On this episode of A Productive Conversation, I sit down with Leslie Grandy, author of Creative Velocity: Propelling Breakthrough Ideas in the Age of Generative AI. Leslie is a global product executive turned CEO advisor who helps organizations unlock creative thinking to accelerate growth. Her decades of leadership at Apple, Amazon, Best Buy, and T-Mobile give her a unique perspective on how creativity, technology, and leadership intersect.

Our conversation explores why creativity isn’t limited to artists, how space and time fuel ideation, and what role emotional regulation plays in sustaining momentum. We also dig into how precision, AI, and frameworks can both hinder and propel breakthrough ideas. If you’ve ever doubted your own creativity—or wondered how to harness it consistently—you’ll want to hear this one.

---

Six Discussion Points

• Why many professionals mistakenly believe they aren’t creative—and how to reframe that thinking.
• The power of space—whether walking, running, or even showering—in activating creative flow.
• Precision as both a driver and deterrent of creative velocity, depending on how it’s applied.
• Emotional regulation and equanimity as essential tools for sustaining creativity without burnout.
• How to think about velocity beyond speed—focusing on predictability, quality, and intentional triggers.
• Using AI as a creative collaborator through structured frameworks to expand possibilities without chaos.

Three Connection Points

This conversation with Leslie reminded me that creativity isn’t an exclusive club—it’s a capacity we all share, provided we give ourselves the time, space, and intention to use it. Whether you’re leading a team, writing your next book, or simply looking to bring more meaning to your daily choices, Leslie’s insights on creative velocity offer a clear path forward.

Want to support the podcast? You can subscribe to the show and leave quick rating and review wherever you listen to podcasts. You can subscribe on Spotify and also on Apple Podcasts.

---

With original reporting and sharp analysis, WIRED’s Uncanny Valley podcast covers today’s biggest stories in tech. We demystify companies like Palantir, trends like vibe coding, and figures like Sam Altman; we break down our essential coverage of DOGE and ICE; we guide listeners through breakthrough innovations like generative AI and sweeping policy changes like the Trump administration’s tariffs.

On September 9, at 7 pm PDT, WIRED is partnering with KQED for Uncanny Valley’s first live show of the podcast. The first part of the event will feature WIRED editor in chief Katie Drummond in conversation with Jack Conte, CEO of Patreon. As a founder and musician, Conte has been at the forefront of adapting and harnessing the power of changing technological landscapes to the advantage of creatives. This summer, Patreon reached a milestone of $10 billion in payments to creators since the company was founded in 2013.

The roundtable conversation with our cohosts Michael Calore and Lauren Goode will center around how San Francisco grew to be the tech hot spot it is today, and how that role has changed in recent years. Emerging technologies like AI and machine learning have allowed the Bay Area to retain its leadership position—but can it keep it?

You won’t want to miss this event. Get your tickets here.

For those not based in the Bay Area, you can tune in via the livestream on this page:

And if you’re not yet a listener, you can check out past episodes below.

An incredible finding from MIT’s NANDA initiative paints a stark picture of generative AI use in the enterprise. While some companies are succeeding in meaningfully growing revenue or reducing costs by leveraging generative AI, around 95% of artificial intelligence (AI) pilot programs fail to make a meaningful impact. This conclusion comes from interviews with leaders, surveys of employees, and analysis of public AI deployments.

The biggest problem, according to MIT’s research, was poor integration. Companies that succeeded in deploying generative AI stuck to narrow, well-defined problems. In contrast, companies that failed to properly integrate powerful AI models into their workflows ran into serious issues.

Another wrinkle was that many AI deployments were focused on sales and marketing applications, whereas the highest returns on investments generally came from back-office automation.

IBM’s AI strategy is validated

This apparent widespread failure to deploy generative AI productively provides validation for IBM‘s (IBM 0.43%) AI strategy. IBM has generated more than $7.5 billion in bookings related to generative AI, but around 80% of that business comes from the company’s consulting arm. By pairing consulting services with software, IBM can deliver complete AI solutions, including integration services, that deliver results for its clients.

The MIT study found that many enterprises in highly regulated sectors were attempting to build their own proprietary generative AI systems, but failure rates were higher compared to purchased solutions. Generative AI is a powerful technology, but it’s far from foolproof and won’t produce results if not implemented with care.

During IBM’s second-quarter earnings call, CEO Arvind Krishna said the company was seeing strong demand for AI agent solutions and its cost-effective Granite AI models. Krishna also noted that demand was accelerating for its consulting services related to deploying AI.

Part of IBM’s secret sauce is its network of partnerships with other technology companies. IBM can construct AI solutions for clients that involve third-party cloud platforms like Amazon Web Services and third-party software, opening the door to a lot more business than the company would win if it stuck with its own products and services. As of late 2023, some of IBM’s partnerships were already bringing in billions of dollars of business annually.

IBM’s AI business is helping to offset weak demand for some discretionary projects. The company is seeing delayed decision-making for projects that aren’t mission-critical and don’t have clear returns on investment. In contrast, many of the AI projects IBM is working on with clients are aimed at reducing costs or boosting efficiency, which is appealing during periods of economic uncertainty.

Time to buy this enterprise AI leader

With enterprises struggling to successfully implement generative AI technology on their own, IBM is in a great position to grow its generative AI business. The company’s consulting-focused strategy is winning more than $1 billion in new generative AI business each quarter, a number that could grow as more clients abandon home-grown AI efforts that aren’t paying off.

AI is one reason why IBM’s revenue growth is accelerating. The company expects to generate constant-currency revenue growth of at least 5% this year despite an uncertain economic backdrop, and free cash flow is expected to grow to more than $13.5 billion. With a current market capitalization around $225 billion, IBM stock trades for less than 17 times free cash flow guidance.

At that valuation, IBM stock seems like a great long-term buy. As enterprises grapple with complex AI integration, IBM is emerging as the partner of choice.

Timothy Green has positions in International Business Machines. The Motley Fool has positions in and recommends Amazon and International Business Machines. The Motley Fool has a disclosure policy.

The biggest pushback after OpenAI announced its new GPT-5 model for ChatGPT came from devotees of older models who felt the new generative AI chatbot lacked the panache of its predecessors.

Now you have more choices of pre-GPT-5 models (although you’ll have to hunt for some of them) and better control over which components of GPT-5 handle your questions.

OpenAI is still sorting through a somewhat rocky launch of GPT-5, led by complaints about the lack of model choices. The model has been anticipated for more than two years and comes as competitors like Anthropic and Google have released powerful new versions of their AI models this year. (Disclosure: Ziff Davis, CNET’s parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.)

OpenAI planned for one model that could handle everything: GPT-5 includes two different modes, one fast and lean for simple tasks, and one aimed at reasoning for complicated ones. A routing program would decide which model handled a given prompt. That’s still the default in ChatGPT, but it’s not your only option. 

Choices of GPT-5 models

There are a few different modes of GPT-5 you can select between if you want to use OpenAI’s newest technology. Here’s a quick rundown:

Enlarge Image


• Auto: This mode allows the switching program built into GPT-5 to decide whether your query is handled by a lighter, faster large language model or a bigger, slower reasoning model. OpenAI CEO Sam Altman posted on X that this will be the best fit for most people.
• Fast: Your query will go straight to the fastest, lightest model. Expect quick, basic answers, but not the in-depth research you’d get from a reasoning model. 
• Thinking: This is a reasoning model, meaning it’ll try to answer your question over several steps. It might use web searches and other tools, or it might go back and redo its past steps to try to get the right answer. There are some limits on how much you can use this model (3,000 messages per week right now).
• Thinking mini: This is a lighter, faster version of the Thinking model. If you hit the usage limit on Thinking, you’ll be stuck with Thinking mini. You can access it on your own if you enable more model options (more on that below).
• Pro: The most powerful reasoning model in the fleet, this is only available at the moment for Pro users, who pay $200 per month. (Although Altman did tease giving Plus users, who pay $20 a month, a very limited number of queries as a taste.)

Again, sticking with Auto is probably easiest for most users. Think of it like driving a car with an automatic transmission instead of having to change gears manually. Sure, people who are really into cars might prefer the stick shift, but most people should probably just let the machine handle it.

How to get the older OpenAI models

Everyone with a paid ChatGPT subscription can access the older GPT-4o model directly in the same menu where you can choose your flavor of GPT-5. This model received the most clamor from users after it was removed, and Altman said if OpenAI ever decides to take it away permanently, “we will give plenty of notice.”

But 4o isn’t your only choice (if you’re a paid user). You just have to know where to look. To access GPT-4.1, 4o-mini and 3o, along with GPT-5 Thinking mini, you’ll have to go into your Settings and toggle on “Show additional models.”

One model, many personalities

OpenAI’s plan was for one GPT-5 model to replace all the old ones, and that still appears to be the goal. Altman said changes to the default personality of GPT-5 should satisfy some users who were unsatisfied by the new version. GPT-5 also allows you to customize how it responds to you. If you want it to be more sassy, or more nerdy, you don’t need to change models for that. More of that seems to be the long-term goal.

“The real solution here remains letting users customize ChatGPT’s style much more,” Altman said on X. 

Kate Gulliver, CFO and chief administrative officer at Wayfair, spoke with CFO Brew about her career, and about her company’s plan to roll with the punches.

From startup to category leader: In some ways, Gulliver has grown along with Wayfair. After working in private equity, she joined the company as head of investor relations in 2014, and helped to run its IPO. At that time, it had about $1 billion in sales and 2,000 employees, Gulliver said. She describes it as “a super high-growth but relatively immature company from a systems and process perspective.” Today, Wayfair employs around 12,000 people and brought in $12 billion in revenue from June 2024 through June 2025.

From investor relations, Gulliver became global head of talent, and was named CFO and CAO in 2022. Her career at Wayfair has evolved in an organic fashion.

“I largely let my career be guided by the opportunity most immediately in front of me,” she said. “I’ve never tried to guide toward ‘10 years from now, here’s where that role is getting me.’ It’s been more ‘Is this the next right move?’”

As a combined CFO and chief administrative officer, Gulliver has plenty on her plate: HR, finance, real estate, legal and compliance, corporate affairs, and communications all report to her. She enjoys the breadth of the dual role, which she says gives her insight into the “backbone” of the company. “Intellectually,” the many departments she oversees “can feel quite different day to day, which is fun,” she said.

A turbulent five years for retail: As a seller of discretionary goods, Wayfair has been on a rocky ride over the past five years. It was able to capitalize on the home goods boom of the pandemic, when shoppers stuck in lockdown were buying items for their spaces. But as restrictions lifted and consumers pivoted toward spending on experiences, it saw net losses for three consecutive years. Wayfair had to restructure and underwent several rounds of layoffs, cutting around 13% of its workforce, or 1,650 jobs, in 2024.

Now, though, the category is “starting to stabilize,” Gulliver said. Wayfair had a bumper second quarter this year, with revenues rising 5% year over year.

“We’re feeling good about the momentum currently,” she said.

Wayfair isn’t seeing consumer softness yet due to tariffs and economic uncertainty, Gulliver said, though it’s seeing more strength in its high-end lines, such as Perigold, AllModern, and Joss & Main, than in its “core mass” lines. (“There’s no question the higher-end market is stronger than mass,” CEO Niraj Shah said during a recent earnings call.) The company is keeping its eye on the macroeconomic picture, though. It’s doing a lot of forecasting, incorporating both its internal data and third-party inputs such as credit card data and housing market trends, Gulliver said.

So far tariffs haven’t had that much of an impact, Gulliver said. That’s partly because Wayfair is a marketplace. Sellers post many unbranded items that look similar to one another, so they’re largely competing on price, she said. Lower prices also allow for better placement on Wayfair’s search results, boosting sales. Sellers, Gulliver said, are finding ways to absorb or offset tariffs at different points along the supply chain, which is “helping to insulate consumers” from higher prices. “Consumers are still seeing like-for-like pricing,” she said.

AI, how about midcentury modern? Wayfair is also anticipating changes generative AI might make to shopping habits. It’s partnering with some major AI providers on developing agentic shopping tools, Gulliver said. And it’s added GenAI features to its website and app that show customers how furniture might look in different spaces within a home, alongside recommendations for similar Wayfair products. “It’s a fun way to capitalize on how consumers might be changing how they shop,” Gulliver said.

At the same time, the retailer’s made a surprisingly analog move: opening brick-and-mortar stores. Its Chicago store has resulted in a “halo” effect, boosting sales and brand recognition in the Chicago area, Shah said on an earnings call. Three more physical stores are planned in the coming years.

As a Wayfair shopper and home design fan herself (“That is the thing I read about in my spare time”), Gulliver understands what consumers are looking for. But even her broad remit, she acknowledges, only goes so far. “I’m always going to the brand team or the merchant team” and asking, ‘Have we thought about getting this product?’,” she said. “And they’re like, ‘Kate, stay in your lane.’”

This report was originally published by CFO Brew.

Introducing the 2025 Fortune Global 500, the definitive ranking of the biggest companies in the world. Explore this year’s list.

As generative AI reshapes industries, one of its most important yet invisible challenges is retrieval, the process of fetching the right data with relevant context from messy knowledge bases. Large language models (LLMs) are only as accurate as the information they can retrieve.

That’s where ZeroEntropy wants to make its mark. The San Francisco-based startup, co-founded by CEO Ghita Houir Alami and CTO Nicolas Pipitone, has raised $4.2 million in seed funding to help models retrieve relevant data quickly, accurately, and at scale.

The round was led by Initialized Capital, with participation from Y Combinator, Transpose Platform, 22 Ventures, a16z Scout, and a long list of angels, including operators from OpenAI, Hugging Face, and Front.

ZeroEntropy joins a growing wave of infrastructure companies hoping to use retrieval-augmented generation (RAG) to power search for the next generation of AI agents. Competitors range from MongoDB’s VoyageAI to early fellow YC startups like Sid.ai.

“We’ve met a lot of teams building in and around RAG, but Ghita and Nicolas’s models outperform everything we’ve seen,” says Zoe Perret, partner at Initialized Capital. “Retrieval is undeniably a critical unlock in the next frontier of AI, and ZeroEntropy is building it.”

Retrieval-augmented generation (RAG) grabs data from external documents and has become a go-to architecture for AI agents, whether it’s a chatbot surfacing HR policies or a legal assistant citing case law.

Yet the ZeroEntropy founders believe that for many AI apps, this layer is fragile: a cobbled collection of vector databases, keyword search, and re-ranking models. ZeroEntropy offers an API that manages ingestion, indexing, re-ranking, and evaluation.

What that means is that — unlike a search product for enterprise employees like Glean — ZeroEntropy is strictly a developer tool. It quickly grabs data, even across messy internal documents. Houir Alami likens her startup to a “Supabase for search” referring to the popular open-source database that automates much of the database management.

“Right now, most teams are either stitching together existing tools from the market or dumping their entire knowledge base into an LLM’s context window. The first approach is time-consuming to build and maintain,” Houir Alami said. “The second approach can cause compounding errors. We’re building a developer-first search infrastructure—think of it like a Supabase for search—designed to make deploying accurate, fast retrieval systems easy and efficient.”

At its core is its proprietary re-ranker called ze-rank-1, which the company claims currently outperforms similar models from Cohere and Salesforce on both public and private retrieval benchmarks. It makes sure that when an AI system looks for answers in a knowledge base, it grabs the most relevant information first.

More than 10 early-stage companies building AI agents across verticals such as healthcare, law, customer support, and sales are already using ZeroEntropy, she adds.

Born and raised in Morocco, Houir Alami left home at 17 to pursue engineering education in France, attending École Polytechnique, a prestigious military and mathematics-focused institution. There, she discovered her love for machine learning.

She moved to California two years ago to complete a master’s in mathematics at UC Berkeley, where she deepened her interest in building intelligent systems.

Before founding ZeroEntropy, Houir Alami dabbled in building an AI assistant—her take on a conversational agent—before ChatGPT became mainstream. She says the insight gained from trying to build that assistant, particularly the realization of how important it was to provide the right context and information to the LLM to be useful, partly inspired her to start ZeroEntropy.

In a field often criticized for its lack of diversity, the 25-year-old Houir Alami is one of the few female CEOs building deep infrastructure for one of the hardest problems in AI. Yet, she hopes it doesn’t stay that way for long. 

“There aren’t many women in DevTools or AI infra,” she said. “But I’d tell any young woman interested in technical problems: don’t let that stop you. If you’re drawn to complex, technical problems, don’t let anyone make you feel like you’re not capable of pursuing them. You should go for it.”

She also stays connected to her roots by giving talks at high schools and universities in Morocco, aiming to inspire more young girls to pursue STEM.

Meta’s big investment in Scale AI may be giving some of the startup’s customers pause.

Reuters reports that Google had planned to pay Scale $200 million this year but is now having conversations with its competitors and planning to cut ties. Microsoft is also reportedly looking to pull back, and OpenAI supposedly made a similar decision months ago, although its CFO said the company will continue working with Scale as one of many vendors.

Scale’s customers include self-driving car companies and the U.S. government, but Reuters says its biggest clients are generative AI companies seeking access to workers with specialized knowledge who can annotate data to train models.

Google declined to comment on the report. A Scale spokesperson declined to comment on the company’s relationship with Google, but he told TechCrunch that Scale’s business remains strong, and that it will continue to operate as an independent company that safeguards its customers’ data.

Earlier reports suggest that Meta invested $14.3 billion in Scale for a 49% stake in the company, with Scale CEO Alexandr Wang joining Meta to lead the company’s efforts to develop “superintelligence.”