2026-04-16 23:08:18
Over the past few years, I’ve been noticing something odd in restaurants.
Customers are waiting longer—not just for food, but for simple things like drink refills or even getting someone’s attention.
At the same time, there’s less interaction at the counter. In some places, no one is really “taking” orders the way they used to.
At first, I thought this was just part of the usual staffing challenges. But then I saw something more concerning - restaurants around me, including ones run by friends, starting to shut down.
Not because they lacked customers, but because the economics stopped working.
Rising labor costs were eating into margins, and operational overhead kept growing. Even busy restaurants weren’t immune.
Most people assume the kitchen is where things slow down.
But during a rush, watch what actually happens.
The line builds up before the order even reaches the kitchen:
That front-of-house chaos is something restaurants have just - accepted for years. It is the waiter’s job
There’s a lot of talk about a waiter’s experience. But operators I’ve spoken to are dealing with more than that:
So the problem isn’t just “we don’t have enough people.”
It’s more like: “The current way we take orders doesn’t scale well under pressure.”
For years I realized, restaurant software systems including - order processing, billing, kiosks worked fine.
But the challenges for the front-of-house almost stayed the same.
Restaurant owners have to scramble to find waiters to balance peak hours. They spend more on training their waiters only to find them moved out after few months. This is not an investment.
The problem starts here, then the ripple effect that slows down operations and brings down profit margins.
If you can make ordering optimized, everything downstream improves.
Kiosks were the ultimate solution. But they weren’t a complete solution suiting restaurants of all sizes.
Huge investments and up-front costs. Most of them were bulky that occupies significant space within indoors.
Most importantly, restaurant owners found it not a great ROI.
As someone with a background in software engineering, this felt familiar.
One-size-fits-all solutions rarely work in complex systems. What works at scale for one setup doesn’t always translate well to another.
Restaurants aren’t that different.
Lately, I’ve been seeing a shift toward lighter, more flexible approaches to ordering—systems that don’t depend on heavy hardware and can run across different types of devices.
Some of these are starting to incorporate AI-driven flows, trying to make ordering feel less rigid and more adaptive.
It’s still early, but it feels like a natural evolution from the kiosk model.
Whenever this comes up, there’s always concern:
“Are these systems replacing people?”
If you strip away the buzzwords, this isn’t about “AI” or “automation.” The tools are changing, but the goal hasn’t.
Restaurants don’t suddenly operate without staff. Instead, they shift where people spend time:
One thing that doesn’t get talked about enough is user engagement.
Softwares are built for input vs output, but then, AI changes the way humans interact with machines. Maintaining customer engagement with ordering plays a very important role in ordering.
In a high-volume environment, small inconsistencies add up to bigger problems:
An AI-driven kiosk doesn’t get tired or rushed. It presents a creative workflow, every time.
That alone increases user attention and reduces a surprising amount of operational noise.
That said, not all of this is smooth. A lot of restaurant tech depend heavily on:
When something fails, it’s not always graceful.
And for smaller restaurants, downtime during peak hours isn’t acceptable.
That’s why there’s a growing focus (even if it’s not marketed loudly) on:
Restaurants aren’t suddenly going fully automated. Delivery bots do exist. Delivery drones are currently being tested.
What’s actually happening is one step at a time:
Technology alone doesn’t solve the problem. Neither does staff alone. What seems to be working is a combination of both—people and tools complementing each other.
For now, it’s a hybrid model. And it’s likely to stay that way for quite some time.
The shift happening right now is easy to miss because it’s not dramatic.
No big announcements. No overnight transformation. Just small changes in how orders are taken, one restaurant at a time.
But those small changes add up.
And over time, they quietly reshape how restaurants operate not by removing people, but by making the system around them work a little better.
:::tip This article is published under HackerNoon's Business Blogging program.
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2026-04-16 22:21:59
Singapore, Singapore, April 15th, 2026/Chainwire/--CredShields, a full-stack security firm specialising in blockchain and traditional security, with expertise in smart contract audits, AI-powered risk detection, and continuous monitoring, today announced it has joined the Canton Network as an official Audit Partner.
Canton Network is the public, permissionless blockchain purpose-built for institutional finance. Trusted by major financial institutions, the network processes over $8 trillion in tokenised transaction volume monthly and settles more than $350 billion in on-chain U.S. Treasuries daily.
As institutional finance accelerates its move on-chain, the security and audit requirements are both urgent and technically demanding. Canton's configurable, sub-transaction privacy architecture, where each participant controls precisely who can see what, creates an environment that standard security firms are not equipped to handle.
CredShields brings purpose-built capability: smart contract audits across Daml-based applications, AI-powered risk detection, and continuous monitoring designed for the on-chain environments where real institutional assets now operate.
The institutions built in Canton deserve security built for Canton. If you're building on Daml or Canton, CredShields, being one of the pioneers in the space, will help you build securely and ship confidently.
"Institutional finance is moving on-chain at a pace most people haven't registered yet. Canton is the infrastructure it's moving onto. The security stakes are significant and that's exactly the environment CredShields was built for." - said Shashank, Co-founder CredShields.
CredShields is a full-stack security company. The firm enables enterprises and blockchain protocols to build, deploy, and scale systems securely. CredShields is also the author of OWASP Smart Contract Top 10 and has been working with some top institutions, including tier-1 banks and financial institutions.
The Canton Network is the only public, permissionless blockchain purpose-built for institutional finance, uniquely combining privacy, compliance, and scalability. Governed by the Canton Foundation with participation from leading global financial institutions, Canton enables real-time, secure synchronization and settlement across multiple asset classes on a shared, interoperable infrastructure.
The open-sourced network is powered by its native token, Canton Coin, and supports decentralized governance and collaborative application development. It's the proven link between the promise of blockchain and the power of global finance, making finance flow the way it should. Users can learn more at: canton.network.
CredShields
:::tip This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program
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Disclaimer:
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
2026-04-16 21:32:46
A couple of weeks ago, we discussed why The Goal is for Your Startup to Become a Verb. The way Google is essentially synonymous with search, and Uber with ride-hailing, and so on.
That piece, much like today’s, draws from a simple idea: anchoring your product or service to user behavior.
I know, I know, it’s an almost painfully obvious idea. But sometimes, the simplest things are the easiest to overlook. So let’s slow down and sit with it for a second.
People don’t hail rides because they’re on the Uber app. They get on the Uber app because they want to hail a ride. People don’t search because they happen to be on Google. More often than not, they go to Google because they already want to search, and Google simply offers a dependable way to do it.
The businesses that win give users a better way to do something they ALREADY want to do.
Now, you might argue that some products come out of nowhere and create entirely new behaviors. And on the surface, that feels true. But if you look closely, you’ll see that those products are simply unravelling a desire that we might not yet have known how to articulate.
Take TikTok, for example. It didn’t invent the desire for short, entertaining content. That behavior was already there—on Vine, on Instagram, even in the way people consumed memes. What TikTok did was package that existing desire into something faster and easier to engage with.
This brings us to the main point of this piece: If your product exists to serve a behavior, then your messaging shouldn’t revolve around the product at all. It should revolve around the person acting on that behavior.
In other words, the story isn’t the product. It’s the user.
Most founders focus brand storytelling on the product/service itself. The features, the architecture, the technical breakthroughs that make it all work. They assume that if they explain it well enough, people will understand its value.
They won’t. Because people don’t engage with products because of what they are. They engage because of what those products allow them to become. A better designer. A faster developer. A more efficient team.
So if your content still revolves around what your product does, it might be time to flip the lens. Start with the outcome. Start with the transformation. Start with the person on the other side of the screen.
That said, before there’s a user story to tell, there must be users whose problems you’re a solution to. People who can vouch for the usefulness of what you’ve built. Because if the value isn’t immediately clear in practice, no amount of storytelling will make it stick.
Which raises a simple but uncomfortable question.
Talking about outcomes is easy. Proving them is where things get interesting. HackerNoon’s Proof of Usefulness Hackathon is built around that same idea, pushing builders to answer a very real question: Does your product actually solve a problem for real people? It’s one thing to declare what you want to be synonymous with. It’s another to prove it publicly.
If you’re building something meaningful and want to sharpen your positioning while competing for over $150,000 in cash prizes and software credits, this is a solid first step.
:::tip You can get started here: https://www.proofofusefulness.com/
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Of course, not all proof happens in a single event.
Every day, startups are putting their ideas to the test, trying to turn intention into something people actually use.
Tripcel is a travel-tech company that uses eSIM technology to keep users connected in over 200 countries without the hassle of physical SIM cards or roaming charges. By activating mobile data through a single QR code in minutes, Tripcel makes global connectivity seamless, affordable, and instant—turning what used to be a logistical headache into a plug-and-play experience for modern travelers.
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RoomsVital is a smart home and proptech startup focused on transforming existing door locks into retrofit smart locks—no replacements, no heavy installation. By combining app-based access, PIN control, and secure encryption, RoomsVital makes it easy for homeowners, renters, and property managers to manage access without the everyday friction of physical keys. The result is a simpler, more secure way to control who gets in, and when.
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Cosmonova Broadcast is a media-tech company that provides end-to-end infrastructure for launching, managing, and distributing TV and streaming content. From cloud-based playout and OTT platform development to IP delivery and monetization, they handle the full content lifecycle—helping broadcasters and content owners reach audiences faster, more reliably, and across every platform that matters.
:::tip Want to be featured? Share Your Startup's Story Today!
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2026-04-16 17:46:34
Stablecoins have moved from crypto native curiosity to serious financial infrastructure and nowhere is that shift more consequential than Asia, where dollar-denominated settlement sits at the intersection of regulatory complexity, booming Web3 adoption, and chronically underserved developer tooling.
\ Stables is betting it can own that infrastructure layer. I sat down with Bernardo Bilotta, Co-founder and CEO of Stables, to understand what they're building, why Asia, and whether the "Stripe for stablecoins" label actually fits.
\ Ishan Pandey: Hi Bernardo, welcome to our "Behind the Startup" series. Tell us about yourself and what led you to build Stables?
\ Bernardo Bilotta: We started Stables in 2021 because the stablecoin experience for consumers was terrible. I've spent the previous decade building fintech consumer products, including leading the global rollout of the Zip app to more than 10 million users as Head of Mobile at Zip Co. That background gave me a very clear understanding of what consumer-grade financial products look like, and stablecoin wallets and experiences in 2021 were nowhere close.
\ Hundreds of millions of people across Asia and emerging markets were already using USDT as their de facto digital dollar. But the products they were using looked and felt like they'd been built by and for crypto engineers. The wallets were clunky, the flows were confusing, and the entire experience assumed you already understood blockchain. We set out to build the best consumer stablecoin product in the market, something that felt like a proper neobank, not a crypto tool.
\ The problem was that there was nothing to build on. No off-the-shelf infrastructure for stablecoin payments existed, certainly not in Asia. No compliant on-ramp and off-ramp APIs. No unified compliance stack. No reliable corridors between USDT and local currencies. So to build the consumer experience we wanted, we had to build all of the infrastructure underneath it ourselves: the banking integrations, the KYC and AML pipelines, the transaction monitoring, the liquidity connections, the fiat settlement rails. Every layer, from scratch.
\ My co-founders brought the right pieces to make that possible. Daniel Li had built Readii to over $20 million in ARR and understood how to scale commercial operations. David Nichols is a lawyer with 20 years in banking risk and compliance, including co-founding Xinja Bank and leading risk at Commonwealth Bank of Australia. Between the three of us, we had the product instinct, the commercial engine, and the regulatory backbone to actually pull it off.
\ What we didn't anticipate was that the infrastructure we built to power our own product would turn out to be more valuable than the product itself. That realization came later, and it changed everything about our trajectory. But the founding impulse was simple: stablecoins deserved a consumer experience as good as the best neobanks in the world, and nobody was building it.
\ Ishan Pandey: Stables launched in 2021 as a stablecoin neobank. What changed internally, in the market or in your own thinking, that led you to open up your infrastructure to developers as a B2B API platform?
\ Bernardo Bilotta: We built the consumer wallet first because we needed to. There was no off-the-shelf infrastructure for stablecoin neobanking in 2021, certainly not in Asia. So we built everything ourselves: the compliance stack, the banking integrations, the on-ramp and off-ramp corridors, the KYC flows, the transaction monitoring. All of it, from the ground up.
\ What changed was the realisation that the hardest part of what we'd built wasn't the wallet interface. It was the infrastructure underneath it. And every other company trying to build on stablecoin rails in the region was hitting the same wall we'd already climbed over: fragmented banking relationships, jurisdiction-by-jurisdiction compliance, unreliable liquidity, and months of engineering just to get a single corridor live.
\ The pivot wasn't a pivot in the dramatic sense. It was more like looking at what we'd built and recognising that the infrastructure was the product. The wallet was a proof of concept; the rails were the business. Once we opened those APIs to other developers, volume grew 8x in a matter of months. That told us everything we needed to know.
\ The market timing helped too. By 2025, regulatory clarity was arriving across key jurisdictions, institutional capital was starting to take stablecoins seriously, and developer demand for compliant, programmable rails had outpaced what any single provider could offer. We'd already built the thing everyone else was scrambling to piece together.
\ Ishan Pandey: You're specifically focused on building compliant USDT rails for the Asian Web3 market. Asia is the largest remittance recipient region in the world, yet you've said the stablecoin infrastructure there remains deeply fragmented. What structural problem are you solving that existing players have failed to address?
\ Bernardo Bilotta: The numbers tell the story pretty clearly. Only about 1% of local banks in Asia are willing to work closely with stablecoin businesses. There are roughly 150 currencies in the region that need stablecoin connectivity. And as of our last analysis, there were zero licensed orchestration platforms purpose-built for USDT in Asia. Zero.
\ That's the structural problem. Asia drives approximately 60% of global stablecoin payment flows, yet there's been a massive overinvestment in the US and Latin America when it comes to stablecoin infrastructure. The capital and the builder attention went west, and the region generating the most actual payment volume got left with duct tape and manual processes.
\ What existing players have failed to address is the full-stack problem. You've got on-ramp providers who are consumer-focused and not built for B2B developer infrastructure. You've got regional OTC desks that are manual and not API-first. You've got crypto payment processors optimised for different stablecoin ecosystems. None of them give a developer a single integration to move USDT in and out of local currencies across the region, with the compliance stack handled.
\ And the fragmentation compounds. Banks in Asia change their risk appetite with little warning, which means developers are constantly scrambling for alternatives. Every corridor is its own regulatory environment with its own capital controls. The result is that building a stablecoin payment product in Asia today feels like assembling a puzzle where the pieces keep changing shape. We built Stables to be the table the puzzle sits on.
\ Ishan Pandey: You've described what you call the "corridor trap", where every new payment corridor requires a new banking relationship, a new compliance stack, and a new integration timeline. How does Stables' infrastructure break that cycle technically, and how does it handle regulatory compliance without adding friction at the developer layer?
\ Bernardo Bilotta: The corridor trap is real, and it's the single biggest reason stablecoin businesses in Asia grow slowly. Without us, every new corridor means a new banking partner to negotiate with, a new compliance framework to build, a new integration to engineer and maintain. It's not a software problem. It's a business development and regulatory problem that happens to require software to solve.
\ Our API is built on three pillars. The Customer layer handles identity, compliance, and risk: KYC and KYB verification, real-time transaction monitoring and KYT, sanctions screening, and travel rule compliance for cross-border transactions. The Ledger layer handles reconciliation and auditability, tracking every debit, credit, and stablecoin-to-fiat conversion with a clean audit trail. The Transfer layer handles orchestration across both fiat and stablecoin rails, including virtual accounts, banking rails, on-chain settlement, and deep stablecoin-to-fiat liquidity.
\ For the developer, all three layers sit behind a single integration. They submit their end users via API. We handle the KYC, the compliance screening, the transaction monitoring. Their users can on-ramp from local currency to USDT or off-ramp from USDT to local currency in their bank account. New corridors become a configuration change, not an engineering project.
\ On the compliance side, we're multi-jurisdictionally licensed. We hold licences in Australia, Europe, and Canada today, with licensing in progress in the UAE and Singapore. Our structure allows us to expand into new jurisdictions by issuing products through existing licensed entities under unified global compliance policies. That means a developer integrating once gets access to our entire licensed corridor network without having to think about regulatory fragmentation.
\ The friction reduction is the product. If a developer has to spend engineering months integrating fragile banking partners, or if frozen funds and failed payouts are eroding their margins, or if compliance delays are damaging their end users' experience, then the infrastructure has failed. Our job is to make all of that invisible.
\ Ishan Pandey: You've processed over a billion dollars in retail transactions and support payments across 19 blockchain networks. Most stablecoin conversations focus on picking the right chain. You've argued that's the wrong question entirely. What should enterprises actually be asking when they build a stablecoin strategy?
\ Bernardo Bilotta: The chain question is a distraction. It's like asking which highway your delivery truck should use when the real question is whether the goods arrive on time, at the right cost, in a way the customer trusts. Enterprises get pulled into chain debates because that's where the marketing noise is. But the problems that actually kill stablecoin adoption in a business context have nothing to do with which L1 or L2 you settle on.
\ The questions enterprises should be asking are more fundamental. Can I move USDT into and out of the local currencies my customers use, reliably, at scale? Can I do it without building a compliance stack in every jurisdiction I operate in? Can I add a new corridor without a six-month integration project? What happens to my funds when a banking partner changes their risk appetite overnight?
\ This is exactly why our integration with USDT0 and LayerZero matters. USDT0 is Tether's omnichain standard, which means USDT can move natively across supported chains without the developer managing bridge infrastructure or multi-chain complexity. For our developers, USDT just moves. They don't need to think about which chain it lives on, because the orchestration layer handles that.
\ The enterprises that will win in stablecoin payments are the ones who stop optimising for chain selection and start optimising for corridor coverage, compliance reliability, and settlement speed. Those are the variables that determine whether a payment product works for real users or just looks good in a pitch deck.
\ Ishan Pandey: Stables has partnerships with Mastercard, Circle, Marqeta, and Coins.ph, and is now expanding into the UAE through Hub71. How do you see this infrastructure contributing to the broader convergence of traditional finance and blockchain-based capital markets — particularly in markets where only around 1% of local banks are willing to work with stablecoins?
\ Bernardo Bilotta: The convergence is already happening, and it's happening faster than most people in traditional finance realise. The question isn't whether fiat and stablecoin rails will merge. It's who builds the connective tissue between them.
\ Our partnership ecosystem reflects that conviction. We've built relationships across both traditional payment networks and the stablecoin-native ecosystem because the infrastructure needs to bridge both worlds. The UAE expansion through Hub71 is strategic. The Middle East is moving faster than almost any other region on regulatory clarity for digital assets, and Dubai in particular is becoming a hub for stablecoin infrastructure companies. That aligns with where we need to be as we scale our corridor coverage beyond Asia into the broader emerging market ecosystem.
\ As for the 1% banking problem, that's actually a competitive advantage for us rather than a barrier. Because so few banks are willing to work with stablecoins, the companies that have already built those banking relationships and wrapped them in a compliant, developer-friendly API have a moat that's extremely hard to replicate. Every banking relationship we hold is one that took months of trust-building to establish. A new entrant can't just spin that up.
\ Ishan Pandey: Given that USDT dominates stablecoin volumes in Asia, your platform seems naturally positioned to serve as connective tissue across Tether-based services in the region. Is unifying that ecosystem a deliberate strategic goal, or an organic outcome of what you're building?
\ Bernardo Bilotta: Deliberate. Completely deliberate.
We are USDT-native by design, not by default. Every partnership in our stack is USDT-aligned. Our integration with USDT0 and LayerZero. Our liquidity partnerships with Mansa and t0 Network. Our institutional rails partnership with eStable, which includes local stablecoin issuing backed by USDT and Hadron. The entire architecture is built around the stablecoin that actually dominates payment flows in Asia.
\ USDT isn't just the most popular stablecoin in the region. It's a $35 trillion payment network. Think of it as a global dollar highway. What Asia has been missing is the on-ramps and off-ramps that connect that highway to local economies in a compliant, programmable way. That's what we're building.
\ We have an active collaboration with Tether that gives us unmatched access to Tether's banking network, liquidity depth, and market credibility. That relationship isn't something we stumbled into. We pursued it because being deeply embedded in the Tether ecosystem is the single most important strategic position you can hold if you're building stablecoin infrastructure in Asia.
\ The way I think about it: in every major technology platform shift, the companies that align early and deeply with the dominant ecosystem capture disproportionate value. USDT is the dominant ecosystem in Asian stablecoin payments. We're building the infrastructure layer for that ecosystem. That's not accidental.
\ Ishan Pandey: Stables has been described as "the Stripe for stablecoins in Asia." Is that analogy accurate and what does it get right or wrong about what you're actually building?
\ Bernardo Bilotta: The analogy is useful shorthand, and it captures the developer experience we're going for. Stripe made it so any developer could accept payments with a few lines of code, and the entire complexity of payment processing, fraud detection, and compliance disappeared behind a clean API. That's exactly what we're doing for stablecoin payments in Asia. One integration. Full compliance stack handled. Deep liquidity. New corridors via configuration.
\ Where the analogy falls short is in the complexity of what we're actually orchestrating underneath. Stripe built on top of existing card networks and banking infrastructure that, for all its flaws, had been standardised over decades. We're building at the intersection of two financial systems that are still learning to talk to each other: traditional banking and stablecoin rails. We're not just processing payments. We're bridging fiat and stablecoin worlds, managing multi-jurisdictional compliance, maintaining deep liquidity across volatile FX pairs, and navigating banking relationships in a region where banks can change their stablecoin risk appetite overnight.
\ The other thing the Stripe comparison misses is the specific bet we're making. Stripe is payment infrastructure. We're money movement infrastructure. The underlying thesis is that cross-border flows, which are $850 billion globally to low- and middle-income countries, are migrating from legacy rails to USDT. The serviceable addressable market for USDT-addressable flows is growing at 19% CAGR and projected to reach $340 billion by 2030. The companies that own the orchestration layer for that migration will capture disproportionate value as the market scales from millions to billions of users.
\ So yes, we're the Stripe for stablecoins in Asia in terms of the developer simplicity we deliver. But the market we're going after and the infrastructure we're building underneath is a fundamentally different animal. We're processing over $1.3 billion in annualised TPV today, we're EBITDA positive, and we're growing at 466% year over year. The Stripe analogy is a starting point for understanding what we do. The numbers tell you how big this is becoming.
\ Don’t forget to like and share the story!
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2026-04-16 17:01:01
Generative AI is extremely appealing for non-technical users, who feel like they've gained access to magic powers. But as this story shows, a little knowledge is a dangerous thing.
Once upon a time, there was a Chief Data Officer who believed that the new wave of Generative AI, such as ChatGPT and Gemini, could bridge any technical hurdle. At a local AI meetup, they met a founder with no technical background who had proudly built an entire prototype website using nothing but natural language prompts. It seemed like a genie in a bottle. Rub it a little bit (give it tokens), make a wish (give your prompt), and *poof* you have code, text, or some description of what you need to do.
Every day, back in their own enterprise, this data leader watched as more team members treated these general-purpose tools as magical little helpers. However, as a data expert, the CDO began to worry about the "fuzziness" of the results. Unlike the Predictive AI they were used to—which is deterministic, consistent, and built on specific statistical mathematics—Generative AI was proving to be non-deterministic. If you gave it the same prompt twice, you’d get two different answers, making it a liability for rigorous production environments.
But then one day, the genie was asked to build a web site. It did so, but did not build all the infrastructure necessary to support a web site. The CDO realized that a little knowledge is a dangerous thing. While the Generative AI could only build a beautiful facade, it couldn't account for what the user "didn't know they didn't know."
As in days of old, the “it works on my machine” dragon was waking up. The user that wrote the prompt was solely responsible for judging the accuracy of output they didn't fully understand. The person making the wish (giving the prompt) would have had to have the expertise of a software engineer, the experience of a network engineer, and the judgement of a systems engineer to get the prompt just right and know if what was generated was ultimately the right thing.
As the data team scrambled to clean up the mess, the CDO thought, “maybe this GenAI isn’t so helpful after all.”
Because of that, the CDO began looking for a better way to manage cognitive load. General Purpose Generative AI (GPGenAI) is truly a genie in a bottle: it can be a powerful but unpredictable tool to those without the right vocabulary. They needed the AI to act less like a creative writer and more like a Subject Matter Expert (SME) that could produce reliable, stable, and consistent output.
That’s what a Domain-Specific Generative AI (DSGenAI) does. DSGenAI tools are highly specialized, with embedded capabilities for tasks, such as SQL generation within known data structures or Python-specific environment management.
Because of that, the organization moved away from relying on "general" generative AI for technical tasks. They recognized that for a tool to be useful in a professional data environment, it needed to be familiar with the specific packages and data architectures of the business. DSGenAI was like a genie that has grown up in a culture of hacking SQL, munging Python, and dealing with package management hell.
Finally, the CDO established a new framework for AI adoption. They now knew that while GPGenAI is excellent for MVPs and brainstorming, production-grade data operations require the precision of DSGenAI. They stopped asking if the AI was "smart" and started asking the people using the tools if the tool was "grounded" in their specific domain.
And ever since, the data team has acted as the ultimate judge of quality. They learned that even with the best DSGenAI, the user is still responsible for verifying that the generated code matches the request. By choosing tools designed for their specific vocabulary, like METIS from DataOps.Live and CoCo (Cortex Code) from Snowflake, they reduced the risk of "non-positive consequences" and ensured that their production environment was built on a foundation of expertise, not just probability.
2026-04-16 17:00:54
When OpenAI’s list of its top 30 customers by token consumption surfaced across social channels, the immediate reaction focused on who appeared on the list. But the more important insight came from the pattern: a mix of large, mature enterprises and fast-moving AI-native startups all consuming tokens at similar scales.
This wasn’t a leaderboard of experimentation. It was a snapshot of where cognition-heavy work is already being automated—and where AI has quietly become embedded infrastructure.
It’s also important to understand what this list does not capture. OpenAI token consumption represents only one slice of how leading organizations actually run AI in production. Many of the most sophisticated teams don’t concentrate usage in a single model or provider—they distribute workloads across multiple models and vendors based on cost, latency, context length, and task complexity. In that sense, this moment mirrors the early days of cloud adoption: the companies that extracted the most leverage weren’t the ones that picked a single hyperscaler, but the ones that designed systems flexible enough to evolve as infrastructure choices changed.
Even with that limitation, the list still reveals something essential. Across this group, token consumption correlates less with company size and more with how deeply AI is woven into real workflows. The organizations driving higher consumption are using AI to replace manual reasoning, not just accelerate isolated tasks.
That signals a deeper shift. Teams are beginning to measure leverage not by headcount, but by how much cognitive work can be offloaded to AI systems. The real competitive advantage comes from designing work so AI agents can operate as specialists—not simply as assistants.
Each of the top token consumers has different motivations and use cases. AI has become embedded inside real workflows that move organizations forward: the workflows that require reasoning, decisions, and customer-facing impact. That’s why it helps to break the pattern down by sector and by role to see where this shift is actually happening.
The list revealed how broadly AI has already moved into production. Each sector uses large volumes of tokens for a different category of cognitive work:
These workflows don’t look the same, but they share something important: they represent high-cost cognitive tasks that used to be bottlenecked by human attention, not compute.
Token-heavy workloads map directly to these categories—retrieval, reasoning, summarization, mediation, debugging—because each requires deep contextual understanding at scale.
The cross-industry spread is only half the story. Inside companies, the roles that consume tokens are just as revealing:
Across these roles, the same shift is visible: AI is no longer a layer on top of work; it is an operational backbone inside the work.
This diversity isn’t noise—it’s a clear signal. The organizations consuming the most tokens are delegating meaningful cognitive work to AI systems thousands of times per day, across functions and across the stack.
A closer look at the top token consumers reveals something more interesting than a startup-versus-enterprise divide.
What’s actually happening is a structural leveling of the playing field.
Generative AI is doing for software what cloud infrastructure did a decade ago: removing a category of constraint that once favored incumbents. Just as startups no longer needed to build their own data centers to compete, they no longer need massive teams of specialists to reason across complex systems, analyze failures, or iterate quickly on customer feedback.
The result is not simply faster execution—it’s a shift in who gets to compete.
New entrants can now operate with the same cognitive surface area as much larger organizations, because AI absorbs the work that used to require scale: context gathering, cross-system reasoning, analysis, and synthesis. Tokens, in this sense, are not about “who runs more AI,” but about how much cognitive terrain a team can cover.
AI-native startups aren’t just doing existing work faster. They’re questioning whether the work needs to look the same at all.
Because AI sits at the center of their architecture from day one, these teams aren’t constrained by legacy assumptions about how problems are solved. They’re free to reimagine entire workflows—not by building a better version of the same process, but by designing fundamentally different ones.
In practice, this means:
This is why small teams can now rival the output and impact of much larger organizations. It’s not that AI has “replaced” humans—it’s that AI has removed the historical penalties of being small.
High token consumption in these teams is a byproduct of this shift. It reflects constant exploration, reasoning, and iteration embedded directly into product and engineering processes.
Key takeaway: AI-native startups gain advantage not by automating humans out of the loop, but by escaping the constraints of how work used to be done.
Enterprises approach AI from a different starting point. They carry existing systems, processes, and organizational structures that can’t be rewritten overnight.
As a result, most enterprise AI adoption today focuses on augmentation:
This isn’t a limitation—it’s a strategic reality.
Augmentation allows enterprises to unlock meaningful gains without destabilizing core systems. And when done well, it enables teams to operate at a scale and level of complexity that would otherwise be unmanageable.
Where enterprises risk falling behind is not in how much AI they use, but in whether they treat AI as a surface-level efficiency tool or as a way to fundamentally expand what their teams can reason about and act on.
Key takeaway: The competitive gap isn’t between startups and enterprises—it’s between teams that use AI to rethink how problems are solved and those that use it only to optimize existing workflows.
Seen through this lens, token consumption is not a proxy for “AI taking over work.”
It’s a signal of how much cognitive work an organization is able to engage with—how many scenarios it can explore, how much context it can reason over, and how quickly it can adapt.
That’s why tokens per employee matter more than raw volume. It reflects how much leverage each person has, not how automated the organization is.
The real transformation isn’t AI execution versus human execution. It’s constraint removal versus constraint preservation—and that’s the shift reshaping competition across software today.
The leaderboard serves a useful purpose: it shows which companies are running the most AI workloads. But total token consumption alone doesn’t tell you whether that usage is valuable, efficient, or strategically sound. A company can burn millions of tokens without changing how it operates—or deploy billions in a way that fundamentally reshapes how work gets done.
For engineering leaders, the more revealing question isn’t how many tokens the organization consumes. It’s how effectively tokens amplify human judgment and execution. The goal isn’t to replace people with AI, but to increase how much meaningful work each person can responsibly orchestrate through AI systems. That’s where durable outcomes show up: lower MTTR, more stable releases, faster iteration, and better customer experiences without linear headcount growth.
Tokens aren’t abstract units—they’re the atomic measure of machine-executed cognition. Each token represents a small unit of reasoning, retrieval, synthesis, comparison, or decision-making performed by AI for humans.
In practice, token-heavy workflows map to work that was historically expensive and slow:
When these workflows are well-architected, token consumption correlates more closely with delivered value than with raw activity. The system isn’t “thinking more” for its own sake—it’s removing cognitive bottlenecks that previously constrained teams. This shows up as shorter delivery cycles, smoother handoffs, and fewer delays caused by manual context gathering or analysis.
Total token consumption tells you how much work the system is doing. Tokens per employee reveal how work is distributed between humans and AI—and whether that balance is healthy.
More tokens per employee aren’t always better. Too few, and teams remain constrained by human bandwidth: decisions pile up, context is fragmented, and progress slows. Too many, and organizations risk letting AI make decisions without sufficient human oversight, increasing the chance of subtle errors, misalignment, or downstream risk.
The most effective teams operate in a sweet spot of AI–human leverage:
This is why tokens per employee is a better diagnostic metric than raw token volume. It reflects whether AI is being used to responsibly amplify human capability—not just automating for automation’s sake.
At that balance point, teams consistently see:
This dynamic is what drives what we refer to as the great flattening: smaller teams achieving impact that previously required far larger organizations—not because AI replaced people, but because it absorbed the most cognitively expensive parts of the workflow.
Viewing tokens through the lens of leverage rather than cost gives leaders a more straightforward way to assess AI maturity. The organizations seeing the strongest returns aren’t optimizing for token minimization or maximization—they’re optimizing for effective AI–human collaboration.
When that balance is right, improvements compound: customer-facing issues are resolved faster, releases stabilize, and teams gain confidence to move quickly without increasing operational risk. These outcomes create a direct line between AI adoption and business performance—and give leaders a practical benchmark to evaluate progress over time.
Tokens aren’t the price of experimentation. They’re the operating fuel of a new way of working—one where leverage comes from how intelligently AI and humans share the cognitive load.
Once AI stops being an experiment and becomes a core executor of work, the entire engineering system comes under pressure in ways that traditional scaling models never predicted.
AI-generated changes move faster than human review cycles. Agentic workflows introduce new dependencies and edge cases. And the pace of iteration increases not because teams grow, but because each engineer now orchestrates 10–100× more cognitive work through AI.
In other words: the moment AI starts running real production workflows, the old assumptions about pace, QA, and reliability break.
The challenges listed below are what companies at the high end of token consumption are currently facing.
When AI-driven code changes, experiments, and decisions continuously flow into production, familiar problems surface much earlier than they used to. Rapid iteration creates failure modes that previously only appeared at massive scale:
Issues that once required huge user bases or massive traffic now appear even in small teams—because throughput is no longer tied to headcount. AI accelerates delivery beyond what legacy QA, review cycles, and guardrails were designed to handle.
As soon as agents begin owning end-to-end tasks, they depend on accurate, current system context to reason correctly. When that context is incomplete or static:
Agents behave differently from humans—they don’t “work around” missing context or ambiguity. That means gaps in system understanding surface as reliability issues almost immediately.
Most QA, triage, and debugging workflows were built for human-driven change velocity, not autonomous or semi-autonomous systems. As AI-generated updates increase:
These bottlenecks aren’t a sign of poor engineering—they’re a sign that the environment has changed. AI-native velocity exposes weaknesses in traditional toolchains and processes far earlier than expected.
These challenges are not edge cases—they are structural outcomes of AI taking on real cognitive work in production. The companies consuming the most tokens are simply encountering them first—and showing that mature AI adoption demands new infrastructure, practices, and ways of working.
As AI moves into the critical path of core workflows, the companies consuming the most tokens are discovering a painful truth: traditional observability and QA aren’t built for continuous machine reasoning. Engineering teams must shift from “debugging code occasionally” to engineering reliability for nonstop, autonomous decision-making. These capabilities are the foundations required to operate AI at scale without sacrificing stability.
AI-driven systems need a complete, code-grounded view of how software behaves in production—a single model that connects repos, telemetry, user sessions, tickets, and logs.
When analysis is anchored directly in the codebase, AI can reason accurately about failures, dependencies, and user impact—eliminating hallucinated conclusions and accelerating triage dramatically.
With AI-generated changes flowing constantly, reactive reliability is no longer enough. Proactive safeguards such as automated regression detection, high-risk change identification, and early-impact signals before users feel degradation.
This shifts engineering from discovering issues late to preventing them early—critical when iteration speed outpaces human review cycles.
As workflows become more distributed and agent-driven, knowledge can no longer live in the heads of senior engineers. Auto-generated architecture maps, cross-service dependency insights, and self-service debugging context remove the dependency on institutional knowledge. This also enables junior engineers to resolve complex issues without constant escalation.
Continuous AI-led change introduces failure patterns that old debugging workflows can’t absorb. Modern reliability loops require code‑anchored evidence, centralized cross‑system context, reduced tool fragmentation, faster root‑cause analysis, and fewer repeat regressions.
Together, these create a feedback system that adapts to the velocity of AI, not the velocity of human-driven development.
For leaders, the lesson from the top token consumers isn’t “use more AI.” It’s that leverage comes from how work is structured—and how responsibility is shared between humans and AI over time.
The organizations getting outsized returns don’t flip a switch and hand everything to agents on day one. They start with humans firmly in the loop, use AI to absorb the heaviest cognitive load, and then deliberately reduce human intervention as workflows prove reliable, explainable, and repeatable. Everything else—lower MTTR, faster releases, fewer regressions—flows from that progression.
Across these organizations, a few patterns show up consistently.
High-leverage teams don’t treat AI as a sidecar or a magic replacement. They design workflows where:
Over time, agents move from assisting on isolated steps to owning larger portions of the workflow—but only once outputs are trustworthy and failure modes are well understood. This is how AI becomes an executor safely, not recklessly.
The most effective teams measure progress by outcomes, not novelty. Early on, humans remain deeply involved while teams track whether AI is actually creating leverage:
As AI systems demonstrate consistency, teams intentionally reduce manual touchpoints—freeing humans to focus on higher-order decisions instead of routine analysis. Tokens per employee become useful here not as a goal to maximize, but as a signal that AI is absorbing the right kind of work at the right pace.
Teams consuming the most tokens learned early that AI adoption isn’t a feature upgrade—it’s a shift in operating model. As AI takes on more responsibility, failure modes surface faster and on a larger scale.
The leaders who navigate this well invest early in:
This ensures that as human intervention scales down, trust scales up—without sacrificing reliability.
PlayerZero is not simply another AI tool provider—it operates using the same patterns as the top AI-consuming companies themselves. Its platform reflects deep AI integration, using meaningful token volume to model, reason about, and execute cognitive workflows that would traditionally require specialized engineers.
At its core, PlayerZero’s agents are designed to mirror how real teams work:
For enterprise engineering teams, the impact shows up quickly—faster issue resolution, fewer customer-facing incidents, more stable releases, and higher throughput without adding headcount. AI projects also reach time-to-value faster because the surrounding reliability system can keep pace with AI-driven velocity.
This pattern plays out across customers like Cayuse, a research management platform with more than 20 interconnected applications and a highly fragmented multi-repo architecture. Before PlayerZero, they relied on slow, reactive workflows to resolve customer issues. With PlayerZero, the team identifies and fixes 90% of issues before they reach the customer. Time to resolution dropped by 80%, junior engineers began handling investigations independently, and high-priority ticket volume declined—resulting in a noticeably smoother customer experience.
Cayuse’s transformation reflects a broader pattern: when AI-driven triage and root-cause analysis sit inside core engineering workflows, teams gain real operational leverage—measured in speed, reliability, and customer outcomes—not just in token consumption.
The list reveals a shift toward AI-driven work, where agents execute cognitive tasks with human oversight. The real metric to watch is tokens per employee, a proxy for how much work each person can offload and how quickly teams can deliver.
Meaningful AI adoption isn’t about experimentation—it’s about redesigning work so AI becomes a true executor, not just an assistant.
For engineering leaders navigating large-scale AI adoption, the real value shows up in metrics like velocity and operational efficiency. The next step is clear: enable AI-native workflows safely—without trading speed for stability.
Explore how PlayerZero helps teams scale AI adoption while maintaining reliability and customer trust.
