#tech advertising companies

[shivamtalkd]

Empowering Brands: How Design Thinking Revolutionizes Digital Marketing Strategies

In the ever-evolving landscape of marketing, the intersection of technology and creativity has become the cornerstone for success. As brands grapple with the dynamic shifts in consumer behavior, the emergence of design thinking as a guiding principle has revolutionized the approach of leading digital marketing and advertising agencies. Amidst this transformative wave, Talkd, one of Pune's premier digital marketing and advertising agencies, stands tall as an epitome of innovation and strategic prowess.

The era of traditional marketing methods is dwindling as businesses recognize the need for a paradigm shift. Talkd's foundation lies in leveraging Design Thinking to redefine marketing strategies, recognizing the essence of understanding the pulse of businesses. This agency's approach amalgamates business intelligence with cutting-edge technology, fostering an environment conducive to groundbreaking solutions across multiple domains.

At the core of Talkd's methodology lies a comprehensive process encapsulating stakeholder interviews, data-driven research, and immersive workshops. This approach ensures a deep-rooted understanding of client needs and challenges, thereby paving the way for innovative solutions. By placing the end-user at the heart of the process, Talkd crafts experiences that resonate and create lasting impressions.

The agency's prowess extends to various facets of brand development, marketing, sales strategies, UX enhancements, and leadership empowerment. Leveraging Design Thinking, Talkd not only audits and positions brands strategically but also crafts compelling narratives that resonate with the target audience.

In a digitally-driven world, marketing transcends mere communication—it's an immersive experience. Talkd's expertise lies in decoding this intricate realm, offering tailored marketing strategies that encompass SEO-driven content plans, multi-channel promotions, and meticulous inbound/outbound campaigns.

Furthermore, Talkd revolutionizes sales strategies by infusing Design Thinking methodologies, catering to personalized experiences and enhanced conversions. Through workshops and meticulous sales playbooks, the agency equips sales teams with the tools to navigate the competitive landscape adeptly.

UX enhancements form another cornerstone of Talkd's offerings, where user expectations are meticulously dissected, and pain points addressed through a comprehensive UX strategy. This approach ensures that products and services resonate deeply with the end-user, fostering brand loyalty and affinity.

Notably, Talkd doesn't limit its impact solely to marketing and sales. Their leadership alignment programs usher in a new era of human-centric decision-making, empowering leaders to navigate complexities and align organizational goals with employee and consumer experiences.

In conclusion, Talkd exemplifies the evolution of digital marketing and advertising agencies. Through the adoption of Design Thinking as a guiding principle, the agency propels brands towards success by orchestrating seamless and impactful experiences, making a lasting imprint in the ever-evolving digital landscape.

[kply-industries]

[snickerdoodlles]

akdj profit-making monster

bold fucking choice of words for a company that's several hundred million in the red in its yearly income

[cryptidize]

I think we may see the end of Google and YouTube in our lifetimes.

[settlercolonialismisbad]

this article reminds me that i never reviewed the reddit ios app, despite having it on my phone for years before switching to apollo and then deleting my accounts in light of reddit’s recent bad faith behavior. i think i’ll go do that.

[bytebun]

.

[glasshomewrecker]

In a silicon valley, throw rocks. Welcome to my tech blog.

Antiterf antifascist (which apparently needs stating). This sideblog is open to minors.

Liberation does not come at the expense of autonomy.

* I'm taking a break from tumblr for a while. Feel free to leave me asks or messages for when I return.

Frequent tags:

[raziraphale]

so I work at [redacted tech company] right and while I only do one specific thing for one specific client all day, I still have to like have a like, general awareness of company offerings. and it's very funny remembering that when I was hired a few years ago my training was all about the power of The Blockchain™, and these days I just get emails that won't shut up about the future of AI. What happened the blockchain guys you used to love the blockchain. did something happen? </3 surely nothing will happen to AI tho no way

[babysgarage]

don't really have enough coherent thoughts to elaborate on this but this is what USA feels like to me re: ads

[jcmarchi]

What do we know about the economics of AI?

New Post has been published on https://thedigitalinsider.com/what-do-we-know-about-the-economics-of-ai/

What do we know about the economics of AI?

For all the talk about artificial intelligence upending the world, its economic effects remain uncertain. There is massive investment in AI but little clarity about what it will produce.

Examining AI has become a significant part of Nobel-winning economist Daron Acemoglu’s work. An Institute Professor at MIT, Acemoglu has long studied the impact of technology in society, from modeling the large-scale adoption of innovations to conducting empirical studies about the impact of robots on jobs.

In October, Acemoglu also shared the 2024 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel with two collaborators, Simon Johnson PhD ’89 of the MIT Sloan School of Management and James Robinson of the University of Chicago, for research on the relationship between political institutions and economic growth. Their work shows that democracies with robust rights sustain better growth over time than other forms of government do.

Since a lot of growth comes from technological innovation, the way societies use AI is of keen interest to Acemoglu, who has published a variety of papers about the economics of the technology in recent months.

“Where will the new tasks for humans with generative AI come from?” asks Acemoglu. “I don’t think we know those yet, and that’s what the issue is. What are the apps that are really going to change how we do things?”

What are the measurable effects of AI?

Since 1947, U.S. GDP growth has averaged about 3 percent annually, with productivity growth at about 2 percent annually. Some predictions have claimed AI will double growth or at least create a higher growth trajectory than usual. By contrast, in one paper, “The Simple Macroeconomics of AI,” published in the August issue of Economic Policy, Acemoglu estimates that over the next decade, AI will produce a “modest increase” in GDP between 1.1 to 1.6 percent over the next 10 years, with a roughly 0.05 percent annual gain in productivity.

Acemoglu’s assessment is based on recent estimates about how many jobs are affected by AI, including a 2023 study by researchers at OpenAI, OpenResearch, and the University of Pennsylvania, which finds that about 20 percent of U.S. job tasks might be exposed to AI capabilities. A 2024 study by researchers from MIT FutureTech, as well as the Productivity Institute and IBM, finds that about 23 percent of computer vision tasks that can be ultimately automated could be profitably done so within the next 10 years. Still more research suggests the average cost savings from AI is about 27 percent.

When it comes to productivity, “I don’t think we should belittle 0.5 percent in 10 years. That’s better than zero,” Acemoglu says. “But it’s just disappointing relative to the promises that people in the industry and in tech journalism are making.”

To be sure, this is an estimate, and additional AI applications may emerge: As Acemoglu writes in the paper, his calculation does not include the use of AI to predict the shapes of proteins — for which other scholars subsequently shared a Nobel Prize in October.

Other observers have suggested that “reallocations” of workers displaced by AI will create additional growth and productivity, beyond Acemoglu’s estimate, though he does not think this will matter much. “Reallocations, starting from the actual allocation that we have, typically generate only small benefits,” Acemoglu says. “The direct benefits are the big deal.”

He adds: “I tried to write the paper in a very transparent way, saying what is included and what is not included. People can disagree by saying either the things I have excluded are a big deal or the numbers for the things included are too modest, and that’s completely fine.”

Which jobs?

Conducting such estimates can sharpen our intuitions about AI. Plenty of forecasts about AI have described it as revolutionary; other analyses are more circumspect. Acemoglu’s work helps us grasp on what scale we might expect changes.

“Let’s go out to 2030,” Acemoglu says. “How different do you think the U.S. economy is going to be because of AI? You could be a complete AI optimist and think that millions of people would have lost their jobs because of chatbots, or perhaps that some people have become super-productive workers because with AI they can do 10 times as many things as they’ve done before. I don’t think so. I think most companies are going to be doing more or less the same things. A few occupations will be impacted, but we’re still going to have journalists, we’re still going to have financial analysts, we’re still going to have HR employees.”

If that is right, then AI most likely applies to a bounded set of white-collar tasks, where large amounts of computational power can process a lot of inputs faster than humans can.

“It’s going to impact a bunch of office jobs that are about data summary, visual matching, pattern recognition, et cetera,” Acemoglu adds. “And those are essentially about 5 percent of the economy.”

While Acemoglu and Johnson have sometimes been regarded as skeptics of AI, they view themselves as realists.

“I’m trying not to be bearish,” Acemoglu says. “There are things generative AI can do, and I believe that, genuinely.” However, he adds, “I believe there are ways we could use generative AI better and get bigger gains, but I don’t see them as the focus area of the industry at the moment.”

Machine usefulness, or worker replacement?

When Acemoglu says we could be using AI better, he has something specific in mind.

One of his crucial concerns about AI is whether it will take the form of “machine usefulness,” helping workers gain productivity, or whether it will be aimed at mimicking general intelligence in an effort to replace human jobs. It is the difference between, say, providing new information to a biotechnologist versus replacing a customer service worker with automated call-center technology. So far, he believes, firms have been focused on the latter type of case. 

“My argument is that we currently have the wrong direction for AI,” Acemoglu says. “We’re using it too much for automation and not enough for providing expertise and information to workers.”

Acemoglu and Johnson delve into this issue in depth in their high-profile 2023 book “Power and Progress” (PublicAffairs), which has a straightforward leading question: Technology creates economic growth, but who captures that economic growth? Is it elites, or do workers share in the gains?

As Acemoglu and Johnson make abundantly clear, they favor technological innovations that increase worker productivity while keeping people employed, which should sustain growth better.

But generative AI, in Acemoglu’s view, focuses on mimicking whole people. This yields something he has for years been calling “so-so technology,” applications that perform at best only a little better than humans, but save companies money. Call-center automation is not always more productive than people; it just costs firms less than workers do. AI applications that complement workers seem generally on the back burner of the big tech players.

“I don’t think complementary uses of AI will miraculously appear by themselves unless the industry devotes significant energy and time to them,” Acemoglu says.

What does history suggest about AI?

The fact that technologies are often designed to replace workers is the focus of another recent paper by Acemoglu and Johnson, “Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution — and in the Age of AI,” published in August in Annual Reviews in Economics.

The article addresses current debates over AI, especially claims that even if technology replaces workers, the ensuing growth will almost inevitably benefit society widely over time. England during the Industrial Revolution is sometimes cited as a case in point. But Acemoglu and Johnson contend that spreading the benefits of technology does not happen easily. In 19th-century England, they assert, it occurred only after decades of social struggle and worker action.

“Wages are unlikely to rise when workers cannot push for their share of productivity growth,” Acemoglu and Johnson write in the paper. “Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed. … The impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages.”

The paper’s title refers to the social historian E.P Thompson and economist David Ricardo; the latter is often regarded as the discipline’s second-most influential thinker ever, after Adam Smith. Acemoglu and Johnson assert that Ricardo’s views went through their own evolution on this subject.

“David Ricardo made both his academic work and his political career by arguing that machinery was going to create this amazing set of productivity improvements, and it would be beneficial for society,” Acemoglu says. “And then at some point, he changed his mind, which shows he could be really open-minded. And he started writing about how if machinery replaced labor and didn’t do anything else, it would be bad for workers.”

This intellectual evolution, Acemoglu and Johnson contend, is telling us something meaningful today: There are not forces that inexorably guarantee broad-based benefits from technology, and we should follow the evidence about AI’s impact, one way or another.

What’s the best speed for innovation?

If technology helps generate economic growth, then fast-paced innovation might seem ideal, by delivering growth more quickly. But in another paper, “Regulating Transformative Technologies,” from the September issue of American Economic Review: Insights, Acemoglu and MIT doctoral student Todd Lensman suggest an alternative outlook. If some technologies contain both benefits and drawbacks, it is best to adopt them at a more measured tempo, while those problems are being mitigated.

“If social damages are large and proportional to the new technology’s productivity, a higher growth rate paradoxically leads to slower optimal adoption,” the authors write in the paper. Their model suggests that, optimally, adoption should happen more slowly at first and then accelerate over time.

“Market fundamentalism and technology fundamentalism might claim you should always go at the maximum speed for technology,” Acemoglu says. “I don’t think there’s any rule like that in economics. More deliberative thinking, especially to avoid harms and pitfalls, can be justified.”

Those harms and pitfalls could include damage to the job market, or the rampant spread of misinformation. Or AI might harm consumers, in areas from online advertising to online gaming. Acemoglu examines these scenarios in another paper, “When Big Data Enables Behavioral Manipulation,” forthcoming in American Economic Review: Insights; it is co-authored with Ali Makhdoumi of Duke University, Azarakhsh Malekian of the University of Toronto, and Asu Ozdaglar of MIT.

“If we are using it as a manipulative tool, or too much for automation and not enough for providing expertise and information to workers, then we would want a course correction,” Acemoglu says.

Certainly others might claim innovation has less of a downside or is unpredictable enough that we should not apply any handbrakes to it. And Acemoglu and Lensman, in the September paper, are simply developing a model of innovation adoption.

That model is a response to a trend of the last decade-plus, in which many technologies are hyped are inevitable and celebrated because of their disruption. By contrast, Acemoglu and Lensman are suggesting we can reasonably judge the tradeoffs involved in particular technologies and aim to spur additional discussion about that.

How can we reach the right speed for AI adoption?

If the idea is to adopt technologies more gradually, how would this occur?

First of all, Acemoglu says, “government regulation has that role.” However, it is not clear what kinds of long-term guidelines for AI might be adopted in the U.S. or around the world.

Secondly, he adds, if the cycle of “hype” around AI diminishes, then the rush to use it “will naturally slow down.” This may well be more likely than regulation, if AI does not produce profits for firms soon.

“The reason why we’re going so fast is the hype from venture capitalists and other investors, because they think we’re going to be closer to artificial general intelligence,” Acemoglu says. “I think that hype is making us invest badly in terms of the technology, and many businesses are being influenced too early, without knowing what to do. We wrote that paper to say, look, the macroeconomics of it will benefit us if we are more deliberative and understanding about what we’re doing with this technology.”

In this sense, Acemoglu emphasizes, hype is a tangible aspect of the economics of AI, since it drives investment in a particular vision of AI, which influences the AI tools we may encounter.

“The faster you go, and the more hype you have, that course correction becomes less likely,” Acemoglu says. “It’s very difficult, if you’re driving 200 miles an hour, to make a 180-degree turn.”

[goldengodcannibal]

It's so hard to really articulate the grip that Silicon Valley has around the bay.

[shivamtalkd]

Talkd: Revolutionizing Digital Marketing Through Design Thinking in Pune

Visit Us at: https://talkd.co/

[blackrealman098]

भारतीय शेअर बाजार की गिरावट के बाद आप कौन-कौन से स्टॉक लोंगे ?

[technoregression]

Because what is exclusive?

It’s defined by all it excludes, NOT by who or what it includes

[phantomrose96]

If anyone wants to know why every tech company in the world right now is clamoring for AI like drowned rats scrabbling to board a ship, I decided to make a post to explain what's happening.

(Disclaimer to start: I'm a software engineer who's been employed full time since 2018. I am not a historian nor an overconfident Youtube essayist, so this post is my working knowledge of what I see around me and the logical bridges between pieces.)

Okay anyway. The explanation starts further back than what's going on now. I'm gonna start with the year 2000. The Dot Com Bubble just spectacularly burst. The model of "we get the users first, we learn how to profit off them later" went out in a no-money-having bang (remember this, it will be relevant later). A lot of money was lost. A lot of people ended up out of a job. A lot of startup companies went under. Investors left with a sour taste in their mouth and, in general, investment in the internet stayed pretty cooled for that decade. This was, in my opinion, very good for the internet as it was an era not suffocating under the grip of mega-corporation oligarchs and was, instead, filled with Club Penguin and I Can Haz Cheezburger websites.

Then around the 2010-2012 years, a few things happened. Interest rates got low, and then lower. Facebook got huge. The iPhone took off. And suddenly there was a huge new potential market of internet users and phone-havers, and the cheap money was available to start backing new tech startup companies trying to hop on this opportunity. Companies like Uber, Netflix, and Amazon either started in this time, or hit their ramp-up in these years by shifting focus to the internet and apps.

Now, every start-up tech company dreaming of being the next big thing has one thing in common: they need to start off by getting themselves massively in debt. Because before you can turn a profit you need to first spend money on employees and spend money on equipment and spend money on data centers and spend money on advertising and spend money on scale and and and

But also, everyone wants to be on the ship for The Next Big Thing that takes off to the moon.

So there is a mutual interest between new tech companies, and venture capitalists who are willing to invest $$$ into said new tech companies. Because if the venture capitalists can identify a prize pig and get in early, that money could come back to them 100-fold or 1,000-fold. In fact it hardly matters if they invest in 10 or 20 total bust projects along the way to find that unicorn.

But also, becoming profitable takes time. And that might mean being in debt for a long long time before that rocket ship takes off to make everyone onboard a gazzilionaire.

But luckily, for tech startup bros and venture capitalists, being in debt in the 2010's was cheap, and it only got cheaper between 2010 and 2020. If people could secure loans for ~3% or 4% annual interest, well then a $100,000 loan only really costs $3,000 of interest a year to keep afloat. And if inflation is higher than that or at least similar, you're still beating the system.

So from 2010 through early 2022, times were good for tech companies. Startups could take off with massive growth, showing massive potential for something, and venture capitalists would throw infinite money at them in the hopes of pegging just one winner who will take off. And supporting the struggling investments or the long-haulers remained pretty cheap to keep funding.

You hear constantly about "Such and such app has 10-bazillion users gained over the last 10 years and has never once been profitable", yet the thing keeps chugging along because the investors backing it aren't stressed about the immediate future, and are still banking on that "eventually" when it learns how to really monetize its users and turn that profit.

The pandemic in 2020 took a magnifying-glass-in-the-sun effect to this, as EVERYTHING was forcibly turned online which pumped a ton of money and workers into tech investment. Simultaneously, money got really REALLY cheap, bottoming out with historic lows for interest rates.

Then the tide changed with the massive inflation that struck late 2021. Because this all-gas no-brakes state of things was also contributing to off-the-rails inflation (along with your standard-fare greedflation and price gouging, given the extremely convenient excuses of pandemic hardships and supply chain issues). The federal reserve whipped out interest rate hikes to try to curb this huge inflation, which is like a fire extinguisher dousing and suffocating your really-cool, actively-on-fire party where everyone else is burning but you're in the pool. And then they did this more, and then more. And the financial climate followed suit. And suddenly money was not cheap anymore, and new loans became expensive, because loans that used to compound at 2% a year are now compounding at 7 or 8% which, in the language of compounding, is a HUGE difference. A $100,000 loan at a 2% interest rate, if not repaid a single cent in 10 years, accrues to $121,899. A $100,000 loan at an 8% interest rate, if not repaid a single cent in 10 years, more than doubles to $215,892.

Now it is scary and risky to throw money at "could eventually be profitable" tech companies. Now investors are watching companies burn through their current funding and, when the companies come back asking for more, investors are tightening their coin purses instead. The bill is coming due. The free money is drying up and companies are under compounding pressure to produce a profit for their waiting investors who are now done waiting.

You get enshittification. You get quality going down and price going up. You get "now that you're a captive audience here, we're forcing ads or we're forcing subscriptions on you." Don't get me wrong, the plan was ALWAYS to monetize the users. It's just that it's come earlier than expected, with way more feet-to-the-fire than these companies were expecting. ESPECIALLY with Wall Street as the other factor in funding (public) companies, where Wall Street exhibits roughly the same temperament as a baby screaming crying upset that it's soiled its own diaper (maybe that's too mean a comparison to babies), and now companies are being put through the wringer for anything LESS than infinite growth that Wall Street demands of them.

Internal to the tech industry, you get MASSIVE wide-spread layoffs. You get an industry that used to be easy to land multiple job offers shriveling up and leaving recent graduates in a desperately awful situation where no company is hiring and the market is flooded with laid-off workers trying to get back on their feet.

Because those coin-purse-clutching investors DO love virtue-signaling efforts from companies that say "See! We're not being frivolous with your money! We only spend on the essentials." And this is true even for MASSIVE, PROFITABLE companies, because those companies' value is based on the Rich Person Feeling Graph (their stock) rather than the literal profit money. A company making a genuine gazillion dollars a year still tears through layoffs and freezes hiring and removes the free batteries from the printer room (totally not speaking from experience, surely) because the investors LOVE when you cut costs and take away employee perks. The "beer on tap, ping pong table in the common area" era of tech is drying up. And we're still unionless.

Never mind that last part.

And then in early 2023, AI (more specifically, Chat-GPT which is OpenAI's Large Language Model creation) tears its way into the tech scene with a meteor's amount of momentum. Here's Microsoft's prize pig, which it invested heavily in and is galivanting around the pig-show with, to the desperate jealousy and rapture of every other tech company and investor wishing it had that pig. And for the first time since the interest rate hikes, investors have dollar signs in their eyes, both venture capital and Wall Street alike. They're willing to restart the hose of money (even with the new risk) because this feels big enough for them to take the risk.

Now all these companies, who were in varying stages of sweating as their bill came due, or wringing their hands as their stock prices tanked, see a single glorious gold-plated rocket up out of here, the likes of which haven't been seen since the free money days. It's their ticket to buy time, and buy investors, and say "see THIS is what will wring money forth, finally, we promise, just let us show you."

To be clear, AI is NOT profitable yet. It's a money-sink. Perhaps a money-black-hole. But everyone in the space is so wowed by it that there is a wide-spread and powerful conviction that it will become profitable and earn its keep. (Let's be real, half of that profit "potential" is the promise of automating away jobs of pesky employees who peskily cost money.) It's a tech-space industrial revolution that will automate away skilled jobs, and getting in on the ground floor is the absolute best thing you can do to get your pie slice's worth.

It's the thing that will win investors back. It's the thing that will get the investment money coming in again (or, get it second-hand if the company can be the PROVIDER of something needed for AI, which other companies with venture-back will pay handsomely for). It's the thing companies are terrified of missing out on, lest it leave them utterly irrelevant in a future where not having AI-integration is like not having a mobile phone app for your company or not having a website.

So I guess to reiterate on my earlier point:

Drowned rats. Swimming to the one ship in sight.

[ewanvelazquez]

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