product pairings based off of uh contextual information of the store the weather and

other things that are going on that AI allows better allocation of resources basically it allows you better planning you save money you become more more efficient there's every reason to think that the use of AI will make our businesses more efficient therefore just literally waste less money that's good it's good for economics like Airbnb and Uber chat GPT was built on the similar Playbook of skirting regulation chat GPT went viral for its breadth of knowledge and its convincing humanlike Pros but when all of that is built on Stolen data who holds the power is it

the people who generated the data or the technology that summarized it like every other hyped Silicon Valley technology of the past deade AI is just another cash grab the primary reason of why open ai's employees revolted when Sam Alman was fired was because his removal made their stock worthless and the staff there are all looking for a Payday just like Sam the board and the Venture capitalists it's not a question of whether or not large language models have Merit or to dispute that technology improves over time but based on how little business value was delivered

in the past decade from Big Data blockchain SAS and every other aformentioned Trend AI deserves much greater scrutiny not every Innovation can be monetized and not every promising technology needs to be a business big data did not make any difference for the consumer startups and the Fortune 500 over the past decade if anything it only prolong the lifespan of startups that should have never existed in the first place these days all companies are doing layoffs cutting costs engaging in shrinkflation and buying back shares to squeeze profits just like they've always done things that they shouldn't

have to do if big data actually generated the greater business efficiencies and Innovation that was promised there's nothing in the fundamentals that suggests that big data has Ned any meaningful improvements for any company If there really was business value in Big Data someone by now would have something to show for it the same exact dynamic is now playing out with AI it's all talk no show all hype and no results all Sizzle and no stake and everyone is falling for the same ruse once again the only winners from Big Data were the founders Executives and

Venture capitalists of the consumer startups who all liquidated their shares at IPO or those of the B2B startups the losers were the public the investors and the employees at these companies if you had to guess who are the winners and losers of AI really going to be and what is the value of information without critical thinking art without authenticity and creation without originality