These days when you want to engage with some media, you can choose from podcasts, videos, games, live performances — or books, one of the oldest and most popular ways to learn something new or escape (at least temporarily) from today’s troubled world.
Artificial Intelligence
The best books we read in 2025
We polled the staff of The Verge to find out what books they read over the past year that really struck a chord — because the books were enlightening, educational, or just enjoyable. Here are some of the answers we got. (And please let us know what your favorite reads were in 2025 in the comments.)
Brandt Ranj, commerce writer
Screwing up is one of the inevitabilities of life, but dwelling on your own mistakes can get toxic — fast. In SNAFU: The Definitive Guide to History’s Greatest Screwups, Ed Helms breaks down dozens of blunders from the past 70 or so years. Sometimes a river gets so polluted that it catches fire (multiple times), or the US government conducts human experiments involving copious amounts of psychedelic drugs. Or it turns out that a pair of the 20th century’s greatest spies were also swingers. These things happen.
While many of the events in the book are absolute tragedies, Helms infuses each chapter with a little bit of humor, mostly in pointing out how stupid the perpetrators are. It’s a relatively breezy book that’ll make you feel better about the time you accidentally overcooked microwave popcorn and smoked out your apartment.
Victoria Song, senior reviewer
I’ve been on a memoir kick this year, and this one knocked my socks off. Things in Nature Merely Grow explores Yiyun Li’s experience with grief after losing both of her teenage sons to suicide. This isn’t a light read by any means. The writing is honest, beautiful, sometimes aloof, and oftentimes devastating. But as someone who lost both parents relatively young, I felt strangely comforted by Li’s take on “the abyss” and “radical acceptance.”
“Some consolations are strictly and purely for the consolers themselves. Please hold on to your silver linings, as I must decline.” Some might say this book is unnecessarily harsh in its unflinching look at loss, but that line stuck with me as the truest reflection of how I felt for the longest time. I recommend it to anyone struggling with isolation after loss. (Some honorable mentions for memoirs: Educated by Tara Westover, I’m Glad My Mom Died by Jennette McCurdy, and Maybe You Should Talk to Someone by Lori Gottlieb.)
Dominic Preston, news editor
I’d somehow never read any Le Guin until this year, and while I’ve been told that The Dispossessed is an odd place to start, it’s the book I had around, having borrowed it from a friend who knows how long ago. I would feel bad for having hung onto it so long, but the erosion of private property rights is surprisingly on-brand for the book. Its exploration of a moon with a (mostly) functional anarchist society, and the arch-capitalist planet its community sprung off from, holds a pretty unflinching mirror up against our world, and although over 50 years old, it’s about as relevant as ever.
Don’t go in expecting a rollercoaster plot, but if you like the sound of capitalist critique interspersed with an exploration of how academic bureaucracy works under anarchism, boy do I have the book for you.
After watching 3 Body Problem on Netflix, I couldn’t stop thinking about it. So naturally, I just had to delve into the book series to see what details the adaptation left out — and it doesn’t disappoint. Cixin Liu’s The Three-Body Problem details the origins of Ye Wenjie, an astrophysicist who’s forced to work on a secretive military project during China’s Cultural Revolution in the late ‘60s. While stationed at the secluded Red Coast Base, Wenjie makes a decision that changes the fate of humanity forever, culminating in a series of unsettling events years later that lead some scientists to question everything they know about the laws of physics. In the present day, Wang Miao, a level-headed nanotechnologist, attempts to brush off strange hallucinations until he’s drawn to a virtual reality game that he discovers is linked to a larger conspiracy tied to Wenjie.
Barbara Krasnoff, reviews editor
After I finished the first story in Theodora Goss’ weird fiction collection, Letters from an Imaginary Country, I was hooked. Titled “The Mad Scientist’s Daughter,” it is the first-person account of a group of 19th century female “monsters” from well-known horror literature such as Frankenstein, The Island of Dr. Moreau, and Dr. Jekyll and Mr. Hyde, among others. The women, who are distinctly other than the perceived norm — either because of their abilities or their appearance, or both — must support each other while navigating the narrow mores of Victorian society. Most of the stories in this collection seem to center on people who are somehow different, outside of their societies, and trying to cope (sometimes successfully, sometimes not) with those differences. I sped through the first story, and while I haven’t finished the book quite yet, I’m certainly looking forward to it.
Alexia LaFata, senior SEO manager
As a published author myself, I’m always looking for novels that combine a propulsive plot, richly drawn characters, and thought-provoking social commentary to inspire my own craft. What Kind of Paradise by Janelle Brown fits that bill completely. It’s an exhilarating novel set in the ’90s about a teenage girl named Jane who escapes her rural life in Montana, where she lives in isolation with her father. Motivated by his fears of technology and progress, her father keeps the two of them living off the grid, banning Jane from using the internet and homeschooling her with old books. But as Jane gets older, she becomes increasingly curious about the outside world and about her father’s mysterious past in San Francisco. She also becomes an accidental accomplice in one of his crimes, and it’s this horrific realization that sends her to the Bay Area to find out why he is the way he is.
What Kind of Paradise is a brilliantly drawn examination of the impact of technology, how deeply your family can influence your identity, and the kinds of doomsday fears that drive people to madness. It’s part coming-of-age tale, part thriller, part family drama — you can’t quite classify it, but its ingenious genre-bending is one of my favorite things about it. If you’re looking to really sink your teeth into a story, this is the one. I promise you will devour it whole.
Elizabeth Lopatto, senior reporter
People will tell you Moby-Dick is Melville’s masterpiece. This is wrong, but understandable — it flatters Americans to think that we bear some resemblance to the insane Ahab, speaking in Shakespearean blank verse as we speed toward our doom.
Melville’s actual masterpiece is his last book, The Confidence-Man, one of the meanest and truest things ever written about this country. It takes place on April Fools’ Day, on a riverboat full of scammers and other assorted riffraff; one of them may or may not be literally Satan. Among the scams being perpetrated on the ship: selling stock of a struggling company, bringing “the Wall Street spirit” to charity, hawking supplements, finessing a free haircut, and forming a cult. There are nasty caricatures of Nathaniel Hawthorne, Edgar Allen Poe, Ralph Waldo Emerson, and Henry David Thoreau, all funny. The book is by turns bizarre, bitter, hopeful, and oddly credulous. Read it — you will recognize several of its characters immediately, as their current incarnations have been barely updated in 2025. Sure, The Confidence-Man isn’t as pretty as Moby-Dick, but then, the truth rarely is.
Elissa Welle, AI reporting fellow
It’s rare for a book about a company’s successful founding to be a page-turner. Usually, corporate drama centers on the company’s fall, its unraveling, or the criminal activity of its leaders. Not so in Empire of AI by Karen Hao. This is a book about OpenAI’s founding and growth, and it’s truly a riveting read. (It also helps that Hao is a lovely writer.) She tracks the personality quirks of visionary leaders over time. She monitors the changing corporate mission. And yes, she dives into The Drama, when Sam Altman was temporarily ousted from OpenAI by the board. But she doesn’t just stay in Silicon Valley. You travel with her to countries in Africa and South America to see how AI is impacting societies there.
I’ve heard complaints that Hao’s personal views on AI, and the technology’s societal repercussions come off a little heavy-handed. She does include opinions, often tucked away at the end of chapters, and it feels all okay to me. Her expertise in the field gained through years of covering OpenAI is a useful perspective for those of us trying to make sense of a rapidly growing industry. You can read the facts presented in her book and come to different conclusions.
Kate Cox, senior producer, Decoder
Honestly, the less you know going in, the better this book might be. The Everlasting by Alix E. Harrow is a love story about a lady knight. It’s also a masterful deconstruction of Arthuriana, the way mythology works, and the very idea of nationalism itself. A brilliant, bittersweet puzzle box that solves itself incredibly satisfyingly at the end. I only wish I could read it for the first time again.
Terrence O’Brien, weekend editor
Agustina Bazterrica’s last novel, Tender is the Flesh, is unflinchingly bleak. And The Unworthy isn’t exactly an uplifting tale. It’s a postapocalyptic story set in the convent of a cult that “rewards” its Enlightened members with physical mutilation. But, where Flesh reveled in its gore, The Unworthy offers moments of genuine beauty and hope.
There are still plenty of passages that showcase Bazterrica’s skills at making your skin crawl, but perhaps the most impressive thing about the book is how much world-building this epistolary novel manages to do in a mere 177 pages — you could easily blow through this in a day. If you’re a fan of The Handmaid’s Tale by Margaret Atwood or I Who Have Never Known Men by Jacqueline Harpman, I don’t think you’ll want to miss out on this excellent piece of Argentinian feminist horror.
Josh Dzieza, investigations editor
I read Chris Hayes’ book about attention, The Sirens’ Call, early in the year and found myself returning to it regularly. Two insights were particularly useful. The first is from the computer scientist Herbert Simon: that as the quantity of information increases, attention becomes the scarcest and therefore most valuable resource — information can be infinite, but we have only so many hours in the day to spend consuming it.
The second is that it is easier to capture attention than to hold it. In Hayes’ example, it’s an easier challenge to get everyone’s attention in a room by, say, screaming or tearing off your clothes, than to keep the room rapt for an hour with a monologue. It follows that if you move public discourse, politics, or culture onto platforms where everyone can say anything at all times, and where the platforms are financially incentivized to keep everyone there transfixed as long as possible and therefore reward their best attention-getters, an arms race occurs that ends in a rapid-fire media environment that is impossible to look away from but also fractured, incoherent, and generally bad for everyone’s brains.
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Artificial Intelligence
Ronnie Sheth, CEO, SENEN Group: Why now is the time for enterprise AI to ‘get practical'
Before you set sail on your AI journey, always check the state of your data – because if there is one thing likely to sink your ship, it is data quality.
Gartner estimates that poor data quality costs organisations an average of $12.9 million each year in wasted resources and lost opportunities. That’s the bad news. The good news is that organisations are increasingly understanding the importance of their data quality – and less likely to fall into this trap.
That’s the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalisation and literacy, and Sheth notes she has been in the data and AI space ‘ever since [she] was a corporate baby’, so there is plenty of real-world experience behind the viewpoint. There is also plenty of success; Sheth notes that her company has a 99.99% client repeat rate.
“If I were to be very practical, the one thing I’ve noticed is companies jump into adopting AI before they’re ready,” says Sheth. Companies, she notes, will have an executive direction insisting they adopt AI, but without a blueprint or roadmap to accompany it. The result may be impressive user numbers, but with no measurable outcome to back anything up.
Even as recently as 2024, Sheth saw many organisations struggling because their data was ‘nowhere where it needed to be.’ “Not even close,” she adds. Now, the conversation has turned more practical and strategic. Companies are realising this, and coming to SENEN Group initially to get help with their data, rather than wanting to adopt AI immediately.
“When companies like that come to us, the first course of order is really fixing their data,” says Sheth. “The next course of order is getting to their AI model. They are building a strong foundation for any AI initiative that comes after that.
“Once they fix their data, they can build as many AI models as they want, and they can have as many AI solutions as they want, and they will get accurate outputs because now they have a strong foundation,” Sheth adds.
With breadth and depth in expertise, SENEN Group allows organisations to right their course. Sheth notes the example of one customer who came to them wanting a data governance initiative. Ultimately, it was the data strategy which was needed – the why and how, the outcomes of what they were trying to do with their data – before adding in governance and providing a roadmap for an operating model. “They’ve moved from raw data to descriptive analytics, moving into predictive analytics, and now we’re actually setting up an AI strategy for them,” says Sheth.
It is this attitude and requirement for practical initiatives which will be the cornerstone of Sheth’s discussion at AI & Big Data Expo Global in London this week. “Now would be the time to get practical with AI, especially enterprise AI adoption, and not think about ‘look, we’re going to innovate, we’re going to do pilots, we’re going to experiment,’” says Sheth. “Now is not the time to do that. Now is the time to get practical, to get AI to value. This is the year to do that in the enterprise.”
Watch the full video conversation with Ronnie Sheth below:
Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour.
The “build it and they will come” model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide deployments because initial financial modelling ignored the realities of production scaling.
“When we integrate FinOps capabilities with automation, we’re looking at a change from being very reactive on cost management to being very proactive around value engineering,” says Holmes.
This shifts the assessment criteria for technical leaders. Rather than waiting “months or years to assess whether things are getting value,” engineering teams can track resource consumption – such as cost per transaction or API call – “straight from the beginning.”
The unit economics of scaling intelligent automation
Innovation projects face a high mortality rate. Holmes notes that around 80 percent of new innovation projects fail, often because financial opacity during the pilot phase masks future liabilities.
“If a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that’s really successful,” says Holmes. “But what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn’t over-provision to that degree during a real production rollout.”
Moving that workload to production changes the calculus. The requirements for compute, storage, and data transfer increase. “API calls can multiply, exceptions and edge cases appear at volume that might have been out of scope for the pilot phase, and then support overheads just grow as well,” he adds.
To prevent this, organisations must track the marginal cost at scale. This involves monitoring unit economics, such as the cost per customer served or cost per transaction. If the cost per customer increases as the customer base grows, the business model is flawed.
Conversely, effective scaling should see these unit costs decrease. Holmes cites a case study from Liberty Mutual where the insurer was able to find around $2.5 million of savings by bringing in consumption metrics and “not just looking at labour hours that they were saving.”
However, financial accountability cannot sit solely with the finance department. Holmes advocates for putting governance “back in the hands of the developers into their development tools and workloads.”
Integration with infrastructure-as-code tools like HashiCorp Terraform and GitHub allows organisations to enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates.
“Rather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,” Holmes explains, companies can verify they are “deploying the right things at the right time.”
When scaling intelligent automation, tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved.
“This translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,” says Holmes. “It’s having a common language between technology and finance and with the business.”
The TBM taxonomy provides a standardised framework to reconcile these views. It maps technical resources (such as compute, storage, and labour) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs.
“I don’t necessarily know what goes into all the IT layers underneath it,” Holmes says, describing the business user’s perspective. “But because we’ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving it to be more expensive as I consume more.”
Addressing legacy debt and budgeting for the long-term
Organisations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernisation. Holmes warns that if a company is “just trying to mask inefficient processes and not redesign them,” they are merely “building up more technical debt.”
A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilised a TCO model across 2,000 different applications – of various maturity stages – to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labour, and the engineering time required to keep automation running.
“Just because of something’s legacy doesn’t mean you have to retire it,” says Holmes. “Some of those legacy systems are worth maintaining just because the value is so good.”
In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. “Sometimes when you add up the TCO approach, and you’re including all these automation layers around it, you suddenly realise, the real cost of keeping that old system alive is not just the old system, it’s those extra layers,” Holmes argues.
Avoiding sticker shock requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency.
Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organisations to negotiate economies of scale and standardise architecture.
“Because you’ve made those longer term commitments and you’ve standardised on different platforms and things like that, it makes it easier to build the right thing out for the long term,” Holmes says.
Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation.
IBM is a key sponsor of this year’s Intelligent Automation Conference Global in London on 4-5 February 2026. Greg Holmes and other experts will be sharing their insights during the event. Be sure to check out the day one panel session, Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons, to hear more from Holmes and swing by IBM’s booth at stand #362.
See also: Klarna backs Google UCP to power AI agent payments
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
FedEx is using AI to change how package tracking and returns work for large enterprise shippers. For companies moving high volumes of goods, tracking no longer ends when a package leaves the warehouse. Customers expect real-time updates, flexible delivery options, and returns that do not turn into support tickets or delays.
That pressure is pushing logistics firms to rethink how tracking and returns operate at scale, especially across complex supply chains.
This is where artificial intelligence is starting to move from pilot projects into daily operations.
FedEx plans to roll out AI-powered tracking and returns tools designed for enterprise shippers, according to a report by PYMNTS. The tools are aimed at automating routine customer service tasks, improving visibility into shipments, and reducing friction when packages need to be rerouted or sent back.
Rather than focusing on consumer-facing chatbots, the effort centres on operational workflows that sit behind the scenes. These are the systems enterprise customers rely on to manage exceptions, returns, and delivery changes without manual intervention.
How FedEx is applying AI to package tracking
Traditional tracking systems tell customers where a package is and when it might arrive. AI-powered tracking takes a step further by utilising historical delivery data, traffic patterns, weather conditions, and network constraints to flag potential delays before they happen.
According to the PYMNTS report, FedEx’s AI tools are designed to help enterprise shippers anticipate issues earlier in the delivery process. Instead of reacting to missed delivery windows, shippers may be able to reroute packages or notify customers ahead of time.
For businesses that ship thousands of parcels per day, that shift matters. Small improvements in prediction accuracy can reduce support calls, lower refund rates, and improve customer trust, particularly in retail, healthcare, and manufacturing supply chains.
This approach also reflects a broader trend in enterprise software, in which AI is being embedded into existing systems rather than introduced as standalone tools. The goal is not to replace logistics teams, but to minimise the number of manual decisions they need to make.
Returns as an operational problem, not a customer issue
Returns are one of the most expensive parts of logistics. For enterprise shippers, particularly those in e-commerce, returns affect warehouse capacity, inventory planning, and transportation costs.
According to PYMNTS, FedEx’s AI-enabled returns tools aim to automate parts of the returns process, including label generation, routing decisions, and status updates. Companies that use AI to determine the most efficient return path may be able to reduce delays and avoid returning things to the wrong facility.
This is less about convenience and more about operational discipline. Returns that sit idle or move through the wrong channel create cost and uncertainty across the supply chain. AI systems trained on past return patterns can help standardise decisions that were previously handled case by case.
For enterprise customers, this type of automation supports scale. As return volumes fluctuate, especially during peak seasons, systems that adjust automatically reduce the need for temporary staffing or manual overrides.
What FedEx’s AI tracking approach says about enterprise adoption
What stands out in FedEx’s approach is how narrowly focused the AI use case is. There are no broad claims about transformation or reinvention. The emphasis is on reducing friction in processes that already exist.
This mirrors how other large organisations are adopting AI internally. In a separate context, Microsoft described a similar pattern in its article. The company outlined how AI tools were rolled out gradually, with clear limits, governance rules, and feedback loops.
While Microsoft’s case focused on knowledge work and FedEx’s on logistics operations, the underlying lesson is the same. AI adoption tends to work best when applied to specific activities with measurable results rather than broad promises of efficiency.
For logistics firms, those advantages include fewer delivery exceptions, lower return handling costs, and better coordination between shipping partners and enterprise clients.
What this signals for enterprise customers
For end-user companies, FedEx’s move signals that logistics providers are investing in AI as a way to support more complex shipping demands. As supply chains become more distributed, visibility and predictability become harder to maintain without automation.
AI-driven tracking and returns could also change how businesses measure logistics performance. Companies may focus less on delivery speed and more on how quickly issues are recognised and resolved.
That shift could influence procurement decisions, contract structures, and service-level agreements. Enterprise customers may start asking not just where a shipment is, but how well a provider anticipates problems.
FedEx’s plans reflect a quieter phase of enterprise AI adoption. The focus is less on experimentation and more on integration. These systems are not designed to draw attention but to reduce noise in operations that customers only notice when something goes wrong.
(Photo by Liam Kevan)
See also: PepsiCo is using AI to rethink how factories are designed and updated
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
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