Whatnot, a live shopping platform and marketplace, has closed a $225 million Series F round, more than doubling its valuation to $11.5 billion in less than 10 months.

DST Global and CapitalG co-led the financing, which brings the Los Angeles-based company’s total raised to about $968 million since its 2019 inception. Whatnot had raised $265 million in a Series E round at a nearly $5 billion valuation in January.

New investors Sequoia Capital and Alkeon Capital participated in the Series F, alongside returning backers Greycroft, Andreessen Horowitz, Avra and Bond. Other investors include Y Combinator, Lightspeed Venture Partners and Liquid 2 Ventures.

As part of the latest financing, Whatnot says it will initiate a tender offer where select current investors will buy up to $126 million worth of shares.

Funding to e-commerce startups globally so far this year totals $7.1 billion, per Crunchbase data. That compares to $11.3 billion raised by e-commerce startups globally in all of 2024. This year’s numbers are also down significantly from post-pandemic funding totals, which surged to $93 billion in 2021.

‘Retail’s new normal’

Live commerce is the combination of livestreaming and online shopping. Grant LaFontaine, co-founder and CEO of Whatnot, said in an announcement that his startup is “proving that live shopping is retail’s new normal.”

The company says more than $6 billion worth of items have been sold on its platform in 2025 so far, more than twice its total for all of 2024. Its app facilitates the buying and selling of collectibles like trading cards and toys through live video auctions. It also offers items such as clothing and sneakers. It competes with the likes of eBay, which currently does not offer a livestreaming option. It’s also a competitor to TikTok Shop.

“Whatnot brought the live shopping wave to the US, the UK, and Europe and has turned it into one of the fastest growing marketplaces of all time, Laela Sturdy, Whatnot board member and managing partner at CapitalG, Alphabet’s independent growth fund, said in a release.

The company plans to use its new funds to invest in its platform, roll out new features and “evolve” its policies. It is also accelerating its international expansion, adding to its current 900-person workforce by hiring across multiple departments.

Related query:

• Funding to E-Commerce Startups, 2025

Related Reading:

• TikTok Shop Rival Whatnot Bags $265M At $5B Valuation For Live Shopping Platform

Illustration: Dom Guzman

Amazon said on Tuesday that it plans to reduce its corporate workforce by 14,000 jobs as it seeks to reduce bureaucracy, remove layers, and invest more in its AI strategy.

By Jeff Seibert

I’ve been building products and companies my entire career — Increo, Box, Crashlytics, Twitter and now, Digits — and I’ve had the privilege of speaking with some of the sharpest minds in venture and entrepreneurship along the way.

One recent conversation with a legendary investor really crystallized for me a set of truths about startups: what success really is, why some founders thrive while others burn out, and how to navigate the inevitable chaos of building something from nothing.

Here are some of the lessons I’ve internalized from years of building, observing and learning.

Success has no finish line

In the startup world, we talk a lot about IPOs, acquisitions and valuations. But those are milestones, not destinations.

The companies that endure don’t “win” and stop — they keep creating, adapting and pushing forward. They’re playing an infinite game, where the only goal is to remain in the game.

When you’re building something truly generative — driven by a purpose greater than yourself — there’s no point at which you can say “done.” If your company has a natural stopping point, you may be building the wrong thing.

You don’t choose the work — the work chooses you

The best founders I’ve met — and the best moments I’ve had as a founder — come from an almost irrational pull toward solving a specific problem I myself experienced.

You may want to start a company, but if you have to talk yourself into your idea, it probably won’t survive contact with reality. The founders who succeed are often the ones who can’t not work on their thing.

Starting a company shouldn’t be a career move — it should be the last possible option after every other path fails to scratch the itch.

The real killer: founder fatigue

Most companies don’t die because of one bad decision or one tough competitor. They die because the founders run out of energy.

Fatigue erodes vision, motivation and creativity. Protecting your own drive — keeping it clean and focused — may be the single most important survival skill you have.

That means staying close to the product, protecting time for customer work, and avoiding the slow drift into managing around problems instead of solving them.

Customer > competitor

It’s easy to get caught up in competitor moves, investor chatter or market gossip. But the most important question is always: Are we delivering joy to the customer?

If you’re losing focus, sign up for your own product as a brand-new user. Feel the friction. Fix it. Repeat.

At Digits, we run our own signup and core flows every week. It’s uncomfortable — it surfaces flaws we’d rather not see — but it keeps us anchored to the only metric that matters: customer delight.

Boards should ask questions, not give answers

Over the years, I’ve learned the most effective boards aren’t presentation theaters — they’re discussion rooms.

The best structure I’ve seen:

• No slides;
• A narrative pre-read sent in advance; and
• A deep dive into one essential question.

Good directors help you widen your perspective. They don’t hand you a to-do list. Rather, they help you see the problem in a way that makes the answer obvious.

Twitter: lessons from a phenomenon

When I think back to my time at Twitter, the most enduring lesson is that not all companies are built top-down. Some — like Twitter — are shaped more by their users than their executives.

Features like @mentions, hashtags and retweets didn’t come from a product roadmap — they came from the community.

That’s messy, but it’s also powerful. Sometimes your job isn’t to control the phenomenon, rather it’s to keep it healthy without smothering what made it magical in the first place.

Why now is a great time to start

If you’re building today, you have an advantage over the so-called “unicorn zombies” that raised massive rounds pre-AI and are now locked into defending old business models.

Fresh founders can design from scratch for the new reality; there’s no legacy to protect, no sacred cows to defend.

The macro environment? Irrelevant. The only timing that matters is when the problem calls you so strongly that not working on it feels impossible.

If there’s one takeaway from all of this, it’s that success is continuing. The real prize is the ability to keep playing, keep serving and keep creating.

If you’re standing at the edge, wondering if you should start — start. Take one step. See if it grows. And if it does, welcome to the infinite game.

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 Jeff Seibert is the founder and CEO of Digits, the world’s first AI-native accounting platform. He previously served as Twitter‘s head of consumer product and starred in the Emmy Award-winning Netflix documentary “The Social Dilemma.”

Illustration: Dom Guzman

PayPal is adopting the Agentic Commerce Protocol (ACP), an open source specification developed by OpenAI that lets merchants make their products available within AI apps, consequently enabling users to shop using AI agents.

Amazon could be planning to cut up to 30,000 corporate positions starting on Tuesday.

The full Disrupt 2025 breakout session agenda is now live. Join for tactical, small-group learning on AI, fundraising, M&A, workflows, and more. Browse the full lineup and register today to save up to $444, and get 60% off a second pass before prices rise on October 27.

Automattic has filed counterclaims against WP Engine, alleging that the company — backed by private equity firm Silver Lake — misused WordPress and WooCommerce trademarks, misled users, and undermined the open source community.

Q4 is here – and for ecommerce brands, that means the biggest sales opportunities of the year are just ahead.

Black Friday, Cyber Monday, Christmas – the biggest sales events are just around the corner. To hit your targets, preparation is key. It’s not too late to act, and the opportunities ahead are huge.

Use this checklist to get up to speed quickly and set your account up for success.

Website and UX

Review site speed 

Start with a website audit to identify any red flags. Tools like PageSpeed Insights can help diagnose technical issues. 

Encourage clients to review key pages and the checkout process on multiple devices to ensure there are no bottlenecks. 

If resources allow, use heatmap or session analysis tools such as Microsoft Clarity or Hotjar to better understand user behavior and improve the on-site experience.

Confirm tracking setup

Double-check that all tracking is configured correctly across platforms. 

Don’t just verify that tags are firing – make sure all events are set up to their fullest potential. 

For example, confirm high match rates in Meta and ensure Enhanced Conversions is fully configured.

Add VIP sign-ups/pop-ups

Before the sales period begins, encourage users to join a VIP list for Black Friday or holiday promotions. 

This can give them early access or exclusive deals. Set up a separate automated email flow to follow up with these subscribers.

Launch sale page early

Publish your sale page as soon as possible so Google can crawl and index it for SEO. 

The page doesn’t need to be accessible from your site navigation or populated with products right away – the key is to get it live early. 

If possible, reuse the same URL from previous years to build on existing SEO equity. 

You can also add a data capture form to collect VIP sign-ups until the page goes live with products.

Display cutoffs clearly

If shipping cutoff dates aren’t clear, many users won’t risk placing an order close to the deadline. 

Clearly display both standard and express delivery cutoff dates on your website.

Highlight sales sitewide with banners

Don’t rely solely on a homepage carousel to promote your sale. 

Add a banner or header across all pages so users know a sale is happening, no matter where they land.

Dig deeper: Holiday ecommerce to hit record $253 billion – here’s what’s driving it

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Creative and messaging

Run pre-sale lead gen ads

As mentioned with pop-ups, supplementing that strategy with lead generation ads can help grow your email list and build early buzz around your upcoming sale.

Launch simple, clear primary sale ads

These will be your Black Friday or holiday sale ads running for most of the campaign. 

Keep the messaging and promotion straightforward. Any confusion in a crowded feed will make users scroll past. 

Use strong branding, put the offer front and center, and include a clear CTA. On Meta, this often works best as a simple image ad.

Create Cyber Monday-specific ads

Many brands simply extend their Black Friday sale rather than creating Cyber Monday-specific ads and web banners. 

Take advantage of the opportunity to give your campaign a fresh angle – both in messaging and offer. 

Since it’s often the final day of your sale, you can go bigger on discounts for one day or add a free gift with purchases over a certain amount. 

It’s also a great way to move slower-selling inventory left over from Black Friday.

Refresh primary ads with ‘last days’ urgency

Add urgency to your messaging as the sale nears its end by including countdowns or end dates. 

This tactic works especially well for longer campaigns where ad fatigue can set in.

Finalize all creative assets early

November and December are busy months for ad builds and platform reviews. 

Make sure all sale assets are ready several weeks before launch to avoid rushed builds and delays from longer approval times.

Advertising and data

Audit product feeds

Make sure item disapprovals and limited products are kept to a minimum. Double-check that your setup is current. 

For example, if your return window has changed, update that information in Google Merchant Center.

Refresh first-party data and remarketing lists

Update any lists you plan to use this season. 

If you don’t have direct integrations, upload new or revised lists manually. 

Review your integrations and confirm that data is flowing correctly.

Build lookalike and custom audiences early

Start building audiences as soon as your first-party and remarketing lists are refreshed. 

Create Meta Lookalike Audiences, Performance Max audience signals, and Custom Audiences. 

If you run into volume issues, you’ll have time to adjust or explore alternatives.

Finalize budget by week, not just month

Agree on budgets early so you know your spending limits. Don’t plan just by month. Map out weekly spend, too. 

You’ll likely want to invest more heavily in the final week of November than in the first.

Use title and description extensions or ad customizers

Updating search ad copy can be tedious and time-consuming. 

These tools let you control and update copy dynamically without editing every RSA manually – saving hours in campaign builds.

Use ad assets, promo sitelinks, and GMC promotions

Enable sale-related sitelinks, callouts, and promotion extensions across search campaigns so your offers appear everywhere. 

In Shopping, set up Google Merchant Center promotions to highlight deals and incentives in your Shopping ad annotations.

Apply countdown features

Add a dynamic countdown timer to search ads to show exactly when your sale ends. 

This feature helps your ads stand out and adds urgency as the sale nears its close.

Launch search remarketing activity

Bid on generic keywords you wouldn’t normally target, but limit them to remarketing or first-party data audiences. 

For example, people searching for “Black Friday deals” who have purchased from your site in the past 30 days already know your brand and are primed to buy again.

Apply seasonality adjustments

If you use Google Ads or Microsoft Ads with a target ROAS strategy, apply seasonality adjustments to prepare the algorithm for higher conversion rates during the sale period. 

Remember to apply a negative adjustment once the sale ends to prevent unnecessary spend spikes.

Dig deeper: Seasonal PPC: Your guide to boosting holiday ad performance

Focus on what matters most for Q4 success

Not every tactic will fit your business or resources – and that’s OK. 

The key is to focus on what will have the biggest impact on your store. 

By addressing most of the points in this checklist, you’ll build a solid foundation for a strong Q4 and set yourself up to capture more sales during the busiest shopping season of the year.

Preparation is everything. The earlier you audit, test, and launch, the smoother your campaigns will run when traffic – and competition – start to surge.

A new research paper quietly published last week outlines a breakthrough method that allows large language models (LLMs) to simulate human consumer behavior with startling accuracy, a development that could reshape the multi-billion-dollar market research industry. The technique promises to create armies of synthetic consumers who can provide not just realistic product ratings, but also the qualitative reasoning behind them, at a scale and speed currently unattainable.

For years, companies have sought to use AI for market research, but have been stymied by a fundamental flaw: when asked to provide a numerical rating on a scale of 1 to 5, LLMs produce unrealistic and poorly distributed responses. A new paper, "LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings," submitted to the pre-print server arXiv on October 9th proposes an elegant solution that sidesteps this problem entirely.

The international team of researchers, led by Benjamin F. Maier, developed a method they call semantic similarity rating (SSR). Instead of asking an LLM for a number, SSR prompts the model for a rich, textual opinion on a product. This text is then converted into a numerical vector — an "embedding" — and its similarity is measured against a set of pre-defined reference statements. For example, a response of "I would absolutely buy this, it's exactly what I'm looking for" would be semantically closer to the reference statement for a "5" rating than to the statement for a "1."

The results are striking. Tested against a massive real-world dataset from a leading personal care corporation — comprising 57 product surveys and 9,300 human responses — the SSR method achieved 90% of human test-retest reliability. Crucially, the distribution of AI-generated ratings was statistically almost indistinguishable from the human panel. The authors state, "This framework enables scalable consumer research simulations while preserving traditional survey metrics and interpretability."

A timely solution as AI threatens survey integrity

This development arrives at a critical time, as the integrity of traditional online survey panels is increasingly under threat from AI. A 2024 analysis from the Stanford Graduate School of Business highlighted a growing problem of human survey-takers using chatbots to generate their answers. These AI-generated responses were found to be "suspiciously nice," overly verbose, and lacking the "snark" and authenticity of genuine human feedback, leading to what researchers called a "homogenization" of data that could mask serious issues like discrimination or product flaws.

Maier's research offers a starkly different approach: instead of fighting to purge contaminated data, it creates a controlled environment for generating high-fidelity synthetic data from the ground up.

"What we're seeing is a pivot from defense to offense," said one analyst not affiliated with the study. "The Stanford paper showed the chaos of uncontrolled AI polluting human datasets. This new paper shows the order and utility of controlled AI creating its own datasets. For a Chief Data Officer, this is the difference between cleaning a contaminated well and tapping into a fresh spring."

From text to intent: The technical leap behind the synthetic consumer

The technical validity of the new method hinges on the quality of the text embeddings, a concept explored in a 2022 paper in EPJ Data Science. That research argued for a rigorous "construct validity" framework to ensure that text embeddings — the numerical representations of text — truly "measure what they are supposed to." 

The success of the SSR method suggests its embeddings effectively capture the nuances of purchase intent. For this new technique to be widely adopted, enterprises will need to be confident that the underlying models are not just generating plausible text, but are mapping that text to scores in a way that is robust and meaningful.

The approach also represents a significant leap from prior research, which has largely focused on using text embeddings to analyze and predict ratings from existing online reviews. A 2022 study, for example, evaluated the performance of models like BERT and word2vec in predicting review scores on retail sites, finding that newer models like BERT performed better for general use. The new research moves beyond analyzing existing data to generating novel, predictive insights before a product even hits the market.

The dawn of the digital focus group

For technical decision-makers, the implications are profound. The ability to spin up a "digital twin" of a target consumer segment and test product concepts, ad copy, or packaging variations in a matter of hours could drastically accelerate innovation cycles. 

As the paper notes, these synthetic respondents also provide "rich qualitative feedback explaining their ratings," offering a treasure trove of data for product development that is both scalable and interpretable. While the era of human-only focus groups is far from over, this research provides the most compelling evidence yet that their synthetic counterparts are ready for business.

But the business case extends beyond speed and scale. Consider the economics: a traditional survey panel for a national product launch might cost tens of thousands of dollars and take weeks to field. An SSR-based simulation could deliver comparable insights in a fraction of the time, at a fraction of the cost, and with the ability to iterate instantly based on findings. For companies in fast-moving consumer goods categories — where the window between concept and shelf can determine market leadership — this velocity advantage could be decisive.

There are, of course, caveats. The method was validated on personal care products; its performance on complex B2B purchasing decisions, luxury goods, or culturally specific products remains unproven. And while the paper demonstrates that SSR can replicate aggregate human behavior, it does not claim to predict individual consumer choices. The technique works at the population level, not the person level — a distinction that matters greatly for applications like personalized marketing.

Yet even with these limitations, the research is a watershed. While the era of human-only focus groups is far from over, this paper provides the most compelling evidence yet that their synthetic counterparts are ready for business. The question is no longer whether AI can simulate consumer sentiment, but whether enterprises can move fast enough to capitalize on it before their competitors do.

ChatGPT referral traffic converts worse than Google search, email and affiliate links, trailing on both conversion rate and revenue per session, according to a new analysis of 973 ecommerce sites.

Why we care. AI search platforms are starting to refer meaningful traffic to retailers – but not yet sales. For now, Google (paid organic) search still wins on conversion and revenue per session.

By the numbers. The dataset consisted of 12 months (Augusut 2024 to July 2025), 973 ecommerce sites, and $20 billion combined revenue.

• ChatGPT referral traffic was ~0.2% of total sessions – ~200× smaller than Google organic.
• >90% of LLM-originating ecommerce traffic came from ChatGPT (Perplexity, Gemini, Copilot, etc., are were negligible).
• Affiliate (+86%) and organic search (+13%) conversion rates were higher than ChatGPT; only paid social converted worse than ChatGPT.
• ChatGPT trailed paid and organic search on revenue per session, but beat paid social.
• ChatGPT referrals had lower bounce rates than most channels, but organic/paid search was still best on bounce rate. Session depth was generally lower than most channels.

Trendline. Conversion rate and revenue per session from ChatGPT improved, while average order value declined.

• Model projections suggested continued gains but no parity with organic search within the next year.

Between the lines. Authors suggested early-stage friction – trust and verification behavior – may push shoppers to confirm elsewhere before buying, shifting last-click credit to traditional channels.

Yes, but. Findings reflect last-click attribution and an emerging channel. If ChatGPT (and other LLMs) reshape customer journeys or make it easier to buy directly, its impact on sales could become more visible in the data.

Bottom line. Despite the hype, the data suggests AI assistants haven’t disrupted Google Search – and won’t at least in the next year. However, the trajectory for AI assistants is up and to the right. Now is the time to test, learn, and iterate to be ready when LLM shopping matures.

About the research. The study analyzed 12 months of first-party Google Analytics data from 973 ecommerce websites generating $20 billion in combined revenue. Researchers compared more than 50,000 ChatGPT-driven transactions with 164 million from traditional digital channels, using regression models that accounted for data sparsity, site effects, and device differences to evaluate conversion, order value, and engagement metrics.

Recent studies echo the same pattern. LLM traffic may be rising, but it’s weaker on engagement and conversion.

• A SALT.agency analysis found organic search visitors were more likely to engage or complete key actions across most sectors, with LLMs performing better only in a few niches like health and careers.
• Another study by Amsive showed no significant conversion difference between LLM and organic traffic, and that LLM referrals made up less than 1% of total sessions.
• All of this research points to a consistent takeaway: AI-driven referrals are growing, but still lag traditional search in both scale and purchase intent.

The working paper. ChatGPT Referrals to E-Commerce Websites: Do LLMs Outperform Traditional Channels?

Dig deeper:

• ChatGPT Shopping is here – and it’s changing ecommerce SEO rules
• Optimizing for ChatGPT Shopping: How product feeds power GEO

MILPITAS, Calif. — Amazon is bringing delivery details directly to drivers’ eyeballs. 

The e-commerce giant on Wednesday confirmed that it’s developing new augmented reality glasses for delivery drivers, using AI and computer vision to help them scan packages, follow turn-by-turn walking directions, and capture proof of delivery, among other features. 

Amazon says the goal is to create a hands-free experience, making the job safer and more seamless by reducing the need for drivers to look down at a device.

Scenarios shown by the company make it clear that the devices activate after parking, not while driving, which could help to alleviate safety and regulatory concerns.

[Update, Oct. 23: Amazon executives said in briefings Wednesday that the glasses will be fully optional for drivers, and they’re designed with a hardware-based privacy button. This switch, located on the device’s controller, allows drivers to turn off all sensors, including the camera and microphone.

From a customer perspective, the company added that any personally identifiable information, such as faces or license plates, will be blurred to protect privacy.

Overall, Amazon is positioning the glasses as a tool to improve safety and the driver’s experience. We had a chance to try the glasses first-hand this week, and we’ll have more in an upcoming post.]

The wearable system was developed with input from hundreds of drivers, according to the company. It includes a small controller worn on the driver’s vest that houses operational controls, a swappable battery for all-day use, and a dedicated emergency button. 

The glasses are also designed to support prescription and transitional lenses. Amazon says future versions could provide real-time alerts for hazards, like pets in the yard, or notify a driver if they are about to drop a package at the wrong address.

According to Amazon, the smart glasses are an early prototype, currently in preliminary testing with hundreds of drivers in North America. The company says it’s gathering driver feedback to refine the technology before planning a broader rollout.

The announcement at Amazon’s Delivering the Future event in the Bay Area today confirms a report by The Information last month. That report also said Amazon is developing consumer AR glasses to compete with Facebook parent Meta’s AI-powered Ray Ban smart glasses.

The enterprise AR market is in flux, with early mover Microsoft pivoting away from HoloLens hardware, creating an opening for players like Magic Leap and Vancouver, Wash.-based  RealWear.

A demo video released by Amazon shows a delivery driver using augmented reality (AR) glasses throughout their workflow. It begins after the driver parks in an electric Rivian van, where the glasses overlay the next delivery address directly onto a view of the road.

“Dog on property,” the audio cue cautions the driver. 

Upon parking, the driver moves to the cargo area. The AR display then activates to help with sorting, with green highlights overlaid on the specific packages required for that stop. As the driver picks each item, it’s scanned and a virtual checklist in their vision gets updated.

After retrieving all the packages from the cargo hold, the driver begins walking to the house. The glasses project a digital path onto the ground, guiding them along the walkway to the front door. 

Once at the porch, the display prompts the driver to “Take photo” to confirm the delivery. After placing the items, the driver taps a chest-mounded device to take the picture. A final menu then appears, allowing the driver to “Tap to finish” the stop before heading back to the van.

Amazon’s e-commerce customers are experiencing unusual delivery delays following the Amazon Web Services outage on Monday — suggesting that the cloud glitch has impacted the company’s own operations more than previously reported.

Customers posting on Reddit and X reported Amazon orders that were scheduled for Monday delivery but did not arrive. Some of the comments:

• “I received a delay email on everything due today. Coming tomorrow and I’m fine with that.”
• “I have 4 items that are suppose to be delivered today as well and they haven’t even left the facility. So I’m sure it’s the outage.”
• “My amazon fresh order was cancelled at 5:15PM.”

Amazon workers posting on the “r/AmazonFC” Reddit community cited downtime at fulfillment centers.

• “Today was the first day I’ve experienced an entire day of downtime, and not as a shutdown for maintenance. Very odd feeling to maintain a constant state of readiness for 10 hours in case the system comes back at any moment.”

We reached out to Amazon for details about delayed deliveries.

Amazon’s package fulfillment systems run atop AWS infrastructure — so disruptions in key AWS services can ripple directly into its retail and logistics network.

Amazon’s logistics arm processes about 17.2 million delivery orders per day, according to Capital One.

The fallout from delayed deliveries could lead to increased costs due to potential refund obligations and additional labor needs.

The outage started shortly after midnight Monday and lasted for about three hours, but the aftershock effects were felt by Amazon’s cloud customers for much of the day. The company blamed a DNS resolution issue with its DynamoDB service in US-EAST-1 region, it oldest and largest digital hub. Major outages originating from this same region also caused widespread disruptions in 2017, 2021, and 2023.

The outage impacted everything from sites including Facebook, Coinbase, and Ticketmaster, to check-in kiosks at LaGuardia Airport. Amazon’s own retail site, its Prime Video streaming service, and its Ring subsidiary were also affected.

Despite the major outage, Amazon’s stock was up Monday and in early Tuesday trading.