Hoto exists because someone got bored.
Artificial Intelligence
First there was nothing, then there was Hoto and Fanttik
CEO Lidan Liu, the company’s founder and a notable industrial designer, tells The Verge she was tired of advising from her consultancy Designaffairs China. I have to build something on my own, she thought. And back then, she was spending a lot of time in her workshop surrounded by the same old tools.
“The tool industry, it never changes, the products are boring, very masculine and very much designed for professional users,” Liu thought. “We can start something new.” So she founded iMonkey Technology, which later became Hoto, short for “Home Tools.”
She didn’t have to build it alone. She knew a cofounder of Xiaomi, one of China’s biggest companies, from when they served together on design award jury panels. She texted Liu De in 2016, asking to meet. He encouraged her to join Xiaomi’s supplier incubator program, which opened up countless doors. She could develop her own relationships with Xiaomi’s component suppliers, and sell her first products under the Xiaomi Mijia (“Mi Home”) brand, in exchange for a 10 percent stake in her startup.
She pitched Xiaomi’s cofounder ideas every two weeks for nearly six months. Hoto’s first product became a three-way collaboration. In 2017, Liu’s team designed a stylish slim screwdriver set with 24 bits from Wiha, a top-shelf German screwdriver brand, and a pop-out magnetic case to gently hold each bit. The product was emblazoned with the Xiaomi and Wiha brands.
But Xiaomi, skeptical of the results, only ordered 5,000 sets for its launch. “A lot of people told us it isn’t going to work, it’s so niche, it looks so different and weird,” Liu says.
“And in seven seconds, it sold out,” she tells me.
By 2020, Hoto was selling five Xiaomi products, including powered versions of its screwdrivers, plus the first tools under its own brand. As of today, Hoto has sold over 4 million pieces of Xiaomi-branded gear — and roughly another 5 million pieces of its own. In the past year, sales have more than doubled.
Fanttik — a play on “fantastic,” not “fanatic” — had help getting started, too.
By 2021, its parent company, Aukey, seemed poised to become the next Anker — the first Chinese electronics company to become a big brand in America by starting with high-quality phone chargers.
But that summer, Aukey was among the 600 Chinese brands Amazon permanently banned for review fraud. Some of those companies were caught bribing customers to leave positive reviews on Amazon or to delete negative ones.
The ban cost Aukey hundreds of millions of dollars in lost revenue, and all its profit for the year — it recorded a net loss of $90 million in 2021, according to public records. But it didn’t have much trouble bouncing back, because the flagship Aukey brand was just one of Aukey’s nearly 300 brand names.
Aukey was and is a massive “cross-border” company designed to sell huge quantities of Chinese products abroad. It operates dozens of warehouses in the US and Europe, ships packages for 700 e-commerce companies beyond its own, and works with over 500 different manufacturing partners to produce the products it sells. The majority of its revenue comes from selling furniture, not electronics or tools — it hawks inexpensive beds, bookshelves, and buffets on Amazon, Wayfair, and Walmart, under brands like Allewie, Sha Cerlin, Homfa, Likimio, Hostack, Fotosok, and Keyluv.
Following “the Amazon Incident,” as the company calls it, Aukey renamed itself to AuGroup, refocused on furniture, and publicly said it wouldn’t put more resources behind banned brands. But what about the 5 percent of its business dedicated to power tools? It consolidated those under a subsidiary with a new brand-first strategy: Fanttik.
Before the incident, Fanttik primarily distributed other companies’ tools. But in 2021, the company’s CEO, Bo Du, rebooted the entire division, downsizing its staff to around 150 people and building product and brand development teams, he told an audience at the FastMoss Global Short Video Conference in January.
Four years later, Fanttik tells The Verge it has sold approximately 5.5 million pieces of gear.
While many Chinese brands never escape the orbits of Amazon and AliExpress, both Hoto and Fanttik are poised to become US household names. They’re at Best Buy, Costco, and Walmart, and they’re all over TikTok, selling cordless drills, air pumps, and motorized kitchen scrubbers that look like they belong on the Starship Enterprise.
They aren’t exactly inventing that Star Trek future, though. Unlike, say, Dyson, they’re not creating ever more powerful mini motors themselves. Hoto and Fanttik’s patents are primarily ornamental.
In interviews, execs at Hoto and Fanttik tell me the same basic thing: Design and quality are the utmost priority. Both say their largest team is their product team, “because we believe the best product is the reason we’ll win the market,” as Fanttik marketing director Zoe Wei puts it.
Often, I’m impressed by the quality and design, enough that I’m giving out mini Hoto spin scrubbers this Christmas and am seriously considering a Fanttik vacuum. But after trying a wide selection of their gear, I’m not always convinced these “design-first” companies think their flashy ideas through.
The biggest selling point of Hoto’s Snapbloq is the magnets. You can snap a miniature drill, rotary tool, and screwdriver kit together to carry around like a miniature sci-fi tool chest. But Hoto’s magnets simply aren’t strong enough to hold the weight of these tools together. Liu tells me more Snapbloqs with upgraded magnets are coming, but it’s a surprising oversight.
I also quickly stopped using Fanttik’s electric scissors I keep seeing all over social media because they often get stuck; I almost always need to reach for a utility knife too. The company’s already started selling an upgraded version to address jams.
Hoto’s cordless leaf blower, an iF Gold Design Award winner, left me scratching my head the most. It transfers buzzy vibration to my hands, produces a shrill whine, and isn’t as powerful as the competition. When the leaves began to hit the pavement this fall, I discovered my unit no longer powers on. Liu admits that the battery was “not strong enough” and says Hoto will replace them immediately when customers ask.
Both Hoto and Fanttik execs say they don’t compete with one another. Yet I’m dead certain these companies are competitors — each stealing the other’s good ideas as fast as they can.
In 2023, for example, Hoto released the “Compressed Air Capsule,” a design striking and clever enough that the Museum of Modern Art added it to its purchasable collection, and one Indiegogo campaign tried to pass off the idea as its own. It’s a compact vacuum, air duster, and inflator all in one, letting you attach nozzles and filters at either end to suck or blow, respectively. At the time, Fanttik’s rival compact vacuum could only suck — but its very next model was bidirectional like Hoto’s.
When I ask Wei, the marketing director at Fanttik, why it markets its handheld vacuums to car owners rather than for the home, she says it’s because the compact gadgets don’t have enough room for a home’s dirt. I quickly find Hoto’s design is even weaker that way — constantly unscrewing and emptying its tiny bin is a chore, not to mention how its nozzle frequently falls out.
A few months later, Hoto introduced its first “autocare” vacuum with a larger dirt compartment that flips open with the press of a button. It’s almost exactly like Fanttik’s design, and with a far more secure nozzle.
One day, I noticed Hoto had introduced its own version of the Fanttik electric scissors, and Fanttik suddenly had a cordless leaf blower and a collection of motorized kitchen scrubbers like Hoto. This spring, both companies released their Dremel-like miniature rotary tools around the same time; this fall, they both launched pivoting spin scrubbers that let you change your angle of attack with the push of a button.
One of the open secrets to Fanttik’s success is paying for reach. It spends millions to sponsor NASCAR drivers, UFC fighters, and NBA teams to build its brand — and it’s attracted a veritable army of over 31,000 TikTok creators competing to make its next screwdriver look like the coolest thing ever.
This January, Fanttik CEO Bo Du revealed that 30 percent of the company’s sales come from TikTok alone. In just two years, it sold $25 million in tools that way. As of today, that number may be as high as $40 million, analytics company FastMoss estimates. The overwhelming majority of those sales and views come from creators, not Fanttik’s own channel.
In one particularly successful video, a bearded man rushes the camera wide-eyed — “Ladies, mothers, wives: stop!” — before telling them to buy a Fanttik screwdriver for their husband’s or father’s next Christmas present. The video got 21 million views and sold 13,000 screwdrivers, according to FastMoss estimates. (The caption does not indicate that it’s a paid ad.)
In another video that sold an estimated 7,000 air pumps, a man pretends to rescue a lady whose Ford F-150 has “broken down” by handing her a Fanttik tire inflator. Never mind that her tire is already fully inflated before they begin: 24 million views, 7,000 estimated sales. (Again, no advertising disclosure, though TikTok does display an “Eligible for Commission” tag on such videos.)
While both men seem to be equal-opportunity gadget influencers, Fanttik’s most successful TikTok salesperson has a different strategy: WrappedOfficial posts the same basic Fanttik vacuum and screwdriver videos again and again, but with tweaks. Many get just a few hundred views, but enough strike TikTok algorithm gold that the channel generated $2 million in sales across 51,000 orders, according to FastMoss estimates.
Fanttik offers 6 to 8 percent commission on most of its products; TikTok creators can earn roughly $3 to $5 per sale. That means a single viral video could bring in tens of thousands of dollars for its creator.
It’s enough incentive that new Fanttik videos now hit TikTok multiple times an hour, and it’s relatively cheap for the company to maintain: The company has just 30 staffers dedicated to TikTok, yet over 90 percent of Fanttik’s 1.1 billion views on the platform come from creators, brand manager Josh Shi tells me.
When I ask Hoto’s founder whether she plans to commission TikTok creators too, she immediately says yes — it’s part of the plan for 2026. Liu says, “We’ll do much more. Our competitor Fanttik is doing very well, their marketing skill is much better. We’re building a marketing team, sales team, brand team, so we will improve. We will do more.”
While they might seem similar, Fanttik and Hoto don’t operate quite the same way. Thanks in part to the Xiaomi partnership, China is one of Hoto’s biggest markets; Fanttik primarily sells to North America and is just beginning to experiment in its home country.
They build their overseas reputations quite differently, too: While Fanttik is always telling the world about its sports sponsorships, Hoto only issues an English press release when it has yet another design award to share — 20 iF Design Awards, 15 Red Dot awards, and 17 of Japan’s Good Design Awards at last count.
Those accolades helped Hoto break into the United States. Liu says her team knew it was time to go global once trendsetting fashion brand Supreme and the Museum of Modern Art reached out to Hoto.
Even Fanttik seems impressed with Hoto’s design chops. “Hoto’s design is quite good,” says Wei, the company’s marketing director. “Maybe customers who buy Hoto care more about the design […] customers who buy our product maybe care more about the power.”
But Fanttik has the resources to pursue design awards, too. The company tells me it has around 1,000 employees, nearly four times as many as Hoto. While some of its earliest successes (tire inflators) were at least partially designed by a different company, iRiding, Fanttik’s in-house team has racked up many iF Design Awards and Red Dot awards of its own.
Still, it sometimes feels like Fanttik is focused on volume rather than taste. It sells five different mini electric screwdrivers, six full-size versions, and four pistol-grip drivers, so many that the company publishes a “shopping guide” to help buyers tell them apart.
To be everything to everyone, it also sells 12 different air pumps — not counting the pumps that double as vacuums — by designating some of them for compact cars, some SUVs, some pickup trucks, some just for bicycle tires, and some for “outdoor enthusiasts.”
And those categories are just the start: It sells kiddie ride-ons, a robotic pool cleaner, a mini chainsaw, and an array of camping tents and tables, too.
It’s also aping Dyson with its own versions of the Supersonic hair dryer and Airwrap hairstyler — though my family didn’t find Fanttik’s hair dryer any better at drying than the other Dyson knockoff we already own.
Hoto, meanwhile, has fewer than 35 products on its entire website and a staff of roughly 300, whom Liu tasks with a narrower remit. “We don’t touch food,” she says, telling me how Hoto once canceled a line of kitchen products, including a blender; won’t do a yard trimmer because it’s too much of a pro tool; and canceled some 40V power tools because they were too heavy for everyday use.
“Tool companies cannibalize themselves,” she says. “We define ourselves more as everyday home tools.” Liu explained her team learned to focus after many previous failures: They were tempted to produce all sorts of products for Xiaomi’s storefront, including an award-winning set of nail clippers, a frying pan, even a flatware set, but decided to stick to tools once she launched the Hoto brand. “We didn’t have the certainty of our strategy and our goal.”
While the companies claim they don’t compete, Hoto and Fanttik admit they’ve crossed paths. Hoto says it partnered with Fanttik early on to learn how to sell on Amazon, and both say they respect one another to this day. “We are thankful for having Fanttik, we can learn from them,” Liu says.
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In a recent blog output, Rackspace refers to the bottlenecks familiar to many readers: messy data, unclear ownership, governance gaps, and the cost of running models once they become part of production. The company frames them through the lens of service delivery, security operations, and cloud modernisation, which tells you where it is putting its own effort.
One of the clearest examples of operational AI inside Rackspace sits in its security business. In late January, the company described RAIDER (Rackspace Advanced Intelligence, Detection and Event Research) as a custom back-end platform built for its internal cyber defense centre. With security teams working amid many alerts and logs, standard detection engineering doesn’t scale if dependent on the manual writing of security rules. Rackspace says its RAIDER system unifies threat intelligence with detection engineering workflows and uses its AI Security Engine (RAISE) and LLMs to automate detection rule creation, generating detection criteria it describes as “platform-ready” in line with known frameworks such as MITRE ATT&CK. The company claims it’s cut detection development time by more than half and reduced mean time to detect and respond. This is just the kind of internal process change that matters.
The company also positions agentic AI as a way of taking the friction out of complex engineering programmes. A January post on modernising VMware environments on AWS describes a model in which AI agents handle data-intensive analysis and many repeating tasks, yet it keeps “architectural judgement, governance and business decisions” remain in the human domain. Rackspace presents this workflow as stopping senior engineers being sidelined into migration projects. The article states the target is to keep day two operations in scope – where many migration plans fail as teams discover they have modernised infrastructure but not operating practices.
Elsewhere the company sets out a picture of AI-supported operations where monitoring becomes more predictive, routine incidents are handled by bots and automation scripts, and telemetry (plus historical data) are used to spot patterns and, it turn, recommend fixes. This is conventional AIOps language, but it Rackspace is tying such language to managed services delivery, suggesting the company uses AI to reduce the cost of labour in operational pipelines in addition to the more familiar use of AI in customer-facing environments.
In a post describing AI-enabled operations, the company stresses the importance of focus strategy, governance and operating models. It specifies the machinery it needed to industrialise AI, such as choosing infrastructure based on whether workloads involve training, fine-tuning or inference. Many tasks are relatively lightweight and can run inference locally on existing hardware.
The company’s noted four recurring barriers to AI adoption, most notably that of fragmented and inconsistent data, and it recommends investment in integration and data management so models have consistent foundations. This is not an opinion unique to Rackspace, of course, but having it writ large by a technology-first, big player is illustrative of the issues faced by many enterprise-scale AI deployments.
A company of even greater size, Microsoft, is working to coordinate autonomous agents’ work across systems. Copilot has evolved into an orchestration layer, and in Microsoft’s ecosystem, multi-step task execution and broader model choice do exist. However, it’s noteworthy that Redmond is called out by Rackspace on the fact that productivity gains only arrive when identity, data access, and oversight are firmly ensconced into operations.
Rackspace’s near-term AI plan comprises of AI-assisted security engineering, agent-supported modernisation, and AI-augmented service management. Its future plans can perhaps be discerned in a January article published on the company’s blog that concerns private cloud AI trends. In it, the author argues inference economics and governance will drive architecture decisions well into 2026. It anticipates ‘bursty’ exploration in public clouds, while moving inference tasks into private clouds on the grounds of cost stability, and compliance. That’s a roadmap for operational AI grounded in budget and audit requirements, not novelty.
For decision-makers trying to accelerate their own deployments, the useful takeaway is that Rackspace has treats AI as an operational discipline. The concrete, published examples it gives are those that reduce cycle time in repeatable work. Readers may accept the company’s direction and still be wary of the company’s claimed metrics. The steps to take inside a growing business are to discover repeating processes, examine where strict oversight is necessary because of data governance, and where inference costs might be reduced by bringing some processing in-house.
(Image source: Pixabay)
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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.
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