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OpenAI’s ChatGPT Instant Checkout: The Dawn of Conversational Commerce CX

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How OpenAI’s new shopping feature will fundamentally reshape customer experience expectations in ecommerce and retail.

The Gist

  • Instant Checkout transforms ChatGPT into a commerce platform. Users can now buy directly from Etsy and over one million Shopify merchants without leaving a conversation—collapsing the traditional ecommerce journey into a chat-to-checkout experience.
  • Frictionless buying raises new CX expectations. Merchants retain order control, but customers will expect conversational ease across all post-purchase support channels.
  • Agentic commerce reshapes trust and transparency. As AI gains more autonomy in purchasing, CX leaders must redefine safeguards, metrics, and relationship ownership in a world where experience becomes inseparable from conversation.

Since its November 2022 launch, ChatGPT has become synonymous with general AI capabilities. Now OpenAI is extending its influence toward a new frontier: ecommerce.

OpenAI has officially transformed ChatGPT from a discovery tool into a complete commerce platform with the launch of Instant Checkout, powered by the Agentic Commerce Protocol developed in partnership with Stripe. Starting with U.S. Etsy sellers and expanding soon to over one million Shopify merchants, including Glossier, SKIMS, Spanx, and Vuori, this development represents more than just another checkout option.

It is a fundamental reimagining of the customer experience journey that could have been implications on the customer experience industry.

Customer experience professionals in e-commerce and retail recognize OpenAI’s entry as a signal that the entire paradigm of how customers discover and purchase products is shifting toward agentic commerce — online shopping managed with AI.

Table of Contents

What OpenAI Is Offering: Instant Checkout Explained

Instant Checkout enables ChatGPT users to complete purchases without leaving the conversational interface. When someone asks a shopping-related question—”best running shoes under $100″ or “gifts for a ceramics lover”—ChatGPT displays relevant products from across the web. For items where Instant Checkout is enabled, users see a “Buy” button that lets them complete the entire transaction within the chat.

How Instant Checkout Collapses the Ecommerce Journey

The technical foundation is the Agentic Commerce Protocol, an open-source standard co-developed with Stripe that OpenAI is making available to any merchant or developer. This protocol creates a secure payment framework where ChatGPT acts as the user’s AI agent, passing information between customer and merchant while the merchant retains full control as the merchant of record. Merchants handle orders, process payments through their existing systems (Stripe or otherwise), manage fulfilment and own the customer relationship post-purchase.

Currently supporting single-item purchases for U.S. users of the Plus, Pro, and Free tiers, OpenAI plans to expand to multi-item carts and additional regions. The company emphasizes that product recommendations are organic and unsponsored, ranked purely by relevance, with merchants paying a small transaction fee on completed purchases. For customers, the service is free and doesn’t affect product prices. ChatGPT Plus and Pro subscribers can leverage saved payment methods and shipping details for even faster checkout, though all users must explicitly confirm each step before purchase.

This represents OpenAI’s first major move toward what they call “agentic commerce”—a platform where AI doesn’t just help you find products but actively facilitates purchasing them on your behalf, with the long-term vision of more autonomous shopping experiences.

The Friction-Free Promise: What Changes for CX

Ecommerce has long been a goal of every digital platform, from the leaders of internet browsers to social media platforms. Yet the customer journey of most ecommerce attempts often includes friction points for customers to complete a purchase: multiple browser tabs and re-entering payment information, all while having users create an account, can lead to abandoned carts.

Many experts had hoped social commerce – retail through social media – would minimize the friction points. The volume of US social commerce did rise, especially during the COVID-19 pandemic. The rise of direct-to-customer retail placed a spotlight on aligning click-through behaviour and sales, creating high interest in a cart checkout with just a few clicks.

OpenAI’s launch of Instant Checkout approaches a speedy checkout with a “chat to checkout in just a few taps.”

How Does Instant Checkout Work?

Here’s how Instant Checkout works: A customer asks ChatGPT for “gifts for a ceramics lover,” receives curated product recommendations, sees a “Buy” button on items with Instant Checkout enabled, and completes the purchase without ever leaving the conversation. For ChatGPT Plus and Pro subscribers, the platform can prefill shipping and payment details, making the experience even more seamless.

This level of convenience raises the digital customer experience bar significantly.

The Rise of Conversational Shopping Behaviour

If customers can complete a purchase in seconds through conversational AI, they’ll increasingly expect similarly frictionless experiences everywhere else. Retailers who maintain clunky checkout processes will feel the comparison acutely.

The Trust Equation: Transparency in a Black Box

One of the most significant customer experience implications involves trust and transparency. OpenAI emphasizes that product results are “organic and unsponsored, ranked purely on relevance to the user,” and that Instant Checkout availability doesn’t influence product rankings. When multiple merchants sell the same product, ChatGPT considers availability, price, quality, primary seller status and Instant Checkout availability to optimize user experience.

One potential shift for customers is the kinds of trust signals to look while shopping online.

New Trust Signals in an AI-Led Environment

Customers have spent years learning which search results, sponsored placements and algorithmic recommendations to trust. They know when they’re being marketed to. Conversational AI collapses those visual cues. There’s no “Ad” label or comparison shopping pages, verifying that you’re seeing the best options.

For CX professionals, this creates a paradox. The experience feels more personal and helpful—like getting advice from a knowledgeable friend—but the mechanisms driving recommendations remain opaque. OpenAI’s commitment to relevance-based ranking is important, but maintaining customer trust will require ongoing transparency about how these decisions are made.

Merchants as Merchants of Record: Preserving Relationship Ownership

Unlike marketplace models where the platform intermediates the customer relationship, OpenAI positions itself as the customer’s “AI agent—securely passing information between user and merchant, just like a digital personal shopper would.” Merchants remain the merchant of record, handling orders, payments, fulfillment and customer support through their existing systems.

This architectural choice has profound CX implications. When issues arise—damaged goods, shipping delays, return requests—customers must navigate the merchant’s existing support infrastructure. They can’t simply resolve everything in ChatGPT. OpenAI explicitly states that “merchants use your order information to complete the order, but OpenAI asks merchants to not sign users up for marketing emails from their ChatGPT orders.”

This creates a potential friction point.

When the Chat Becomes the Customer Support Channel

Customers who complete purchases in a conversational environment may expect conversational support. They’ll ask ChatGPT about order status, return policies or replacement requests. While ChatGPT can surface information, the actual resolution still requires engaging with the merchant directly.

For retailers, this means your post-purchase CX needs to match the seamlessness of the purchasing experience. If ChatGPT makes buying easy but your support remains difficult, the disconnect will be glaring.

The Context Advantage: Memory and Personalization

ChatGPT’s existing features—Memory, Custom Instructions and conversation history—create opportunities for deeply personalized commerce experiences. The platform can remember that you prefer sustainable products, have a specific budget range, or are shopping for someone with particular interests.

Memory as the Engine of Relationship Commerce

This contextual awareness enables product recommendations that feel genuinely helpful rather than algorithmically generic.

For customer experience strategy, this represents a shift from session-based commerce to persistent relationship commerce. Instead of starting fresh with each visit, customers maintain an ongoing dialogue where preferences, constraints and needs are already understood. It’s the digital equivalent of shopping with a personal stylist who remembers your taste, size and budget.

However, this also requires rethinking privacy and consent. OpenAI notes that “to respond to your shopping question, ChatGPT uses your query and available context (such as Memory or Custom instructions).” Customers may not fully grasp how much information they’re sharing through casual conversation or how it’s being used to shape recommendations.

Multi-Item Carts and the Future of Agentic Commerce

Currently, Instant Checkout supports single-item purchases only. OpenAI plans to add multi-item carts and expand merchant and regional availability. But the real customer experience transformation lies in what OpenAI calls “agentic commerce”—where AI doesn’t just help you find what to buy but actually makes purchases on your behalf.

Imagine asking ChatGPT to “stock my pantry with staples I usually buy” or “replace my worn-out workout clothes with similar items” and having it autonomously complete those purchases based on your preferences, budget and past behaviour.

AI Autonomy and the Next Phase of Agentic Commerce

OpenAI emphasizes that “users stay in control—they explicitly confirm each step before any action is taken,” but it’s easy to see how this could evolve toward greater autonomy.

From a CX perspective, this promises ultimate convenience but introduces new anxieties. What happens when the AI makes a wrong assumption? How do you dispute an order you didn’t manually approve? What safeguards prevent accidental purchases during casual conversation? These aren’t theoretical concerns—they’re fundamental customer experience challenges that will need addressing as agentic commerce matures.

The Discovery-to-Purchase Continuum Collapses

Traditional ecommerce has maintained a clear separation between discovery (search engines, social media, content sites) and purchase (retailer websites, marketplaces). ChatGPT collapses this continuum entirely. The same conversation that starts with “how do I decorate a small apartment” can seamlessly transition to purchasing specific furniture pieces without the customer ever consciously entering “shopping mode.”

This fluidity creates immense convenience but also removes traditional decision-making waypoints. In conventional ecommerce, the journey from discovery to checkout includes multiple opportunities for price comparison, reading reviews and specification verification.

Discovery, Purchase and Confidence in One Flow

Conversational commerce compresses these steps, potentially reducing buyer confidence even as it increases convenience.

Savvy retailers will need to ensure their product information, reviews and trust signals are accessible within conversational contexts. If ChatGPT recommends your product, customers should still be able to access detailed specifications quickly, customer reviews, return policies and other information that builds purchase confidence.

Six Strategic Imperatives for Retail CX Leaders

Actions ecommerce and CX professionals can take to prepare for conversational commerce.

Action Recommendation
Prepare for conversational commerce expectations Even customers who never use ChatGPT shopping will expect its convenience. Streamline your checkout to minimize steps between discovery and purchase.
Ensure your product data is AI-ready ChatGPT relies on structured data—pricing, inventory, and descriptions—to recommend accurately. Optimize catalogues for AI parsing, not just human browsing.
Strengthen post-purchase CX Make order tracking, returns, and support as effortless as buying through chat. Consider adding conversational AI support on your own channels.
Maintain transparent pricing and policies AI shoppers may buy without visiting your site. Ensure your product feeds include clear pricing, shipping, and return data to prevent confusion.
Rethink customer acquisition costs OpenAI’s per-transaction fees shift focus from ad-driven discovery to conversion-based models. Re-evaluate your acquisition and retention ROI.
Plan for autonomous shopping Prepare for AI-driven, recurring purchases where customer oversight decreases. Define safeguards, limits, and opt-ins to maintain control and trust.

The Larger Context: Commerce at the Conversation Layer

OpenAI’s move follows a broader trend of commerce functionality migrating to conversational interfaces powered by AI. Meta has been experimenting with business messaging on WhatsApp and Instagram. Google has integrated shopping into search results, hoping to further leverage its AI Overview integration with its search engine.

But OpenAI’s approach—combining product discovery, recommendation and checkout entirely within a conversational AI interface—represents the most complete implementation yet. OpenAI’s decision to open-source the Agentic Commerce Protocol suggests ecosystem ambitions.

Commerce at the Conversation Layer

By creating a standard that works across AI platforms and payment processors, OpenAI is positioning conversational commerce as infrastructure, not just a ChatGPT feature. Marketing professionals must monitor adoption of conversational commerce as an element of marketing strategies and campaigns.

Moreover, competitors who are still finding their AI strategy will see the Agentic Commerce Protocol as a significant competitor. Amazon, for example, has long offered shopping capabilities with Alexa. But partners in the Alexa ecosystem may move toward Open AI if Amazon does not launch a similar AI protocol for Alexa.

 

An orange infographic showing a bridge connecting “Fragmented Shopping” on the left—representing disconnected discovery and purchase experiences—to “Seamless Commerce” on the right, illustrating unified, personalized and convenient shopping through AI-powered conversational commerce.
An AI-driven bridge is forming between fragmented shopping journeys and seamless, personalized commerce as retailers embrace conversational AI experiences.Simpler Media Group

 

Measuring Success in Conversational Commerce CX

Traditional ecommerce metrics—bounce rate, cart abandonment, time on site—don’t translate cleanly to conversational commerce.

Metrics That Redefine Success in Conversational Commerce

New ways to measure engagement, conversion and satisfaction when shopping happens inside AI conversations.

Metric Definition
Recommendation acceptance rate Percentage of purchases made from ChatGPT’s initial suggestions versus alternatives.
Conversational conversion Ratio of shopping-related prompts that end in a completed transaction.
Repurchase through conversation Share of customers returning to ChatGPT for repeat or follow-up purchases.
Post-purchase satisfaction Customer-reported satisfaction after buying through ChatGPT, including fulfilment and support quality.
Preference drift How accurately ChatGPT adapts to a customer’s evolving preferences and feedback over time.

These metrics will help retailers understand whether conversational commerce delivers genuine CX improvements or simply novelty-driven early adoption.

The Questions That Remain

OpenAI’s Instant Checkout raises as many customer experience questions as it answers:

How will product returns work when the purchase was made conversationally? Can customers modify orders placed through ChatGPT? What happens when products are out of stock after ChatGPT recommends them? How do subscription services and recurring purchases translate to conversational commerce? What safeguards prevent accidental purchases during ambiguous conversations?

These implementation details are fundamental to whether conversational commerce is a fit for the seamless customer experience being sought. Marketers should consider whether the answers mean achieving the promised experiences or are an indicator of implementation frustrations.

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Sourced from CMSWIRE

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Let’s not make the same mistakes with AI that we made with social media

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Nathan E. Sanders archive page& Bruce Schneier

Social media’s unregulated evolution over the past decade holds a lot of lessons that apply directly to AI companies and technologies.

Oh, how the mighty have fallen. A decade ago, social media was celebrated for sparking democratic uprisings in the Arab world and beyond. Now front pages are splashed with stories of social platforms’ role in misinformation, business conspiracy, malfeasance, and risks to mental health. In a 2022 survey, Americans blamed social media for the coarsening of our political discourse, the spread of misinformation, and the increase in partisan polarization.

Today, tech’s darling is artificial intelligence. Like social media, it has the potential to change the world in many ways, some favourable to democracy. But at the same time, it has the potential to do incredible damage to society.

There is a lot we can learn about social media’s unregulated evolution over the past decade that directly applies to AI companies and technologies. These lessons can help us avoid making the same mistakes with AI that we did with social media.

In particular, five fundamental attributes of social media have harmed society. AI also has those attributes. Note that they are not intrinsically evil. They are all double-edged swords, with the potential to do either good or ill. The danger comes from who wields the sword, and in what direction it is swung. This has been true for social media, and it will similarly hold true for AI. In both cases, the solution lies in limits on the technology’s use.

#1: Advertising

The role advertising plays in the internet arose more by accident than anything else. When commercialization first came to the internet, there was no easy way for users to make micropayments to do things like viewing a web page. Moreover, users were accustomed to free access and wouldn’t accept subscription models for services. Advertising was the obvious business model, if never the best one. And it’s the model that social media also relies on, which leads it to prioritize engagement over anything else.

Both Google and Facebook believe that AI will help them keep their stranglehold on an 11-figure online ad market (yep, 11 figures), and the tech giants that are traditionally less dependent on advertising, like Microsoft and Amazon, believe that AI will help them seize a bigger piece of that market.

Big Tech needs something to persuade advertisers to keep spending on their platforms. Despite bombastic claims about the effectiveness of targeted marketing, researchers have long struggled to demonstrate where and when online ads really have an impact. When major brands like Uber and Procter & Gamble recently slashed their digital ad spending by the hundreds of millions, they proclaimed that it made no dent at all in their sales.

AI-powered ads, industry leaders say, will be much better. Google assures you that AI can tweak your ad copy in response to what users search for, and that its AI algorithms will configure your campaigns to maximize success. Amazon wants you to use its image generation AI to make your toaster product pages look cooler. And IBM is confident its Watson AI will make your ads better.

These techniques border on the manipulative, but the biggest risk to users comes from advertising within AI chatbots. Just as Google and Meta embed ads in your search results and feeds, AI companies will be pressured to embed ads in conversations. And because those conversations will be relational and human-like, they could be more damaging. While many of us have gotten pretty good at scrolling past the ads in Amazon and Google results pages, it will be much harder to determine whether an AI chatbot is mentioning a product because it’s a good answer to your question or because the AI developer got a kickback from the manufacturer.

#2: Surveillance

Social media’s reliance on advertising as the primary way to monetize websites led to personalization, which led to ever-increasing surveillance. To convince advertisers that social platforms can tweak ads to be maximally appealing to individual people, the platforms must demonstrate that they can collect as much information about those people as possible.

It’s hard to exaggerate how much spying is going on. A recent analysis by Consumer Reports about Facebook—just Facebook—showed that every user has more than 2,200 different companies spying on their web activities on its behalf.

AI-powered platforms that are supported by advertisers will face all the same perverse and powerful market incentives that social platforms do. It’s easy to imagine that a chatbot operator could charge a premium if it were able to claim that its chatbot could target users on the basis of their location, preference data, or past chat history and persuade them to buy products.

The possibility of manipulation is only going to get greater as we rely on AI for personal services. One of the promises of generative AI is the prospect of creating a personal digital assistant advanced enough to act as your advocate with others and as a butler to you. This requires more intimacy than you have with your search engine, email provider, cloud storage system, or phone. You’re going to want it with you constantly, and to most effectively work on your behalf, it will need to know everything about you. It will act as a friend, and you are likely to treat it as such, mistakenly trusting its discretion.

Even if you choose not to willingly acquaint an AI assistant with your lifestyle and preferences, AI technology may make it easier for companies to learn about you. Early demonstrations illustrate how chatbots can be used to surreptitiously extract personal data by asking you mundane questions. And with chatbots increasingly being integrated with everything from customer service systems to basic search interfaces on websites, exposure to this kind of inferential data harvesting may become unavoidable.

#3: Virality

Social media allows any user to express any idea with the potential for instantaneous global reach. A great public speaker standing on a soapbox can spread ideas to maybe a few hundred people on a good night. A kid with the right amount of snark on Facebook can reach a few hundred million people within a few minutes.

A decade ago, technologists hoped this sort of virality would bring people together and guarantee access to suppressed truths. But as a structural matter, it is in a social network’s interest to show you the things you are most likely to click on and share, and the things that will keep you on the platform.

As it happens, this often means outrageous, lurid, and triggering content. Researchers have found that content expressing maximal animosity toward political opponents gets the most engagement on Facebook and Twitter. And this incentive for outrage drives and rewards misinformation.

As Jonathan Swift once wrote, “Falsehood flies, and the Truth comes limping after it.” Academics seem to have proved this in the case of social media; people are more likely to share false information—perhaps because it seems more novel and surprising. And unfortunately, this kind of viral misinformation has been pervasive.

AI has the potential to supercharge the problem because it makes content production and propagation easier, faster, and more automatic. Generative AI tools can fabricate unending numbers of falsehoods about any individual or theme, some of which go viral. And those lies could be propelled by social accounts controlled by AI bots, which can share and launder the original misinformation at any scale.

Remarkably powerful AI text generators and autonomous agents are already starting to make their presence felt in social media. In July, researchers at Indiana University revealed a botnet of more than 1,100 Twitter accounts that appeared to be operated using ChatGPT.

AI will help reinforce viral content that emerges from social media. It will be able to create websites and web content, user reviews, and smartphone apps. It will be able to simulate thousands, or even millions, of fake personas to give the mistaken impression that an idea, or a political position, or use of a product, is more common than it really is. What we might perceive to be vibrant political debate could be bots talking to bots. And these capabilities won’t be available just to those with money and power; the AI tools necessary for all of this will be easily available to us all.

#4: Lock-in

Social media companies spend a lot of effort making it hard for you to leave their platforms. It’s not just that you’ll miss out on conversations with your friends. They make it hard for you to take your saved data—connections, posts, photos—and port it to another platform. Every moment you invest in sharing a memory, reaching out to an acquaintance, or curating your follows on a social platform adds a brick to the wall you’d have to climb over to go to another platform.

This concept of lock-in isn’t unique to social media. Microsoft cultivated proprietary document formats for years to keep you using its flagship Office product. Your music service or e-book reader makes it hard for you to take the content you purchased to a rival service or reader. And if you switch from an iPhone to an Android device, your friends might mock you for sending text messages in green bubbles. But social media takes this to a new level. No matter how bad it is, it’s very hard to leave Facebook if all your friends are there. Coordinating everyone to leave for a new platform is impossibly hard, so no one does.

Similarly, companies creating AI-powered personal digital assistants will make it hard for users to transfer that personalization to another AI. If AI personal assistants succeed in becoming massively useful time-savers, it will be because they know the ins and outs of your life as well as a good human assistant; would you want to give that up to make a fresh start on another company’s service? In extreme examples, some people have formed close, perhaps even familial, bonds with AI chatbots. If you think of your AI as a friend or therapist, that can be a powerful form of lock-in.

Lock-in is an important concern because it results in products and services that are less responsive to customer demand. The harder it is for you to switch to a competitor, the more poorly a company can treat you. Absent any way to force interoperability, AI companies have less incentive to innovate in features or compete on price, and fewer qualms about engaging in surveillance or other bad behaviours.

#5: Monopolization

Social platforms often start off as great products, truly useful and revelatory for their consumers, before they eventually start monetizing and exploiting those users for the benefit of their business customers. Then the platforms claw back the value for themselves, turning their products into truly miserable experiences for everyone. This is a cycle that Cory Doctorow has powerfully written about and traced through the history of Facebook, Twitter, and more recently TikTok.

The reason for these outcomes is structural. The network effects of tech platforms push a few firms to become dominant, and lock-in ensures their continued dominance. The incentives in the tech sector are so spectacularly, blindingly powerful that they have enabled six megacorporation’s (Amazon, Apple, Google, Facebook parent Meta, Microsoft, and Nvidia) to command a trillion dollars each of market value—or more. These firms use their wealth to block any meaningful legislation that would curtail their power. And they sometimes collude with each other to grow yet fatter.

This cycle is clearly starting to repeat itself in AI. Look no further than the industry poster child OpenAI, whose leading offering, ChatGPT, continues to set marks for uptake and usage. Within a year of the product’s launch, OpenAI’s valuation had skyrocketed to about $90 billion.

OpenAI once seemed like an “open” alternative to the megacorps—a common carrier for AI services with a socially oriented nonprofit mission. But the Sam Altman firing-and-rehiring debacle at the end of 2023, and Microsoft’s central role in restoring Altman to the CEO seat, simply illustrated how venture funding from the familiar ranks of the tech elite pervades and controls corporate AI. In January 2024, OpenAI took a big step toward monetization of this user base by introducing its GPT Store, wherein one OpenAI customer can charge another for the use of its custom versions of OpenAI software; OpenAI, of course, collects revenue from both parties. This sets in motion the very cycle Doctorow warns about.

In the middle of this spiral of exploitation, little or no regard is paid to externalities visited upon the greater public—people who aren’t even using the platforms. Even after society has wrestled with their ill effects for years, the monopolistic social networks have virtually no incentive to control their products’ environmental impact, tendency to spread misinformation, or pernicious effects on mental health. And the government has applied virtually no regulation toward those ends.

Likewise, few or no guardrails are in place to limit the potential negative impact of AI. Facial recognition software that amounts to racial profiling, simulated public opinions supercharged by chatbots, fake videos in political ads—all of it persists in a legal grey area. Even clear violators of campaign advertising law might, some think, be let off the hook if they simply do it with AI.

Mitigating the risks

The risks that AI poses to society are strikingly familiar, but there is one big difference: it’s not too late. This time, we know it’s all coming. Fresh off our experience with the harms wrought by social media, we have all the warning we should need to avoid the same mistakes.

The biggest mistake we made with social media was leaving it as an unregulated space. Even now—after all the studies and revelations of social media’s negative effects on kids and mental health, after Cambridge Analytica, after the exposure of Russian intervention in our politics, after everything else—social media in the US remains largely an unregulated “weapon of mass destruction.” Congress will take millions of dollars in contributions from Big Tech, and legislators will even invest millions of their own dollars with those firms, but passing laws that limit or penalize their behaviour seems to be a bridge too far.

We can’t afford to do the same thing with AI, because the stakes are even higher. The harm social media can do stems from how it affects our communication. AI will affect us in the same ways and many more besides. If Big Tech’s trajectory is any signal, AI tools will increasingly be involved in how we learn and how we express our thoughts. But these tools will also influence how we schedule our daily activities, how we design products, how we write laws, and even how we diagnose diseases. The expansive role of these technologies in our daily lives gives for-profit corporations opportunities to exert control over more aspects of society, and that exposes us to the risks arising from their incentives and decisions.

The good news is that we have a whole category of tools to modulate the risk that corporate actions pose for our lives, starting with regulation. Regulations can come in the form of restrictions on activity, such as limitations on what kinds of businesses and products are allowed to incorporate AI tools. They can come in the form of transparency rules, requiring disclosure of what data sets are used to train AI models or what new preproduction-phase models are being trained. And they can come in the form of oversight and accountability requirements, allowing for civil penalties in cases where companies disregard the rules.

The single biggest point of leverage governments have when it comes to tech companies is antitrust law. Despite what many lobbyists want you to think, one of the primary roles of regulation is to preserve competition—not to make life harder for businesses. It is not inevitable for OpenAI to become another Meta, an 800-pound gorilla whose user base and reach are several times those of its competitors. In addition to strengthening and enforcing antitrust law, we can introduce regulation that supports competition-enabling standards specific to the technology sector, such as data portability and device interoperability. This is another core strategy for resisting monopoly and corporate control.

Additionally, governments can enforce existing regulations on advertising. Just as the US regulates what media can and cannot host advertisements for sensitive products like cigarettes, and just as many other jurisdictions exercise strict control over the time and manner of politically sensitive advertising, so too could the US limit the engagement between AI providers and advertisers.

Lastly, we should recognize that developing and providing AI tools does not have to be the sovereign domain of corporations. We, the people and our government, can do this too. The proliferation of open-source AI development in 2023, successful to an extent that startled corporate players, is proof of this. And we can go further, calling on our government to build public-option AI tools developed with political oversight and accountability under our democratic system, where the dictatorship of the profit motive does not apply.

Which of these solutions is most practical, most important, or most urgently needed is up for debate. We should have a vibrant societal dialogue about whether and how to use each of these tools. There are lots of paths to a good outcome.

The problem is that this isn’t happening now, particularly in the US. And with a looming presidential election, conflict spreading alarmingly across Asia and Europe, and a global climate crisis, it’s easy to imagine that we won’t get our arms around AI any faster than we have (not) with social media. But it’s not too late. These are still the early years for practical consumer AI applications. We must and can do better.

Feature Image Credit: STEPHANIE ARNETT/MITTR | GETTY, ENVATO

Nathan E. Sanders archive page& Bruce Schneier

Nathan E. Sanders is a data scientist and an affiliate with the Berkman Klein Center at Harvard University. Bruce Schneier is a security technologist and a fellow and lecturer at the Harvard Kennedy School.

Sourced from MIT Technology Review

 

 

Nathan E. Sanders archive page& Bruce Schneierarchive page

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5 Potential Impacts Of Generative AI Within The Enterprise Sector

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By Gary Fowler

It’s been almost six months since OpenAI dropped the ChatGPT bomb and effectively reinvigorated AI adoption across a multitude of verticals, sectors, industries and users. For the first time in years, we are seeing a large-scale commercial adoption of generative AI technology—and new applications of LLMO models are introduced by the day.

While individual uses have comprised some of the most common examples of how AI has been transforming our day-to-day workflows, the enterprise-level application of the technology is relatively overlooked, even though it holds immense potential.

Due to transformative strides on an individual consumer level, AU holds immense promise on an enterprise growth and process level—from enhancing creativity and productivity to streamlining business processes and decision making, generative AI can reform how organizations operate today.

In this article, I will explore five potential generative AI use cases for the enterprise sector which stand to unlock new opportunities and help drive innovation.

1. Creative Innovation And Branding

Generative AI opens new doors for enterprises to reach new levels of creativity and innovation—from branding and marketing efforts to content creation and internal communication or data visualization. With the help of such tools as MidJourney, Stable Diffusion, Dall-E and ChatGPT, among others, companies can produce a high-quality and high volume of content, both visual and textual.

Leaders should seek solutions that allow the brand to push boundaries when it comes to branding, brand storytelling, content creation (photo, video, text), A/B testing, blog writing, headline generation and a variety of other assets that can help push the boundaries of any brand’s online and offline presence.

Generative AI is also a powerful lever to pull for user feedback, persona development, value proposition exploration and developing customized marketing campaigns and experiences that stem directly from customer needs and asks.

2. Decisions And Predictions Based Upon Deeper Foresight

Forecasting, future-proofing and planning are the cornerstones of successful enterprise management. In other words, leaders must constantly strive for efficient and sustainable growth built upon data-driven and well-informed decision making.

Generative AI offers a significant boost in this area by providing organizations with advanced data analysis and predictive analytics capabilities. By analysing large volumes of structured and unstructured data, generative AI can provide immediate and accurate insights, aiding decision makers in formulating strategies, optimizing processes and predicting industry trends.

Generative AI isn’t just a powerful source of predictive analytics. Its progressive capabilities also include simulations of potential scenarios if provided with all variables to account for—which creates a clear scenario to aid in data-driven decision making, risk mitigation and opportunity discovery.

3. Streamlined Workflows, Operations And Processes

Generative AI’s ability to analyse data and identify patterns stands as a powerful solution for optimizing workflows, identifying inefficiencies and building processes that are significantly more streamlined and automated. The application of this includes but is not limited to supply chain management, resource allocation and workflow automation—with generative AI transforming labour-intensive tasks into efficient and accurate processes.

In other words, generative AI is also able to assume more administrative or time-consuming, repetitive tasks that would otherwise prove to be a waste of time for employees who strive to drive more impact and plan strategically on a managerial level. This can not only save time and reduce costs but also enhance overall productivity, enabling employees to focus on more strategic and value-driven/high-involvement activities.

4. Personalized Customer Experiences

Building on the major branding, storytelling and marketing benefits of generative AI, the technology also opens doors to new ways of delivering exceptional customer experiences. Generative AI provides brands and organizations with the tools to personalize their interactions with customers on a whole new level—from more powerful chatbots that proactively react to the customer needs to providing unique personalized recommendations to customers based on their preferences and previous activity.

By analysing large amounts of customer data, generative AI can generate individualized product/service recommendations, highly targeted ads and customized user interfaces fit for every user’s personal activity patterns. This is a direct path to higher customer satisfaction, loyalty and engagement while ensuring higher consistency in conversions and bottom-line impact.

5. Accelerated Research And Development

Research and development (R&D) is largely where innovation happens within the enterprise sector. Improving this sector is like throwing a brand new lifeline to any enterprise business—and generative AI has the power to significantly accelerate the R&D process by assisting in the ideation, prototyping and testing phases.

The simulation capabilities generative AI offers can allow for the mapping and exploration of a variety of formulas, outcomes, products and prototypes while significantly shortening time to market and product launches. This can allow businesses to stay ahead of the competition, adapt to rapidly changing markets and deliver cutting-edge solutions to their customers.

Revolution In The Enterprise Sector

Generative AI holds tremendous potential to provide organizations with unprecedented capabilities to innovate, streamline internal and external workflows and deliver custom-tailored experiences to their customers. From empowering creative positioning and branding initiatives to improving data-backed decision making, generative AI holds the potential to drive enterprise to new heights of success.

As the enterprise sector continues to adopt generative AI, leaders must build out strategic initiatives that invest in the necessary infrastructure, talent and resources to remain ahead of the curve and leverage the technology in full capacity. Close partnerships with AI experts, data scientists and researchers are only one of the many ways to efficiently incorporate generative AI into existing business processes and systems.

Feature Image Credit: Getty

By Gary Fowler

Gary Fowler is a serial AI entrepreneur with numerous startups and an IPO. He is CEO and cofounder of GSDVS.com and Yva.ai. Read Gary Fowler’s full executive profile here.

Sourced from Forbes

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Anthropic leapfrogs OpenAI with a chatbot that can read a novel in less than a minute

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By James Vincent

Anthropic has expanded the context window of its chatbot Claude to 75,000 words — a big improvement on current models. Anthropic says it can process a whole novel in less than a minute.

An often overlooked limitation for chatbots is memory. While it’s true that the AI language models that power these systems are trained on terabytes of text, the amount these systems can process when in use — that is, the combination of input text and output, also known as their “context window” — is limited. For ChatGPT it’s around 3,000 words. There are ways to work around this, but it’s still not a huge amount of information to play with.

Now, AI startup Anthropic (founded by former OpenAI engineers) has hugely expanded the context window of its own chatbot Claude, pushing it to around 75,000 words. As the company points out in a blog post, that’s enough to process the entirety of The Great Gatsby in one go. In fact, the company tested the system by doing just this — editing a single sentence in the novel and asking Claude to spot the change. It did so in 22 seconds.

You may have noticed my imprecision in describing the length of these context windows. That’s because AI language models measure information not by number of characters or words, but in tokens; a semantic unit that doesn’t map precisely onto these familiar quantities. It makes sense when you think about it. After all, words can be long or short, and their length does not necessarily correspond to their complexity of meaning. (The longest definitions in the dictionary are often for the shortest words.) The use of “tokens” reflects this truth, and so, to be more precise: Claude’s context window can now process 100,000 tokens, up from 9,000 before. By comparison, OpenAI’s GPT-4 processes around 8,000 tokens (that’s not the standard model available in ChatGPT — you have to pay for access) while a limited-release full-fat model of GPT-4 can handle up to 32,000 tokens.

Right now, Claude’s new capacity is only available to Anthropic’s business partners, who are tapping into the chatbot via the company’s API. The pricing is also unknown, but is certain to be a significant bump. Processing more text means spending more on compute.

But the news shows AI language models’ capacity to process information is increasing, and this will certainly make these systems more useful. As Anthropic notes, it takes a human around five hours to read 75,000 words of text, but with Claude’s expanded context window, it can potentially take on the task of reading, summarizing and analyzing a long documents in a matter of minutes. (Though it doesn’t do anything about chatbots’ persistent tendency to make information up.) A bigger context window also means the system is able to hold longer conversations. One factor in chatbots going off the rails is that when their context window fills up they forget what’s been said and it’s why Bing’s chatbot is limited to 20 turns of conversation. More context equals more conversation.

Feature Image Credit: Anthropic

By James Vincent

A senior reporter who has covered AI, robotics, and more for eight years at The Verge.

Sourced from The Verge

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