The deflationary unit economics of AI
It's instructive to look at the unit economics.
As the AI systems scale, the cost of producing additional outputs - whether it's content, decisions, insights, or services - drops sharply, often approaching zero
The rapid rollout of AI tools by tech giants such as Meta, Google, TikTok and Amazon, aims to replace much of the work traditionally carried out by ad agencies.
Artificial intelligence was hardly a recognized concept in 1956, when leading computer scientists gathered at Dartmouth College for a summer conference.
The term had just been coined by John McCarthy in the event's funding proposal—a visionary attempt to explore how machines might one day use language, solve problems like humans, and even improve themselves.
The group's bold founding belief was bold 'Any feature of human intelligence could ,in principle , be so precisely described that a machine can be made to simulate it.'
That is now a reality. Or is it ?
Meta, which owns Facebook and Instagram, with its 'Infinite Creative' is giving AI-powered, automated, scalable ad creation and optimization.
It's part of Meta's strategy to make advertising more efficient and performance-driven by combining machine learning, personalization, and automation.
Amazon Ads now offers
generative AI
tools that enable brands to create their own ads and dynamically allocate budgets in real time across both linear TV and streaming platforms.
This has major economic and societal implications.
Here's an expansion:
A) Near-Zero Marginal Cost
Once an AI model is trained (often at high upfront cost), it can generate results :text, images, predictions and code at almost no additional cost per unit.
AI doesn't tire, forget, demand higher wages, or require physical resources to scale its output.
B) Productivity Increases Without Proportional Cost
AI tools allow individuals and companies to do more with less.
A single employee with AI assistance can produce the output of a team, reducing the need for large headcounts and pushing prices down.
C) Market Pressure to Lower Prices
As more businesses adopt AI, competition forces everyone to reduce prices. Advertising , Media Buying, Design , Education, Legal services and other knowledge-based industries are vulnerable.
The question remain on the value of Differentiation, Trust, Curation and authenticity of Human experience.
The system is efficient.
The output is infinite.
But will a rolling tear on a cheek still produce poetry?
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Mint
4 hours ago
- Mint
Technobabble: We need a whole new vocabulary to keep up with the evolution of AI
The artificial intelligence (AI) news flow does not stop, and it's becoming increasingly obscure and pompous. China's MiniMax just spiked efficiency and context length, but we are not gasping. Elon Musk says Grok will 'redefine human knowledge," but is that a new algorithm or just hot air? Andrej Karpathy's 'Software 3.0" sounds clever but lacks real-world bite. Mira Murati bet $2 billion on 'custom models," a term so vague it could mean anything. And only by testing Kimi AI's 'Researcher" did we get why it's slick and different. Technology now sprints past our words. As machines get smarter, our language lags. Buzzwords, recycled slogans and podcast quips fill the air but clarify nothing. This isn't just messy, it's dangerous. Investors chase vague terms, policymakers regulate without definitions and the public confuses breakthroughs with sci-fi. Also Read: An AI gadget mightier than the sword? We're in a tech revolution with a vocabulary stuck in the dial-up days. We face a generational shift in technology without a stable vocabulary to navigate it. This language gap is not a side issue. It is a core challenge that requires a new discipline: a fierce scepticism of hype and a deep commitment to the details. The instinct to simplify is a trap. Once, a few minutes was enough to explain breakthrough apps like Google or Uber. Now, innovations in robotics or custom silicon resist such compression. Understanding OpenAI's strategy or Nvidia's product stack requires time, not sound-bites. We must treat superficial simplicity as a warning sign. Hot areas like AI 'agents' or 'reasoning layers' lack shared standards or benchmarks. Everyone wants to sell a 'reasoning model,' but no one agrees on what that means or how to measure it. Most corporate announcements are too polished to interrogate and their press releases are not proof of defensible innovation. Extraordinary claims need demos, user numbers and real-world metrics. When the answers are fuzzy, the claim is unproven. In today's landscape, scepticism is not cynicism. It is discipline. This means we must get comfortable with complexity. Rather than glossing over acronyms, we must dig in. Modern tech is layered with convenient abstractions that make understanding easier, but often too easy. A robo-taxi marketed as 'full self-driving' or a model labelled 'serverless' demands that we look beneath the surface. Also Read: Productivity puzzle: Solow's paradox has come to haunt AI adoption We don't need to reinvent every wheel, but a good slogan should never be an excuse for missing what is critical. The only way to understand some tools is to use them. A new AI research assistant, for instance, only feels distinct after you use it, not when you read a review of what it can or cannot accomplish. In this environment, looking to the past or gazing towards the distant future is a fool's errand. History proves everything and nothing. You can cherry-pick the dot-com bust or the advent of electricity to support any view. It's better to study what just happened than to force-fit it into a chart of inevitability. The experience of the past two years has shattered most comfortable assumptions about AI, compute and software design. The infographics about AI diffusion or compute intensity that go viral on the internet often come from people who study history more than they study the present. It's easier to quote a business guru than to parse a new AI framework, but we must do the hard thing: analyse present developments with an open mind even when the vocabulary doesn't yet exist. Also Read: Colleagues or overlords? The debate over AI bots has been raging but needn't The new 'Nostradami' of artificial intelligence: This brings us to the new cottage industry of AI soothsaying. Over the past two years, a fresh crop of 'laws' has strutted across conference stages and op-eds, each presented as the long-awaited Rosetta Stone of AI. We're told to obey Scaling Law (just add more data), respect Chinchilla Law (actually, add exactly 20 times more tokens) and reflect on the reanimated Solow Paradox (productivity still yawns, therefore chatbots are overrated). When forecasts miss the mark, pundits invoke Goodhart's Law (metrics have stopped mattering) or Amara's Law (overhype now, under-hype later). The Bitter Lesson tells us to buy GPUs (graphic processing units), not PhDs. Cunningham's Law says wrong answers attract better ones. Our favourite was when the Victorian-era Jevons' Paradox was invoked to argue that a recent breakthrough wouldn't collapse GPU demand. We're not immune to this temptation and have our own Super-Moore Law; it has yet to go viral. Also Read: AI as infrastructure: India must develop the right tech These laws and catchphrases obscure more than they reveal. The 'AI' of today bears little resemblance to what the phrase meant in the 1950s or even late 2022. The term 'transformer," the architecture that kicked off the modern AI boom, is a prime example. Its original 2017 equation exists now only in outline. The working internals of today's models—with flash attention, rotary embeddings and mixture-of-experts gating—have reshaped the original methods so thoroughly that the resulting equations resemble the original less than general relativity resembles Newton's laws. This linguistic mismatch will only worsen as robotics grafts cognition onto actuators and genomics borrows AI architecture for DNA editing. Our vocabulary, built for a slower era, struggles to keep up. Also Read: Rahul Matthan: AI models aren't copycats but learners just like us Beneath the noise, a paradox remains: staying genuinely current is both exceedingly difficult and easier than ever. It's difficult because terminology changes weekly and breakthroughs appear on preprint servers, not in peer-reviewed journals. However, it's easier because we now have AI tools that can process vast amounts of information, summarize dense research and identify core insights with remarkable precision. Used well, these technologies can become the most effective way to understand technology itself. And that's how sensible investment in innovation begins: with a genuine grasp of what's being invested in. The author is a Singapore-based innovation investor for GenInnov Pte Ltd
Hindustan Times
5 hours ago
- Hindustan Times
Women, be careful: 7 types of photos to avoid that can easily be ‘undressed' by AI dress changing apps
Be careful about what kind of photos you're putting out there. A full-length shot in a sharp business suit on LinkedIn can be turned into a bikini pic in seconds — no skill, no money, just one AI app that pops up on the first page of Google Search. The scary part is that these apps are now getting better in terms of accuracy. All it takes is a screenshot of one solo, full-body image. The threat is real. But there are ways to prevent your public photos from being misused by AI dress changing apps. (Photo generated by Google Gemini) Across India, women are discovering their social media photos are being misused by AI-powered 'dress changing' tools that digitally strip clothes and create fake nudes. No consent. No clue. Just a clear image and a screenshot. These apps are floating around on Telegram channels, and the dark web. Many free AI dress changing apps that 'gets-the-job-done' can be accessed through a simple Google Search for free. And they're not going after celebrities. They're targeting everyday women: students, professionals, influencers, even homemakers. Why blame just shady apps? Google has announced its own AI dressing changing app called 'Doppl'. This app is currently available in the US and does not support try-ons for shoes, swimwear, lingerie, or accessories, yet. All it takes is a screenshot of one solo, full-body image. That's enough for the algorithm to generate a fake lingerie or nude version that can look realistic, without the woman ever knowing. The threat is real. But there are ways to prevent your public photos from being misused. There are 7 common photo types these AI tools rely on — avoid them, and you make their job a hell of a lot harder for perverts. 7 types of photos you should avoid posting If you want to stay active on social media but reduce your risk, avoid uploading these photo styles: 1. Full-length, solo shots AI tools work best on clear, front-facing, full-body pictures — especially when you're alone in the frame. Stick to waist-up or group shots when possible. 2. Single-layer or body-hugging clothes Tight outfits or visible body outlines make it easier for AI to guess what's underneath. Layered clothing like jackets, dupattas, or scarves are harder to fake. 3. Photos with plain backgrounds Blank walls or clean backdrops are ideal for AI detection. Instead, use natural settings like streets, parks, or indoor scenes with visible objects. 4. Unedited high-resolution images Crystal-clear, high-res images give AI more data to process. Slightly reduce resolution or apply filters before posting. 5. Photos without watermarks A small watermark, even a light diagonal one with your name or username, can confuse AI and lower the chances of misuse. 6. Straight-on poses with arms by the side Straight posture with arms by your side makes it easy for AI to outline your figure. Natural poses — crossing your arms, holding a bag, or turning sideways — make manipulation harder. 7. Similar photos posted frequently Posting the same type of pose or angle often gives AI more data to learn from. Vary your photo style and frequency. Why AI dress-changing apps are dangerous These tools use machine learning to scan a photo, detect the body outline under clothes, and generate a manipulated version of the image — sometimes changing traditional Indian outfits into revealing bikinis, lingerie, or outright fake nudes. Unlike Photoshop, these apps don't need skill. All they need is the right kind of photo — which is where your caution comes in. What can Indian users do to keep a check India has one of the largest internet user bases, but awareness about image misuse is still low. Here's how you can stay ahead:
Time of India
6 hours ago
- Time of India
Bengaluru techie's brother joins Google like him. ‘Super proud' sibling moment melts the internet
In a world where landing a job at Google is considered a dream come true for many, two Indian brothers just turned that dream into a shared family milestone. Software engineer Priyansh Agarwal, who works at Google's Bengaluru office , recently took to social media to celebrate his younger brother Priyam Agarwal's new beginning at the same tech giant . The emotional exchange between the siblings has captured the hearts of thousands online—and for good reason. A Screenshot of a Dream It all began when Delhi-based Priyam posted a screenshot of his Google onboarding portal on July 5, revealing that he was just days away from starting his new role as a software engineer. 'Less than 10 days before I start a new journey. Super excited and a little nervous,' he captioned the post on X (formerly Twitter). The image showed the countdown ticking down to his first day at one of the most competitive and sought-after workplaces in the tech world. But the post didn't just mark the beginning of Priyam's journey. It also sparked an emotional reaction from his elder brother, Priyansh, who reposted the announcement with pride swelling through every word. 'Younger brother coming to Google as well. Super proud of him,' he wrote, showing the world not just professional pride, but the quiet joy of witnessing a sibling's success. — Priyansh_31Dec (@Priyansh_31Dec) Sibling Goals, Delivered It didn't take long for Priyam to respond to his brother's public shoutout, and his words melted many hearts online. 'The elder brother everyone wishes they had. Always had my back,' he wrote in the comments, sparking a wave of admiration across the platform. The bond between the two brothers resonated with countless users. While some simply extended their congratulations, others expressed how deeply the moment moved them. One user wrote, 'Do bhai bhai dono tabahi,' highlighting the duo's power-packed potential, while another cheered, 'Ohh lesssgooo! This is amazing! God bless.' MORE STORIES FOR YOU ✕ « Back to recommendation stories I don't want to see these stories because They are not relevant to me They disrupt the reading flow Others SUBMIT — PriyamA_18 (@PriyamA_18) A Rare Feat Worth Celebrating Getting hired at Google is no small feat. With a notoriously selective hiring process and rigorous interviews, the probability of two siblings making it into the company independently is extraordinary. But the Agarwal brothers have shown that with determination, mutual support, and a shared vision, even the most ambitious goals can be achieved—together.
