On April 2, U.S. president Donald Trump announced the launch of large-scale “reciprocal tariffs.” Once the news came out, corporate pricing, inventories, procurement rhythms, and market expansion plans were thrown into disarray. The U.S. and China quickly entered a cycle of retaliation and counter-retaliation, straining cross-border supply chains and plunging the entire world into unprecedented uncertainty.

Half a year later, countries were stuck at the negotiating table, while businesses were struggling to adjust their survival strategies under mounting cash flow pressure. In such an environment, any system that can reduce costs and improve cash flow flexibility has become more important than ever.

Precisely at this time, a mechanism that had been overlooked for more than 200 years was once again discussed: “duty drawback.”

“Many companies don’t even know that the tariffs they pay are refundable,” said Penny Chen, founder of Pax, a startup that uses AI to help companies reclaim taxes. In her interviews with clients, she sees the same thing over and over again: companies pay tens of billions of dollars in tariffs to the government every year, and only about 20 percent of that money is actually refunded. The remaining 80%—amounting to nearly $15 billion—just sits there unclaimed. “It’s free money left on the table!” she added with a sense of helplessness.

This huge disconnect unexpectedly became an entry point for Pax. With AI at its core, Pax uses algorithms to help companies identify 15% more refundable tariffs than traditional service providers, streamlining processes that would otherwise take more than six months to just over ten working days. For the first time, organizations are realizing that AI tools can transform tariff refunds from a burdensome process into an immediate source of cash flow.

Companies Don’t Have to Overpay Tariffs! But Who Is Eligible for a Refund?

A “duty drawback” is a refund administered by U.S. Customs and Border Protection (CBP) that allows businesses to recover import duties they have already paid; it is distinct from the more familiar income tax refund. Under U.S. law, any business that has paid duties on goods imported into the United States can recover part or all of the duties if the goods are subsequently re-exported, re-exported after processing, or destroyed within the United States, among other qualifying conditions.

The most common case comes from the manufacturing industry: if a company imports raw materials from overseas, processes them in the United States, and then exports the finished products, the tariffs previously paid can then be refunded in accordance with U.S. law. Another typical example comes from

Another typical example involves retailers and distributors: if a company imports merchandise from overseas and re-exports it without selling or using it in the United States, the duties previously paid can also be refunded under U.S. law.

Cross-border e-commerce companies and large retailers, which have grown rapidly in recent years, are also frequently eligible for tax refunds. Shipments moved from within the U.S. to overseas warehouses are considered exports. If a consumer returns shipped goods that are then destroyed in the U.S., duties on those goods can also be recovered.

In other words, many routine logistics operations, such as moving warehouses, returning goods, or destroying products—which on the surface may not seem directly related to revenue—can in fact be a source of substantial tax rebates. As long as companies can clearly track the flow of their goods, they can reclaim funds that already belong to them.

Complicated Processes and Outdated Tools: Why Traditional Tariff Refunds Are So Difficult

Although the system itself is not difficult to understand, the high complexity of implementation is the most challenging aspect for companies. Penny Chen explained that, in addition to different commodities and various import and export scenarios, a bigger headache is that the information required for tariff refunds is often scattered across PDFs, Excel files, and ERP systems. Companies are tasked with organizing data from piles of invoices, customs declarations, and logistic records—which use different formats and layouts—into the specific format required by government agencies, an extremely time-consuming process.

Due to this complexity, many companies choose to outsource to specialized service providers, but this does not necessarily make the problem any simpler. Traditional providers continue to rely on software that has been in use for more than 20 years, and the process still depends heavily on manual labor. Each case has to be manually entered and compared line by line, and a company applying for a refund for the first time may need to wait an entire year from the time it submits the relevant documents to when it actually receives the refund.

Because the process is so cumbersome and slow, many service providers are only willing to take on large clients with potential refunds of $100,000 or more per year. As a result, even though SMEs are eligible for tariff reimbursement, they are often unable to find anyone who is willing to handle their cases.

“This Is a Fun Math Problem!” How Pax Uses Algorithms to Help Companies Recover 20% More in Tariff Refunds

Chen’s earliest exposure to tariff refunds came when she worked as a researcher at Flexport. She quickly realized that the process involved extensive data cleansing and rule comparison. Through exchanges with industry experts, she sought to understand the market’s real pain points. “I found that everyone’s dilemma was almost exactly the same,” she said. “They were eligible to recover the tariffs they had already paid, but because they didn’t understand the system, they didn’t have the tools, or they couldn’t find providers who were willing to support them, they ended up with nothing.”

Penny Chen holds a Ph.D. from the Massachusetts Institute of Technology (MIT), where she specialized in algorithmic design. “From my point of view, tariff refunds are actually a very interesting mathematical problem—it’s just that no one has ever approached them algorithmically!”

Simply put, Pax is like TurboTax for corporate tariff refunds. TurboTax, the most widely used tax filing software in the United States, breaks down complicated rules into a standardized process that allows taxpayers to file returns with a single click. Pax aims to re-create the same experience for corporate tariff refunds, helping businesses reclaim tariffs without needing to understand the rules, organize the data, or waste time and money on a long, drawn-out process.

However, the first and most difficult step in achieving this is addressing a fundamental pain point: “fragmented data.”

Currently, service providers handling tariff refunds spend most of their time manually preparing data. To proceed to the next steps, companies often have to submit all necessary documents up front, requiring them to be organized in a uniform format. This stage alone demands a significant amount of time and manpower, and it’s the reason why most companies find their first experience with tariff refunds so frustrating.

Pax’s approach eliminates this “manual review” step. Companies no longer need to prepare any documents in advance; they simply provide the raw data to Pax, and the system automatically reads and extracts the relevant information, transforming unstructured data into structured, calculable formats, thus saving countless hours.

The next step is the algorithm. Chen and her team design their own algorithms that enable the system to identify which permutations will maximize tariff refunds. Many businesses, after submitting their cases to Pax for calculation, have been able to recover an additional 15% to 20% compared with the amounts previously determined through manual review.

The final step is actually submitting the information. After calculating the refundable amount using its algorithm, Pax has its in-house tax experts verify the results and then submits the documents directly to the government. Because Pax is authorized for U.S. submissions, the entire process eliminates the need for the traditional iterative submission process.
Under this model, processes that used to take six months to a year for companies can now often be completed in just 10–15 working days, resulting in a significant efficiency boost.

A Niche but Overlooked Market, Reimagined by AI

Tariff refunds have long been regarded as a fringe aspect of the trade system, but in reality, they are part of a system that has existed for over 200 years, involving the entire cross-border supply chain of import, processing, and export. Any company engaged in import and export activities may be eligible for refunds, meaning that the range of industries covered is far broader than generally imagined.

That’s why it’s a mature but fragmented market: even though more than a dozen service providers in the U.S. have been operating for years, the process is still highly dependent on manual labor, leaving a large volume of eligible refunds untouched for long periods—like forgotten “sleeping cash” waiting to be reawakened.

Pax tackled this problem by combining algorithms with experienced domain experts. Within just over a year of its founding, it was selected for Y Combinator, a leading Silicon Valley accelerator, received $4.5 million in early stage investment, and processed tariff refunds totaling around $10 million. Following Trump’s tariff announcement this year, demand from businesses surged, and Pax’s revenue tripled as a result.

With a team of fewer than 10 people at its founding, Pax achieved these results not only through the strength of its product but also thanks to favorable policy conditions. The evidence is clear: there is a huge and vastly undervalued market for “duty drawback,” and Pax is leading the way in addressing this gap.

Looking ahead, Penny Chen points out that the United States has adjusted the relevant laws and regulations numerous times. This system, which has existed since the founding of the nation, has been continuously revised over the past two centuries, becoming increasingly complicated as a result. “I think tariffs will only continue to increase,” Chen said. Amid supply chain restructuring and geopolitical tensions, it is difficult for companies to go back to the past, and the demand for tariff refunds is only likely to grow stronger.

In a rapidly changing world, “duty drawback” deserves to be better understood and more effectively utilized than ever before. Chen expects that through AI and automation, Pax can help businesses of all sizes transform what was once a cost burden into cash flow resilience, giving them greater control in the new economic landscape.

Looking back through history—from the steam engine to the computer, from the carriage to the automobile—every technological revolution has made humanity more efficient. But AI is different. For the first time, technology is directly replacing human thinking. As AI agents become widespread, even the act of “execution” will be automated. When technology shifts from being a “tool” to becoming a “competitor,” the speed and depth of this wave of impact will far exceed any previous industrial revolution.

Even more worrying is AI’s impact on the education system. For over a century, there has been a stable pathway from education to the workplace: graduating from school, taking an entry-level position, learning on the job, and gradually advancing through the ranks.

But the rise of AI is disrupting this pathway. Companies now prefer to buy a few more AI tools rather than invest time in training newcomers. The Federal Reserve Bank of New York warns that the U.S. unemployment rate for recent graduates climbed to 5.8%. Meanwhile, research by the Stanford Digital Economy Lab reports a sharp decline in employment among 22- to 25-year-olds, especially in software development, customer service, and clerical roles.

Yet what concerns me even more is this: where will those who are replaced go?

While most companies are busy using AI to cut costs and increase efficiency, another market with huge potential is emerging. McKinsey & Company predicts that by 2030, more than 400 million people worldwide will need retraining or career transitions. This means that for every person displaced by AI, another will need to return to the workforce. I believe this is not just a crisis—it could become the next trillion-dollar industry.

Amid this wave, countries around the world are taking action. Our government plans to train 200,000 AI professionals by 2028, building a workforce ready to meet industry demands. Japan has gone a step further: starting in 2024, it launched a five-year reskilling support program, investing a total of one trillion yen to help companies and workers relearn in the areas of AI application and digital transformation.

In the United States, startups are also entering this field. Inference.ai is developing an AI-driven, human-centered “employment infrastructure” designed to help displaced white-collar workers reenter the job market. The team began by focusing on high-demand positions such as machine learning—roles that have long faced talent shortages but have high entry barriers. The Inference.ai system functions like a “driving school for the AI era,” using AI to scan global job postings, break down required competencies, and build skill trees and personalized training maps.

Leveraging its proprietary GPU partitioning technology, Inference.ai enables thousands of participants to gain hands-on experience in real computing environments at low cost, guided by mentors from leading U.S. tech companies and AI-based coaching systems. Participants then use simulated question banks and AI interviewers to validate their skills and prepare for job applications.

Without any publicity, Inference.ai has already attracted more than 1,000 engineers and professionals to join its community, which continues to grow rapidly each week. This shows that “helping people become needed again” is emerging as a central theme of the new workplace.

The AI revolution is advancing quickly, and new forms of employment, education, and social order are already taking shape. To me, this is not merely a labor market crisis—it is a global experiment in how humanity can coexist with AI, a question that we must all take part in answering together.

In recent years, I’ve had many conversations with people from different generations. What’s striking is how much earlier—and how much more intensely—this sense of uncertainty is showing up. Many people have taken countless courses and earned every certification they could, yet still find themselves asking the same question: What does it actually mean to be ready?

These conversations have pushed me to rethink the learning paths we’ve long taken for granted. Traditionally, the sequence was clear: choose a major, spend years accumulating knowledge and skills, then enter the workforce and draw on what you’ve learned when real problems arise.

That model worked because industries evolved slowly and access to knowledge was expensive. If you didn’t prepare in advance, many doors simply remained closed.

But AI is fundamentally changing that assumption. Today, learning a new skill no longer requires years of upfront investment. As long as you have a sense of what you want to do, the relevant knowledge and tools can often be filled in later—with the help of AI. In that sense, knowledge itself is becoming inflated. Simply accumulating skills is no longer enough to create a lasting advantage.

Against this backdrop, the idea of “being fully prepared before you begin” feels increasingly outdated—and in some cases, inefficient. As we move from learn first, then apply to apply first, then learn, the real differentiator may no longer be how many skills you’ve mastered, but whether you’re clear about the problem you want to solve.

This reversal in learning order may feel counterintuitive, but it often leads to greater clarity. That doesn’t mean foundational knowledge is no longer important. Rather, it should function as a map—helping you identify good problems—rather than as the sole weapon you rely on.

Those who can identify meaningful problems early tend to see their learning efficiency grow exponentially. On the other hand, even vast amounts of knowledge can become scattered and unfocused if there’s no clear problem guiding it.

As we look ahead to 2026 and begin setting new learning goals, perhaps the better question to ask is this: What problem is worth solving in the coming year? When direction comes first, learning tends to follow naturally. And perhaps, this way of preparing for the future can make the year ahead feel more purposeful—and more exciting.

The reason is simple. AI now performs many of the skills that used to belong exclusively to trained professionals. If you spend your entire youth mastering a single, rigid skillset, by the time you finally gain expertise, there’s a good chance AI will already be doing it faster, cheaper, and at scale.

This isn’t to say expertise doesn’t matter — it does. But expertise alone is no longer your greatest competitive advantage. What will set you apart in the years ahead is flexibility: the ability to learn across domains and adapt when the world shifts under your feet. Over the next decade or two, industries we once thought were stable will be reshuffled. And that won’t stop just because you happen to be good at one thing.

I often tell students: don’t just learn knowledge — learn how to learn. It sounds abstract, but in the age of AI, this may be the most practical skill of all. AI can generate endless answers, but it cannot define the right questions. It can show you many possible paths, but it cannot decide which one you should walk.

That’s why the value of a generalist becomes even clearer. When your perspective is broader and your interests span multiple fields, you’re more capable of cross-disciplinary thinking — of combining ideas that don’t usually sit together to create something new. AI can give you all the pieces, but you need the ability to see patterns, make connections, and even challenge assumptions.

Looking back, my own career has unfolded the same way. I didn’t follow a straight professional track. I’ve been an athlete, a founder, an investor, and now I work deeply in education. These roles may seem unrelated, but precisely because I never confined myself to a single identity, I’ve been able to navigate major transitions and keep finding new directions.

So if you’re still in school, don’t rush to label yourself as “a finance person,” “an engineer,” or “a legal professional.” Instead, train yourself to pick up new domains quickly and apply your knowledge in more flexible, creative ways.

If I could leave you with one message as you step into the future, it would be this: don’t lock yourself into one specialty — build the ability to cross boundaries. Be a generalist, not a specialist.

Taiwan-based TherapiAI, founded by a group of AI experts and medical specialists, has built an AI platform that helps biotech companies, pharmaceutical manufacturers, and CDMOs (Contract Development and Manufacturing Organizations) save raw materials and speed up production lines—allowing pharmaceutical production to run at ten times the speed, no longer dragged down by cumbersome traditional processes.

The structural bottlenecks that have accumulated in the biopharma industry over the years are clear: fragmented experimental data, slow R&D processes, shortages of specialized manpower, and complicated, time-consuming regulatory documentation cycles. TherapiAI aims to solve these four major pain points. They have built AI infrastructure focused on CDMO and CMC (Chemistry, Manufacturing, and Controls) needs, enabling faster R&D, optimized process parameters, and accelerated documentation and compliance workflows across the board.

Just as smartphones require an operating system to allow apps to work together, TherapiAI functions as an “AI operating platform” for pharmaceutical companies: connecting fragmented data, automating complex workflows, and transforming expert knowledge into reusable AI agents. Work that once relied on specific doctoral experiences to move forward can now be accelerated and scaled.

Crossing from the Courts into Pharmaceuticals: Building Foundational Cross-Domain Capability

Therapi AI, formerly known as Akousist, was founded by Michael Han in 2018. Interestingly, its early work had nothing to do with biopharma. “We previously handled AI-driven automation of electronic case files for 36 courts across Taiwan,” Han explained. Using AI to help court professionals automate large volumes of routine documents was the difficult challenge they were focused on at the time.

This seemingly unrelated experience turned out to be a key capability that allowed Therapi AI to enter the biopharmaceutical industry. The challenges faced by pharmaceutical companies are, in fact, very similar to those faced by the courts: data scattered across ERP, equipment, and laboratory systems; departments seeing different sets of information; and clinical and experimental documents containing sensitive information that cannot leave secure environments.

Through its work with the courts, the Akousist team developed a rigorous approach and deep expertise that enabled AI to “act as an agent” for professionals—performing routine tasks under strict data-security and interoperability constraints. These are precisely the foundational structures most lacking on pharmaceutical production lines, and thus became one of TherapiAI’s core strengths.

However, to understand why this AI infrastructure matters, we must first return to the question: what are the pain points of CDMOs?

The Four CDMO Pain Points: Data, Process, Manpower, and Regulations

First is the fundamental issue of data silos. On pharmaceutical manufacturing floors, critical experimental data is often scattered across instruments, paper records, and systems used by different departments. Formats are incompatible, and it is difficult to compare everything on a single platform.

Second, the high variability of cell and drug processes makes scale-up especially challenging. These processes are extremely sensitive to parameters and environments, where even tiny deviations can cause multi-million-dollar experiments to fail. While cells exist in relatively simple conditions in laboratory settings, once they enter large reactors, every parameter must be recalibrated. Pharmaceutical companies often rely on repeated trial and error and additional batches of raw materials to identify a stable process for production.

The third pain point is the manpower bottleneck created by heavy reliance on experts.

CDMO projects are complex and lengthy, taking an average of 18 months just to sign a contract. Much of the workload falls on a handful of senior VPs, BDs, and PMs. Take PMs as an example. CDMOs commonly face shortages of experienced PMs, high turnover rates, and overwhelming workloads. Even more challenging, key process know-how often sits only in the minds of PhDs, so when a core PhD leaves, the entire line has to be rebuilt from the ground up.

The final and most difficult pain point is data sensitivity and regulatory pressure.

In CDMO operations, much of the data is highly confidential and inherently unsuitable for the public cloud. A deeper issue is that “the whole industry shares a common misconception: that the first step to adopting AI is to centralize all the data,” Han explained. “But centralized data processing is slow, expensive, easily costing millions, and it may not even be effective.” As a result, although most pharmaceutical companies understand the potential benefits of AI, many prefer to maintain the status quo rather than take risks.

What Does TherapiAI Do? Building the AI Operating Platform for the Biopharmaceutical Industry

TherapiAI’s technical architecture consists of two core layers: the underlying “Knowledge Layer Model” and the front-end “AI agents.” At the knowledge layer, the team integrates publicly available global datasets with CDMO internal production data, enabling AI to genuinely understand highly specialized pharmaceutical knowledge. Such adjustments turn the models into a credible base for pharmaceutical companies and lay the groundwork for subsequent automation and application capabilities.

On top of this foundation, TherapiAI goes on to build AI agents that can “actually do the work.” These agents are not designed to serve a single function but rather to address the three interlocking stages of the pharmaceutical process: research, exploration, and exploitation.

First of all, in the research stage, AI must follow a “better none than wrong” principle. This working environment demands high precision and has nearly zero tolerance for error. If the model lacks sufficient supporting data, it will simply respond with “I don’t know,” avoiding incorrect inferences. This allows researchers to treat AI as a trustworthy information partner rather than a black box requiring constant verification.

Next is the exploration stage, during which researchers seek not just answers but AI-assisted reasoning. At this point, the AI agent uses its built-in knowledge and cross-system data to propose possible parameter ranges, hypothesis paths, or potential causes of anomalies, helping researchers shorten experimental iteration cycles. This marks the key transition from “looking up information” to “thinking together.”

Finally, in the exploitation stage, AI formally “enters the production line,” transforming research outcomes into operational workflows—such as performing cell-line screening; automatically generating contracts, process documents, and GMP reports; and querying FDA regulations—all directly addressing CDMO pain points.

For pharmaceutical companies, research directions converge faster, manufacturing can identify stable parameters with fewer raw materials, and regulatory teams can reduce document turnaround and revision cycles. Tasks that once required weeks of cross-checking by senior PMs or PhD-level personnel can now produce a preliminary version in minutes, ready for expert review.

Han notes that TherapiAI’s core is stripping away all non-domain noise from large language models, leaving only pharmaceutical-relevant knowledge. Combined with its three-stage agent workflow, the model not only “answers questions” but can “actually get work done” on R&D and production floors. That is why TherapiAI offers not just standalone tools but AI infrastructure that compresses the entire development cycle to a fraction of its previous length.

Practical Applications of AI Agents: Accelerating “Magic Bullet” ADC Drug Development and Tracking Regulatory Changes

TherapiAI’s AI agents are already being applied in a number of highly specialized domains. Among them, the most representative case involves the development of ADCs (Antibody-Drug Conjugates), a field that has drawn much attention in recent years. Because ADCs can precisely deliver payloads without damaging normal cells, they are regarded as a major breakthrough in cancer therapy and nicknamed “magic bullets,” with licensing deals often reaching billions of dollars.

In this trial-intensive, highly complex domain, TherapiAI has built an AI agent specifically for ADC development. After researchers pose questions in natural language, the system automatically integrates cross-system and cross-literature data—covering antibodies, linkers, payloads, and other core components—and organizes design factors that influence efficacy and toxicity. This enables teams to evaluate the feasibility of different strategies early on, without repeatedly cross-checking literature and databases.

More importantly, the ADC AI agent highlights parameters likely to require adjustment later, helping teams to quickly narrow down their direction. For pharmaceutical companies and CDMOs, this reduces the number of unnecessary experiment cycles, dramatically shortens the traditional 2–3 year early exploration phase, and enables earlier entry into development stages with commercial value.

In the highly sensitive regulatory domain, TherapiAI uses its GMP AI agent to challenge manual workflows and help companies stay current with global regulatory changes.

The system can instantly search, compare, and interpret international regulatory texts such as FDA and ICH guidelines, and automatically detect inconsistencies or gaps across department documents, ensuring alignment and preventing delays caused by version errors.

The GMP AI agent can also simulate questions likely to be raised by reviewers using its built-in risk-prediction model, marking high-risk sections on a “risk map” so documents undergo a round of pre-review before submission.

TherapiAI has already been deployed at multiple pharmaceutical companies and CDMOs across Taiwan, Japan, and other places, supporting use cases such as early ADC design, cell-line screening, automated process documentation, and GMP regulatory comparison. Some customers have integrated AI agents directly into their production workflows for cross-department collaboration and document generation; others, after adopting the system, have proactively requested new features, hoping to shift more critical process steps from manual work to AI automation. These collaborations have allowed TherapiAI to evolve into an essential operating layer in the pharmaceutical production chain.

Are You Selling a “Solution” or a “Tool”?

Many deeptech start-ups encounter the same early-stage blind spot: strong technology does not automatically translate into perceived value, and having many tools does not mean that customers want to assemble them themselves. TherapiAI faced the same issue.

“We started with the technology, expecting customers to operate everything on their own,” Han said. However, most pharmaceutical teams are overwhelmed with work. They simply don’t have the time, and therefore won’t pay for “tools.” Whether a tool is powerful or not is a separate issue from whether it solves real on-site problems.

The team later realized that “experts don’t need a powerful tool, yet they need their pain point solved directly.” Han explained, “Within two weeks, we pulled back all the separate tools, stopped selling technology, and transformed it into AI agents that could directly complete work.” For example, their previously standalone OCR module was integrated into a GMP document AI agent. Once the “tool” became an “agent” capable of delivering outcomes, its value became immediately clear: customers were willing to pay and more willing to adopt.

Customers do not want separate tools; they want foundational capabilities that truly integrate R&D, manufacturing, documentation, and regulatory workflows. TherapiAI is not merely creating tools to solve individual problems; it is building an AI operating platform capable of reshaping the entire biopharma sector.

Not every AI revolution begins in Silicon Valley. TherapiAI, a team from Taiwan, offers a glimpse of another future possibility: the next major breakthrough will not depend on geographical coordinates but on who can bring AI into the world’s most complex and critical operational sites.

That question immediately took me back to my teenage years — to a decisive match I was certain I would win, yet ended up losing completely.

At the time, I was ranked No. 1 nationally. Winning that match would have secured my year-end position. Everything started off perfectly — my rhythm was steady, my strategy precise, and I was fully in control. But after a few unforced errors, things began to fall apart. The shots I trusted most suddenly failed me. Anxiety crept in: What’s happening? My focus shifted from “How do I play the next point?” to “Please don’t make another mistake.”

From that moment on, I was no longer playing to win — I was playing not to lose. My opponent sensed the hesitation and turned up the pressure. I grew increasingly cautious, until eventually, I lost the match altogether.

It took me a long time to move past that defeat. Because deep down, I knew I hadn’t been beaten by my opponent — I had been beaten by the version of myself that was afraid to lose.

Years later, as I stepped into the professional world, I realized that almost everyone faces the same inner opponent. In entrepreneurship and investing alike, success often hinges less on intelligence or skill and more on mindset. When things go wrong, some people panic and lose focus, while others pause, assess the situation, and recalibrate. The difference is not in how fast they react, but in how well they reset. The ones who don’t dwell on the last point are the ones who have the energy and clarity to play the next.

I’ve seen founders whose first ventures failed, even earning them the label of “loser.” Yet they didn’t stay down. They absorbed the lessons, shifted focus to what came next, and tried again. Those are often the ones who go on to build great companies.

Watching them, I came to understand that the real turning point isn’t external — it’s internal. This isn’t just a founder’s lesson; it’s a lifelong practice. Life, like tennis, is a series of matches against yourself. Losing to your own fear isn’t shameful — it’s often when true growth begins. Real victory isn’t about never making mistakes, but about learning to let go, reset, and win again — this time, against the version of yourself that came before.

“Engineers often have to spend over 100 hours going through as many as 2,000 pages of datasheets, just to verify that the hardware components are compatible,” said Niyam AI co-founder and CEO Samarth Shyam. “In this industry, every day of delay can cost hundreds of thousands of dollars, and for brands, a month’s delay in launching a product can result in a loss of 20% to 30% of their lifecycle profits. Across a portfolio, those slips compound to millions, if not billions.”

The electronics manufacturing industry is the backbone of global innovation, yet it has long been hampered by inefficient design tools. The first-pass success rate of prototyping is less than 1%, and engineers not only have to check the compatibility of components one by one but also deal with downstream  issues such as parts obsolescence and supply chain instability.

An even bigger problem is that electronic design involves multiple teams but lacks a unified platform to integrate the entire process, resulting in many problems only discovered after the prototype has been completed. Existing tools lack real-time feedback and proactive analysis, leaving companies often forced to “put out fires” only after mistakes have been made.

And this is the very pain point that Niyam AI is trying to solve.

How can two atypical hardware professionals disrupt the electronics manufacturing industry?

The two founders of Niyam AI are not typical “hardware folks,” but they have recognized the industry’s long-standing pain points.

CEO Samarth Shyam is a serial entrepreneur and angel investor with a track record of shipping and exits and plenty of battle scars; his previous company sold tokenized loyalty platforms to enterprises and was acquired. CTO Agrim Singh was Citibank’s first Hacker in Residence and built market analytics that surfaced price-moving events minutes before Bloomberg or Reuters.

The two met in the Entrepreneur First program in Singapore and have since worked on a number of projects, but the real turning point came after watching a founder friend nearly lose his startup because of a single wrong MCU choice. It was an upstream failure. Kickstarter is a graveyard of similar stories where tiny part data mistakes snowball into missed launches.They found that hardware prototypes often required multiple revisions due to component compatibility or specification errors, and engineers lacked tools that could “proactively” identify problems and were forced to make revisions only after the errors had occurred. This was a pure data (ETL) problem and they knew it!

This led them to the decision to found Niyam AI, which catches component risks early by surfacing compliance, sourcing, and design errors before they turn into costly surprises. The duo combines commercial GTM firepower with deep AI execution, the mix needed to win a fast moving, niche technical market.

“The AI spellcheck for the World of Hardware” makes design ten times faster! What does Niyam AI do?

“Niyam is the AI spellcheck for the world of hardware.” Shyam explained that engineers, who used to spend hundreds of hours comparing data and copying specs out of PDFs, now have Niyam sitting inside ECAD and PLM. The agent pulls design files, BOMs, and linked datasheets automatically, then parses and checks them in minutes.

Shyam further explained that at the beginning of the process, the agent extracts and normalizes specs from each datasheet and design file, builds structured part data, and runs instant compatibility checks. It then looks for risks like insufficient inventory, long lead times, and expiring certifications.

All test results are consolidated into a single dashboard, where engineers can simply click on the suggested options and have an updated bill of materials automatically generated and imported back into the existing Product Lifecycle Management (PLM) system, with little or no change to their work habits.

On the supply-chain side, Niyam has significantly reduced the cost of manual cross-checking. On the supply chain side, the agent removes most manual cross checking. In the past, teams entered part numbers across thousands of suppliers, verified inventory and certifications, and picked replacements by hand.

Now, Niyam’s agentic AI will start from the bill of materials and proactively provide “contextualized recommendations”not only telling you “what parts are available” but also filtering out solutions that “really work,” based on criteria. This proactive, real-time feedback makes design over ten times faster, revolutionizing the reactive, time-consuming model of the past.

How can Niyam AI enter the EDA market dominated by giants?

Over the past 30 years, the EDA market has been monopolized by giants such as Synopsys, Cadence, and Siemens, with tools primarily focused on design. Although they do have verification features, these are limited to basic checks. As hardware design becomes more complex, traditional tools struggle to perform cross-component analysis at an early stage and lack data integration with the supply chain. EDA vendors make money from simulation seats, and PLM vendors make money from change-order churn, so proactive upstream verification has not been a priority.

In the wave of AI, many startups such as Flux, Celus, and JITX have tried to enter the market, but most focus on design efficiency but they leave the core problem untouched: part data still moves by hand and compatibility gets verified late. 

Niyam, on the other hand, targets the upstream failure. Their agent sits inside the ECAD and PLM, pulls schematics, BOMs, and linked datasheets automatically, then runs continuous risk checks for compatibility, lifecycle, sourcing, and compliance. No uploads, no one-off scans, just background guardrails that surface issues early and propose fixes. Compressing what would otherwise take hundreds of hours into minutes.

This positioning has allowed Niyam to quickly establish an advantage: on one hand, it has accumulated expertise in component lifecycle, supply-chain dynamics, and design rules; on the other, it has secured validation from major EMS’s and ODMs within a year of its founding, continuously strengthening its data, processes, and trust in real-world scenarios to build a high barrier to entry.

“In enterprise SaaS, one of the hardest things is figuring out who the real users are, and where in the value chain you fit in”

In November 2024, the team rebuilt the product to run as an agent inside the customer’s environment. 

Upload-based workflows hit a wall in practice. “A BOM or schematic can have over 300 permutations in the lifespan of a product. No one is going to upload a new file each time. If you rely on uploads, most risks stay invisible,” Shyam said. Rather than trying to change habits that are decades old, the team made the workflow agentic. “You meet engineers where they work. The agent watches ECAD and PLM for changes, pulls the right datasheets, runs the checks, and proposes fixes automatically,” he added.

Adoption improved dramatically once those extra steps disappeared. Early versions asked teams to upload files to a dashboard, which worked once in a while, not every day. The new flow plugs directly into ECAD and PLM and serves results in place. The system still supports Excel and CSV for teams that need them, but the default is no manual uploads.

The team also learned who the real day-to-day users are. “At first we thought the daily users would be design engineers,” said Shyam. “In practice, librarians, component engineering, PLM, sourcing, and compliance live in these tools. They care most about accuracy and traceability. If it takes hours, they will still choose correctness.” Time pressure sits with downstream brands and program owners. “For an international brand like Apple, a delay can miss a market window. A month can cut lifecycle profit by 20 to 30 percent,” Shyam said.

Those lessons shaped the product. Meet the bar for accuracy that front line teams demand, run inside their systems so adoption is painless, and deliver time savings leadership can feel. “That is how Niyam becomes part of the supply chain instead of another dashboard people open once and forget,” Shyam said.

Niyam: Creating a “New Order” for the Manufacturing Industry

After a run of pilots and validations, Niyam drew interest from major EMS and ODM partners and was selected for the Wistron Accelerator in 2025. The program let the team test and iterate in real factory conditions and signaled meaningful validation from industry leaders. That selection led to a full sitewide pilot with Wistron, one of the largest manufacturers in the world, and pilots in the pipeline with three of the ten biggest manufacturers globally.

Rather than just catching errors, Niyam reshapes the engineer’s workflow. The agent sits inside ECAD and PLM, surfaces risks early, proposes fixes, and writes clean revisions back into the system of record. “In this industry, even a two to three percent improvement in efficiency can translate into billions of dollars in revenue,” said Shyam.

For Shyam, this is the starting line. The vision is clear: in five to seven years, Niyam will be the agentic AI backbone of the two trillion dollar electronics supply chain. “Every tier 1 EMS, ODM, and OEM runs designs through us, from datasheets and BOMs to alternates, certifications, even placement. What used to be manual, siloed, and error prone becomes self healing and automated. Hardware finally iterates as fast as software, and if you are building electronics anywhere in the world, Niyam is quietly in the background making sure nothing slips,” said Shyam. 

The name Niyam comes from Sanskrit for rules and a way of life, and that is the aim: turn the rules that keep hardware safe into everyday practice inside ECAD and PLM so discipline becomes the default across the supply chain.

Faced with this steep revenue decline, pharmaceutical companies have no choice but to accelerate their transformation. They are reducing costs, shortening R&D time, and even outsourcing more manufacturing to CDMOs (Contract Development and Manufacturing Organizations.) To support this transformation, “Pharma 4.0” is gradually shifting from an option to a necessity.

Pharma 4.0, inspired by Industry 4.0, emphasizes leveraging data to integrate R&D with clinical, manufacturing, and quality management, turning previously fragmented processes into a closed loop that enables real-time decision making. Artificial Intelligence (AI) is the core driver of this change.

With the global pharmaceutical industry investing more than US$100 billion in R&D each year, any improvement in efficiency means a huge return in value. International giants are already taking action: Genentech, a Roche subsidiary, has partnered with NVIDIA to accelerate drug development using AI. French pharmaceutical leader Sanofi was the first to integrate AI into R&D, collaborating with UK-based Exscientia in oncology and immunology, as well as forming alliances with OpenAI, Formation Bio, and others to speed up the clinical development of new drugs.

However, the real game-changer is not AI that merely analyzes data, but AI that can delve into R&D and manufacturing processes and “take direct action.” In recent years, similar initiatives have begun to emerge in Asia.

For example, Taiwan-based startup Therapi AI is seeking to enter the pharmaceutical industry with deployable AI agents, transforming AI from a passive adviser into an active “executor” on the production line. These agents have clearly defined roles, enabling them to quickly identify high-potential cell lines from data or assist with compliance reviews. Using a “no centralization, no retention” architecture, researchers can access data across systems simply by issuing natural-language commands, thereby reducing compliance and cybersecurity risks.

The potential of this type of technology is beginning to be proven in practice. Some cases have shown that introducing AI models can dramatically reduce the cost of cell line screening and shorten the R&D cycle by several-fold. For CDMOs, this is not just about improving efficiency—it also determines their ability to take on more orders and fully embrace Pharma 4.0.

The digital transformation of the pharmaceutical industry is no longer a matter of whether to invest, but a race to see who can achieve it fastest. From multinational pharmaceutical companies integrating AI into the heart of R&D to startups and CDMOs collaborating to build smart production lines, the Pharma 4.0 wave is rapidly taking shape worldwide. Over the next decade, this wave will redefine the rules of the pharmaceutical industry and determine who emerges as the leader in the competition.

But over the years, I’ve watched countless founders, and almost none of their original ideas grew up unchanged. Some pivoted from consumer to enterprise markets, some rebuilt their products from scratch, and some only found a breakthrough path at the edge of giving up.

My first lesson: ideas are never the ultimate key to success.

An idea is more like a window—revealing a founder’s worldview and thought process. Why this market? Why now? Most importantly: what problem do they see, and how do they define it? Ideas are merely a starting point, constantly reshaped by the market. What truly matters is the founder’s insight into the world and how they ask questions—these determine the direction and quality of every adjustment. What’s worth investing in is not a “perfect answer,” but the unique way of thinking behind it.

Over the past decade, I’ve also come to realize that markets are far more powerful than I once imagined. Products can always be revised, but if the market is too small—or the timing too early—even the smartest solution may end in vain. Entrepreneurship is never a solo battle; it needs the push of a larger wave. Otherwise, it’s hard to go far. Many teams that seemed technically perfect still failed, often because the market wasn’t big enough or the rhythm was off.

Most importantly, what ultimately determines whether a company can endure is people. What brings most companies down isn’t the wrong direction—it’s founders who can’t fix mistakes or keep going when things get tough.

When faced with misjudgments, can a founder swallow their pride and admit fault? After repeated failures, can they still stand back up? Ideas can be adjusted, markets can be re-chosen—but the founder’s integrity is non-negotiable. In the end, those who succeed are not always the smartest, but those who are honest with themselves and refuse to give up.

I once believed investing was a science. After ten years, I’ve learned it is much more an act of trust. The future cannot be predicted, and data is never complete.The biggest lesson I’ve learned in this decade is to keep faith when things are uncertain, to look beyond the moment, and to see the future with a longer perspective.

Many assume that “storytelling” is a natural-born talent. But this student’s performance proved something else: what truly influences others is not innate eloquence, but a skill that can be learned and developed. I call this skill Story Crafting.

Imagine walking into a room. Your first task is not to speak, but to observe and listen—to understand what the other person truly cares about. Once you discover the angle that resonates, your story gains its entry point. Then, you layer emotion with evidence, so your message touches the heart while standing on reason. As the conversation unfolds, you guide the interaction—when the listener starts nodding or even engaging actively, you know they’ve already stepped into your story.

Finally, you must leave behind something they can carry with them: perhaps a short but powerful phrase that echoes in their mind; a vivid image that lingers in memory; or, most importantly, a concrete action that motivates them to take the next step after the meeting. A story is only complete when the listener walks away still remembering you.

When a story follows these steps, it ceases to be mere performance—it becomes a force that drives decisions and inspires change. It creates resonance in the moment, and leaves a lasting aftereffect long after the conversation ends.

In the post-AI era, information is more abundant than ever, and knowledge is at our fingertips. What’s scarce is no longer content, but trust and influence. AI can generate endless text, but it cannot reveal your values. It can mimic language, but it cannot build authentic connection. Ultimately, what determines success is whether the other person chooses to keep the conversation going—or even take action—after hearing you.

That is the true value of Story Crafting. It is not a “superpower” reserved for the gifted few, but a hard skill that anyone can hone through practice. And mastering it may well be the key to seizing your next opportunity.