In October 2024, my co-founders and I set out to make our Ph.D. research useful in the real world. We had built AI models that could interpret medical images such as X-rays and CT scans across tens of thousands of potential diagnoses, generating comprehensive radiology reports that mirror how radiologists reason in clinical practice. At a time when AI in radiology was limited to flagging a handful of specific conditions, this marked a fundamental shift.

Less than a year later, we faced a critical fork in the road: raise venture capital and continue independently, or accept an acquisition offer from , the world鈥檚 largest radiology practice.

The conventional wisdom in tech is that real ambition means staying independent. But in asking ourselves what it would truly take to transform healthcare, the answer was different.

Clinical AI is highly regulated with long sales cycles and complex stakeholder dynamics, where structural advantages tend to harden market positions and compound over time. We decided that joining forces 鈥� carefully structured to protect our velocity 鈥� would dramatically improve the odds that we realize our mission of significantly increasing the world鈥檚 access to healthcare.

Research success is not clinical readiness

During my Ph.D., I trained radiology AI foundation models on what, at the time, felt like massive research-scale datasets; tens to hundreds of thousands of studies. These models make for strong academic demonstrations, prototyping new capabilities across a range of tasks. In real clinical settings, however, they would not yet have met the standards required for production-level safety and consistency in patient care.

Despite the persistent narrative that AI will make radiology obsolete, the reality is that the problem is extraordinarily difficult. A single CT study, for example, can contain 10 high-resolution volumetric series, effectively 3D videos. Add prior studies for the same patient, and you can have a billion pixels of data.

Those billion pixels encode entire medical textbooks worth of information. On top of this, real-world radiology is defined by edge cases where rare but critical pathologies are encountered regularly. We learned a hard truth early on: Models that work in controlled research environments often fall apart when exposed to real-world complexity.

Think about self-driving cars. A decade ago, progress looked impressive. But the real world kept introducing new failure modes. After more than a decade of significant capital investment, only a handful of companies have approached true reliability.

Components required to build reliable models

Key patterns emerged. The companies that made the most progress controlled the entire system and achieved scale early. They owned the vehicles, the sensor stack, the data collection pipeline, the simulation environments, and the deployment infrastructure. That integration, operating at scale, allowed them to continuously collect rare edge cases, retrain models, validate improvements and redeploy safely.

Radiology is no different. Success in the real world requires massive, diverse historical datasets and live data feeds that continuously surface rare edge cases and distributional shifts. It requires vast clinical resources and operational infrastructure to redesign clinical workflows around AI, engineer systems that perform reliably at scale, conduct large-scale research studies, secure regulatory clearance, refine models safely, and continuously monitor performance post-deployment.

Additionally, frontier language models have clearly demonstrated that continuous, high-quality and extensive human feedback is the secret sauce in making models useful. This is no different in radiology. In a world where radiology reports are drafted by AI, every draft must be reviewed, edited and signed off by a human radiologist.

Those edits become high-quality signals that can be leveraged for improving the AI models. Better models elevate radiologists’ accuracy and capacity. Improved radiologist accuracy increases the quality of future training data. Increased capacity allows radiologists to take on additional contracts.

That, in turn, generates more data and high-quality corrections, setting a powerful flywheel in motion. Access to this correction data is rare in AI and can only work meaningfully at a massive scale. These capabilities would be incredibly difficult to achieve as a standalone AI startup.

In healthcare, growth follows evidence

In healthcare, trust is hard earned. It rests on demonstrated clinical efficacy, reliability, security and regulatory rigor. For a health system or radiology group to adopt technology from a new startup, particularly in workflows that directly affect patient care, requires rigorous, real-world evidence.

Evidence in healthcare is not generated in small pilots. It is built through sustained performance across diverse sites, patient populations, modalities and edge cases. If a system proves itself within the world鈥檚 largest radiology practice, it establishes credibility across multiple dimensions at once 鈥� efficacy, reliability, security and scalability.

In sectors where lives are at stake and the goal is to build something that endures, the way to build it is from within the system you鈥檙e trying to improve. Selling early didn鈥檛 shorten our journey, it accelerated it. It gave us the foundation required to deliver on our mission of significantly increasing the world鈥檚 access to healthcare.

is the CEO and co-founder of , the AI business unit of at . During his undergraduate studies in physics and electrical engineering, he became driven by a singular mission: increasing the world’s access to healthcare through technology. Convinced that AI was the most promising technology to make this happen, but not yet good enough for real-world clinical use, he pursued a Ph.D. in AI at where he focused on foundation models for radiology. His doctoral work produced Merlin, a 3D vision-language model for CT interpretation published in 鈥淣ature鈥� in 2026 and recognized as one of the most important papers in the field.

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Founders spend a lot of time thinking about capital. They model burn carefully. They negotiate valuation. They weigh hiring plans against runway.

But many startups overlook a source of capital that doesn鈥檛 require dilution at all: tax credits. And to be clear, this isn鈥檛 typically because a business doesn鈥檛 qualify. It鈥檚 because no one builds a process to identify and capture these credits consistently.

Most startups are aware of at least one major opportunity, and that鈥檚 the Research & Development tax credit. But fewer founders take a broader look at business decisions throughout the year and how many of those may lead to tax credit opportunities. Hiring decisions, benefit structures, accessibility upgrades, facility investments and certain energy projects all can carry incentives.

So, the issue isn鈥檛 eligibility. It鈥檚 ownership, timing and consistency.

In early-stage companies, finance teams are lean. Credits often get discussed once a year during tax preparation. However, by that point, it can be too late. The required elections may have been missed, documentation may not support a claim, or deadlines may have passed.

When that happens, the opportunity is gone. We see this pattern frequently in examples such as:

• A company hires several employees who may have qualified for a hiring credit, but no screening process was in place at onboarding.
• A retirement plan is launched without evaluating available startup or employer contribution credits.
• Paid leave policies are expanded without reviewing whether a federal credit applies.
• A facility upgrade is completed without considering whether accessibility- or energy-related incentives were available before the project was placed in service.

None of the above decisions are inherently wrong, but they are incomplete.

Coordinating credits

Tax credits don鈥檛 appear automatically because money was spent. Taking advantage requires planning, including specific documentation, elections and coordination between departments. Without that coordination, even well-managed startups leave savings unclaimed.

More-mature companies approach this differently.

Instead of waiting until year-end to ask, 鈥淒id we qualify for anything?鈥� established organizations build periodic reviews into their operating cadence.

• Hiring processes include the necessary steps to preserve potential credits.
• Engineering teams track qualifying activities as projects progress.
• Finance evaluates larger operational investments before contracts are finalized.

This doesn鈥檛 mean turning every department into tax specialists. It requires clarity around who鈥檚 responsible for asking the question early enough, and it ideally includes expert guidance and support to get it right.

It鈥檚 helpful to think about this as an evolution.

At a reactive stage 鈥� which is most startups 鈥� credits are evaluated only when the tax return is being prepared. At a more structured stage, the company reviews credit opportunities quarterly and aligns documentation throughout the year. And in a strategic stage, leadership fully understands how certain business decisions may create incentives and ensures the right processes are in place before those decisions are implemented.

Multiple credits add up

The accumulated financial impact can be meaningful. While a single credit isn鈥檛 likely to transform a business, multiple credits across hiring, development and benefits can offset real costs. For companies focused on extending runway without raising additional capital, those offsets matter.

There鈥檚 also a governance component.

Investors and buyers increasingly review operational controls during diligence. A startup that has evaluated available credits and maintained documentation signals discipline. A company that hasn鈥檛 considered them at all may invite additional questions (especially if elections were missed or filings need to be amended).

None of this is to suggest credits should drive a founder鈥檚 core strategy. Product development, revenue growth and customer demand remain the priority. But when companies are already investing in innovation, hiring and infrastructure, it makes sense to evaluate whether part of that investment can be recovered.

The first step is simple: Get the full picture before making any decisions. In many cases, that includes working with an adviser who understands how credits apply to growing businesses.

Then, assign ownership. Determine who is responsible for reviewing credit opportunities throughout the year. Coordinate among departments like finance, HR and operations before major decisions are finalized. Make documentation part of the process rather than a reconstruction exercise at the end of the year.

Being proactive

Again, tax credits are not automatic. They鈥檙e for those who plan the entire year.

Startups looking to be more proactive should keep credits like the R&D in mind for its potentially meaningful offsets when investing in product or technical improvements. But don鈥檛 stop there.

If considering structured paid leave, review the Paid Family and Medical Leave Credit, which can apply when policies meet specific requirements. Businesses reviewing facility improvements may qualify for the Disabled Access Credit. While credits such as these don鈥檛 apply to every company, they鈥檙e common enough to demand attention before decisions are finalized 鈥� even seemingly unrelated ones.

Startups focused on capital efficiency will see this planning make a measurable difference over time.

is a tax supervisor specializing in research and development tax credits at . He holds a master of science in accounting from and has experience across both public and private sectors. Garba has spent several years advising companies on R&D tax credits, helping startups and growth-stage businesses navigate complex tax regulations and maximize available incentives.

Related reading:

• Sales And Use Tax: What Every High-Growth Startup Should Know About Compliance
• How Your Startup Can Take Advantage Of The R&D Tax Credit

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If you鈥檙e the CEO of or and reached $100 million in ARR in under a year, this article isn鈥檛 for you 鈥� enjoy being a unicorn as thousands of investors beg to fund you.

But if you鈥檙e not, then let’s be honest: raising in 2026 is tough. Although global venture funding is growing, raising capital isn鈥檛 any easier for the average startup. According to 附近上门 , more than a third of global funding in 2025 went to just 629 companies, compared to 24% of funding in 2024.

This highlights a growing concentration of capital, making most of that funding effectively inaccessible to early-stage startups. So what can founders do to fix that?

We don鈥檛 invite strangers to our houses, and we don鈥檛 hire them for important jobs either. For thousands of years, trust and credibility were the most important factors in forming relationships, both business and personal.

In 2025, Silicon Valley companies attracted nearly 50% of the entire U.S. . Silicon Valley is also home to , over half of all U.S.-based unicorns.

It鈥檚 not because San Francisco Bay Area founders are inherently smarter 鈥� it鈥檚 largely about being close to capital and networks. When you鈥檙e in constant proximity of MAG7 companies and hundreds of VCs, connections happen organically, through social gatherings, meetups and referrals. This is how credibility is formed: through connections and exposure.

So, is networking the secret to raising capital? Partially, but it doesn鈥檛 scale. You can鈥檛 just meet the whole industry and invite them all to a 1-1.

So instead, you have to build your reputation. Here are my top four pieces of advice on how to build it right.

Be visible

Make your growth visible. Whenever you reach a significant milestone 鈥� raising a round, hitting a user target, or achieving revenue growth 鈥� the market should hear about it.

We鈥檝e seen countless companies reach a huge target and then fail to spread the word about it.

Make sure to plan all media coverage in advance, keep exclusive news up your sleeve, and have an extensive media strategy. Once the word is out through your company鈥檚 social media, pitching to journalists becomes significantly harder. Everybody wants exclusives, and no one wants to write about old news. Global media outlets are all about relationships. Make sure to form a meaningful connection with journalists covering your particular niche.

Focus on customers

The second priority when raising funds is your company鈥檚 place in the overall market landscape. Be where your customers are. Many founders make the same mistake: chasing investors instead of customers.

Remember that investors will always find good investment opportunities. Your job is to make sure that your company is one of them. Investors have to see that your company has a sustainable customer acquisition approach and is able to continuously grow its user base.

Chasing investors can even damage your public picture. If VCs see you spending heavily to attract investors rather than customers, it may signal misaligned priorities.

Be a thought leader

Important thing No. 3: thought leadership. You have to prove your credibility through actively participating in conferences and meetups.

Speaking at industry events signals credibility at scale. Conferences are highly selective. Being on stage implies that organizers have already vetted your expertise. Getting on the stage and delivering your core message will help your credibility more than any degree or a title.

Raise symbolic capital

The fourth significant factor is symbolic capital 鈥� the way your company is perceived by the market. A great way to acquire symbolic capital is through various ratings and features. They鈥檙e usually put together by the larger media outlets and include programs such as Forbes鈥� 30 Under 30, TechCrunch Startup Battlefield and Slush100.

Similar to conferences, participating in different features shows potential investors that a credible player with a good reputation has already done a background check on you and is ready to endorse you. One well-known logo in your endorsements list can go a long way in securing the next round of funding for your startup.

A somewhat unexpected benefit of getting into the biggest ratings and roundups is your AI visibility. Your company being featured in one of these lists will significantly improve the odds that AI will highlight your company in relevant conversations. AI visibility is increasingly important for user acquisition, considering that according to , already use AI instead of traditional search engines for shopping.

Reputation: You can鈥檛 buy it

Reputation is one of the rare things in the business world that you can鈥檛 just buy.

One of our longstanding partners received an invitation to a dinner with the Royal Family of the United Kingdom, which is something that no amount of marketing budget will give you. It takes a lot of coordinated work and effort that won鈥檛 result in exact KPIs on day one, which is why many startups just don鈥檛 have the patience and strategy it takes to build credibility.

As development and compute costs fall, the number of startups continues to grow. In that environment, reputation becomes the key differentiator between companies that attract capital 鈥� and those that don鈥檛.

is a communications strategist and serial entrepreneur with more than 10 years of experience. She co-founded , an AI adviser for smart manufacturing, and leads BeGlobe, a PR agency for tech startups and VCs. She is a graduate of 鈥檚 SkyDeck Accelerator.

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For much of the past year, the conversation around agentic AI was dominated by ambition. Founders and investors alike talked about autonomous systems that could reason, act and operate with minimal human involvement. As we move into 2026, that narrative is shifting away from what agents might do someday toward what they can reliably deliver today.

This shift is evident in findings from 鈥檚 report, which quotes from conversations with eight AI-focused VC investors who discuss what they see in the market today and in the year ahead. Their perspectives reflect a broader recalibration underway across the venture ecosystem. The experimentation era is giving way to one of more intentional adoption. AI is increasingly treated not as a standalone feature, but as an operating layer 鈥� embedded in workflows, governed by policy and evaluated on outcomes rather than ambition.

In practice, that means agents are finding traction in narrow, well-defined use cases. Fully autonomous agents remain rare in production, particularly for complex or high-risk workflows. What is working are agents deployed in data-rich domains like software development, customer support, sales operations and internal analytics. In these environments, human-in-the-loop designs are not a compromise; they are often the reason agents can be trusted and adopted at scale.

What investors look for

This shift has changed how startups are evaluated. As agentic tooling becomes easier to build, impressive demos have lost much of their signaling power. What matters now is evidence of usage: customers running agents in production, measurable productivity gains and early revenue momentum.

Founders need to clearly articulate how their agents improve existing workflows and why that value persists over time. Without that clarity, even technically strong products struggle to stand out.

Capital dynamics are also shaping the market. Investment continues to concentrate around a small group of foundational models and infrastructure providers. Rather than crowding out startups, many investors see this as an enabling layer. Well-capitalized platforms absorb the cost of training and inference, allowing startups to focus on application-level value.

Looking ahead, 2026 is shaping up to be less about sweeping claims of autonomy and more about execution. Enterprises want agentic solutions that fit into existing operating models, meet governance requirements and deliver quantifiable business impact.

For VCs, the hype cycle has done its job. The next phase will reward startups that turn agentic AI into focused, outcome-driven businesses and can prove it.

is the head of where he focuses on investments to drive innovation and unlock new value on top of the Snowflake platform. A seasoned product management leader, he originally joined Snowflake in 2017 as a senior product manager, a role in which he played a pivotal role in the company鈥檚 partner ecosystem expansion. Prior to Snowflake, he spent 18 years at with various roles across master data management and data platforms. Kapre earned his bachelor鈥檚 degree in electrical engineering and computer science from the .

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$4 trillion and 100,000 new companies.

These are the outcomes of a quarter-century of venture capital investments into startups.

Since the new millennium, $4.2 trillion has been invested into the global startup economy, producing 97,982 scaleups.

Of these, 7,030 have raised more than $100 million. We call them the Scalers. And 473 have surpassed the $1 billion mark in capital raised. We call them the Super Scalers.

These are just some of the findings of the latest report, produced by with for the held in Paris last month.

The innovation paradox: Nothing seems to move

While comparing the world鈥檚 Startup Atlas 2025 with the 2024 edition, one thing becomes clear: despite the 鈥渘ew economy鈥� continuing to generate industry disruptors at a steady pace 鈥� with nearly 8,000 new scaleups added since January 鈥� the overall picture of global innovation remains largely unchanged.

And for much of the world, that鈥檚 not good news. The balance of power has barely moved.

• North America continues to dominate, with 43% of the world鈥檚 scaleups, while attracting an even larger share of capital 鈥� now exactly half of all global scaleup investments.
• The APAC region maintains its second-place position, with about 27,000 scaleups (27% of the global volume) that have raised $1.3 trillion (31% of total capital).
• Europe remains stuck in third place, failing to gain relevance. The Old Continent is actually getting 鈥渙ld,鈥� accounting for 22% of scaleups but just 13% of global investments. In other words, Europe doesn鈥檛 have enough companies, and even worse, they鈥檙e under-capitalized.
• The Middle East, Latin America and Africa still struggle to meaningfully appear on the global map.

In a world where concentration attracts more concentration, reshaping the landscape takes time. But the iceberg rule applies: most activity happens below the surface 鈥� and reveals itself only much later. And today, there is far more dynamism beneath the surface than the top-line numbers suggest.

A decade in the world of innovation

But just zoom out, and everything changes.

A decade on planet Startup is a geological era.

The Innovation Ecosystem Life Cycle Curve hasn鈥檛 changed shape, but it has become dramatically more crowded, with far more weight shifted toward the later stages.

In 2015, fewer than 500 startup ecosystems sat on the global curve. The Star stage was an exclusive club of just three tech hubs (Silicon Valley, New York and Beijing), and only 13 ecosystems had reached the Scaleup stage. In short, the startup scene was relatively simple and still heavily concentrated in the U.S. and APAC.

Ten years later, the landscape is radically different. Nearly 900 ecosystems now appear on the curve. The Star stage includes 19 ecosystems (a 6x increase), while the Scaleup stage counts 45 (3.5x growth).

If we look into the crystal ball, it鈥檚 easy to foresee that by 2030 the life cycle curve may host 1,500-plus ecosystems, a massive expansion.

Of these, 40-50 are likely to reach the Star stage, and 90-100 the Scaleup stage. Practically speaking, this means one thing for innovation hunters (VCs and corporates): Navigating this landscape is about to become a serious headache.

The geography of the startup economy has also shifted.

On the right-hand side 鈥� the upper stages 鈥� the dominance pattern remains similar: once led by the U.S. (2) and China (1), the Star stage is now shared by the U.S. and APAC (8 each), with Israel, London and Paris as notable exceptions.

But moving left along the curve, Europe is gaining ground.

At the Scaleup stage, Europe now counts 12 ecosystems (up from just two a decade ago), surpassing APAC.

And in the startup and standup stages, Europe is now the region with the highest number of ecosystems overall.

In a nutshell: Europe seems to have built a broad, diversified base 鈥� but the question remains: Is this latent potential waiting to emerge, or simply the result of structural fragmentation?

A final note: Latin America, the Middle East and Africa are 鈥� with a few exceptions 鈥� almost absent from the Star (Israel) and Scaleup (Dubai, S茫o Paulo and Istanbul) stages. In the Startup stage, they represent only 10 out of 93 ecosystems. In the Standup stage, we finally see larger volumes (73 out of 721).

The question is: When will this immense innovation potential finally surface?

鈥斅燜or more insights, you can download the report: 鈥淭he Calm Before the AI Storm: Global Innovation Ecosystems: Who Leads, Who Lags, and Who Could Rise鈥� .

is chairman of and a professor at . He is a serial entrepreneur who has started three startups in his career, the last of which is , among the five Italian scaleups that have raised the largest amount of capital. He is recognized among the leading international experts in open innovation and has wide experience in setting up and managing open innovation projects 鈥� venture clients, venture builders, intrapreneurship, CVCs 鈥� with large multinational companies, as well as advising and training on this subject. Onetti has a column on () and several other tech blogs.

Related reading:

• Innovation Policies: The Australian Lesson Focusing Startup Investments On A Few Strategic Sectors
• AI Is Gorging On Venture Capital. This Is Why 鈥楶hysical AI鈥� Is Next
• The Dual-Use Tech Surge: Innovation鈥檚 Double-Edged Sword

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By and

We like to think markets naturally align incentives over time. Maybe they will. But transitions always involve friction, and AI has accelerated the friction point between economic and human interests.

AI didn鈥檛 create the tension between efficiency and decency, but it intensified and accelerated it. And even if AI doesn鈥檛 replace jobs wholesale, it will reshape them, compress them and fundamentally change the human-machine ratio inside companies.

Which means leaders must answer a new question: How do we build a culture that keeps humans valued and motivated while leveraging machines fully?

A hybrid human-machine organization requires rethinking almost everything: norms, rituals, contributions, trust dynamics, leadership models and more.

Culture has always been the stabilizer that holds companies together. Now it must evolve. Below are the cultural imperatives we believe will define organizations that navigate this transition well.

Five cultural imperatives for the human-machine era

1. Build a culture of hybrid identity. Not human-first or AI-first, but hybrid. Humans bring judgment, creativity, empathy, taste and leadership. AI brings speed, scale, memory and tireless iteration. A culture that values both reduces fear and increases clarity.

2. Establish trust norms between humans and machines. Teams need to know when to rely on AI, when to challenge it, and how to collaborate with it. Trust must be explicit and expressed 鈥� not assumed or hidden.

3. Redefine contribution and recognition. If AI plays a meaningful role in output, recognition must shift too. Don鈥檛 just reward production. Reward insight, direction, taste, judgment, strategy and creative authorship.

4. Preserve belonging as leverage increases. Smaller teams can still be human-centered 鈥� but only with transparency, clear purpose and rituals that reinforce connection. Humans鈥� need for belonging must be intentionally designed.

5. Build culture early. Cultural debt accumulates faster than technical debt. Leaders who design norms early 鈥� around language, expectations, rituals and trust 鈥� will avoid confusion and resentment.

I think about this constantly. Our company is one small version of what鈥檚 happening across industries. Efficiency is accelerating, roles are evolving and culture is stretching into something new.

But I鈥檓 optimistic. History suggests we eventually find equilibrium with transformational new technologies. Perhaps it will even be a version like the one imagines as 鈥渢he Singularity鈥� 鈥� where humans and machines truly elevate one another.

Until then, we鈥檙e committed to building a culture where the efficient thing and the decent thing can coexist. Where machines do what they鈥檙e best at, humans do the same, and the space between them becomes a new source of creativity and possibility.

We believe it, we鈥檙e building for it, and we鈥檒l stand by it until proven otherwise.

is the co-founder of the world鈥檚 newest creative AI marketing tool, RYA. He鈥檚 also the co-founder of , an advertising agency that leverages data to make hits for , , , and many other marquee brands. Over the past two decades he has led cross-functional teams and developed multidiscipline communications and creative strategies for both for-profit and nonprofit organizations. Himmelsbach is a MBA graduate from 鈥檚

is head of business development at WestComms. He strongly believes that high-quality communication will only continue to appreciate in value and supports clients working in AI, crypto and frontier technologies. Pinson still keeps a regular hand-written journal, loves wine and earned a degree in economics and philosophy at in California.

By and

We鈥檙e not doomsday thinkers. The truth is, we can鈥檛 afford to be, because founders rarely have that luxury. Instead, we make decisions in real time with clients, teams and cash flow.

But the signals around us are loud, and getting stronger.

Startups like , and each operate with . Humanoid robots are being built explicitly to replace labor. and have cited automation or AI as contributing factors in recent reductions in force. Whether every case is perfectly causal isn鈥檛 the point 鈥� the direction is unmistakable.

So what about the companies in the middle? Not fully AI-native. Not legacy. Actively transforming in real time.

A reply to 鈥檚 captured the moment with dark humor: 鈥�.鈥�

It鈥檚 funny but clarifying. If a PE firm were leading our transformation, they鈥檇 restructure aggressively around speed, automation and product velocity. Many roles 鈥� potentially including ours, even as founders 鈥� would be redesigned or replaced.

Welcome to the modern dilemma faced by nearly every existing founder, CEO and management team on the planet.

Everyone is experimenting with AI partly out of curiosity but mostly out of fear 鈥� fear of being left behind, losing business, missing the next shift. No one, however, agrees on what 鈥淎I is coming鈥� actually means. CEO predicts abundance; CEO predicts chaos. The truth is probably somewhere in between.

Meanwhile, leverage is becoming nonlinear, just as Ravikant warned. Highly leveraged individuals can create exponentially more output than peers. Society, however, isn鈥檛 built for exponential asymmetry, and most companies aren鈥檛 either.

Which is precisely why culture is so important. We鈥檙e definitively reorganizing work and what it means to be professional, yes, but we must also reorganize culture in order to succeed.

Nearly all AI commentary focuses on operations 鈥� efficiency, automation, productivity, tools, workflows. But what of culture 鈥� the real operating system of a company 鈥� within organizations?

We have now over-indexed on the operational, rational, intellectual, left-brain paradigm. Ironically, we鈥檙e doing so precisely as intelligence gets commoditized. We must now concern ourselves with its reciprocal.

• How do teams build trust when AI handles core work?
• What does 鈥渃ontribution鈥� mean when output is hybrid?
• How do you maintain belonging when leverage increases?
• What does creative authorship look like between humans and machines?
• How do you preserve dignity, identity and motivation?

These are cultural questions. And we don鈥檛 have established norms, language or frameworks for them yet. The cultural gap is the founder鈥檚 dilemma hiding in plain sight.

is the co-founder of the world鈥檚 newest creative AI marketing tool, RYA. He鈥檚 also the co-founder of , an advertising agency that leverages data to make hits for , , , and many other marquee brands. Over the past two decades he has led cross-functional teams and developed multidiscipline communications and creative strategies for both for-profit and nonprofit organizations. Himmelsbach is a MBA graduate from 鈥檚

is head of business development at WestComms. He strongly believes that high-quality communication will only continue to appreciate in value and supports clients working in AI, crypto and frontier technologies. Pinson still keeps a regular hand-written journal, loves wine and earned a degree in economics and philosophy at in California.

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By and

This story starts with a loss.

A client didn鈥檛 renew, so we reduced headcount. Operationally, it made sense, but the decision surfaced something bigger than a single role or contract: a growing tension inside modern leadership that鈥檚 becoming harder to ignore.

Our company spans a creative services business and an AI-powered software platform. That combination has made one trend obvious: The modern economy rewards software, automation and what investor and entrepreneur calls 鈥� the 鈥渇orce multiplier for your work.鈥�

Years ago, Ravikant bemoaned that labor 鈥� once the original form of leverage 鈥� had become 鈥渙vervalued,鈥� and that capital, code and now AI would define the next era. 鈥淵ou want the minimum amount of labor that allows you to use the other forms of leverage,鈥� he wrote.

, perhaps the 鈥渇ace鈥� of AI writ large, is famous for espousing the as the ultimate form of leverage, claiming that it will create 鈥�,鈥� and that 鈥淸a]lmost everyone will want more AI working on their behalf.鈥�

Investor enthusiasm around , and 鈥� companies with extraordinary output and extremely small teams 鈥� suggests he may be right. Humanoid robots, though still early, extend this trajectory even further.

Behind their enthusiasm, however, lies something the most fervent AI supporters seem reluctant to name. In this brave new world of AI, optimal business models continue to reward leverage, software and automation, of course, but they now also appear to reward something new: having materially fewer people.

As a consequence, we keep returning to a central question: What happens when the efficient thing and the decent thing diverge, even temporarily?

The economic pressure is clear, but the human pressure is just as real. We all seem convinced that we鈥檒l remake our workflows with AI, but what of our culture 鈥� the intangible connective tissue beneath it all? It鈥檚 no coincidence that before AI, the best companies often had the best cultures. In fact, not only do we believe this dynamic will continue as we adapt to AI, we think it will become even more important.

The future of work 鈥� perhaps, even, the future period 鈥� will invariably depend on how humans and machines collaborate, but it will also depend upon the culture we create that holds that collaboration together.

The frameworks, norms and practices that will eventually govern human-AI partnerships are critical, but they remain undefined. And we鈥檙e all going to have to build that culture largely from scratch.

is the co-founder of the world鈥檚 newest creative AI marketing tool, . He鈥檚 also the co-founder of , an advertising agency that leverages data to make hits for , , , and many other marquee brands. Over the past two decades he has led cross-functional teams and developed multidiscipline communications and creative strategies for both for-profit and nonprofit organizations. Himmelsbach is a MBA graduate from 鈥檚

is head of business development at WestComms. He strongly believes that high-quality communication will only continue to appreciate in value and supports clients working in AI, crypto and frontier technologies. Pinson still keeps a regular hand-written journal, loves wine and earned a degree in economics and philosophy at in California.

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For companies in rapid growth mode, sales and use tax compliance tends to sit low on the priority list. And then, it suddenly matters.

But as companies scale across states and/or add new revenue streams, tax exposure also can quietly expand in the background. The U.S. has more than 12,000 distinct sales tax jurisdictions, and each has its own rules and rates. So, even a small misstep can snowball into significant penalties or create challenges during due diligence.

At the most basic level, sales tax is what a business collects from customers on taxable goods or services. Use tax applies when a company purchases taxable items and no sales tax was charged (which commonly occurs from an out-of-state vendor).

For example, if a startup based in California orders $10,000 of equipment from an Oregon supplier, the business likely owes use tax to California. The point of the system is to keep local and remote sellers on equal footing.

However, complexity arises because rules differ dramatically by state and industry. For founders, that complexity becomes more than a compliance nuisance 鈥� it鈥檚 a business risk. Noncompliance can delay funding, lower valuation and, in some cases, create personal liability.

Legally, nexus is the connection that requires a company to collect and remit sales tax in a state. And historically, this required physical presence such as an office, a warehouse, or an employee. But after the Supreme Court鈥檚 2018 decision in South Dakota v. Wayfair, Inc., states have imposed obligations based solely on economic nexus, meaning a certain level of sales or transactions within the state.

Most states set the threshold at $100,000 in annual sales. So, even fully remote SaaS or e-commerce companies may trigger nexus without realizing it. And today, more than 45 states enforce economic nexus standards, making it critical for startups to regularly review where their activity might create obligations.

Mapping your tax liability

A quarterly 鈥渘exus map鈥� can help track thresholds and avoid surprises.

But it gets tricky because not everything a company sells is taxable.

Tangible goods are almost always taxable. However, digital products like software as a service vary: some states tax them fully, others exempt them, and a few tax only certain components and may do so at varying rates.

Services are often exempt, but are also increasingly being taxed as states broaden their bases to capture digital and professional offerings. Understanding the nuance isn鈥檛 just an accounting detail. It鈥檚 critical to ensure accurate pricing and revenue forecasting.

Further, marketplace facilitator laws mean that platforms such as or often collect and remit sales tax on behalf of third-party sellers.

Startups selling directly through their own website or issuing invoices must manage those obligations themselves 鈥� even marketplace sales could require a business to register and file in a state. Keeping marketplace and direct sales segmented in your accounting system avoids double taxation or missed remittances.

It’s worth noting that a big area that can trigger an audit is tax due on nontaxed purchases. Another is bundling a nontaxable service with a taxable product/service, which is an area sees come up frequently with our clients.

Additional detail on overlooked areas, which can create exposure:

• Shipping and handling: Taxable in some states if bundled as part of the sale and exempt if listed separately.
• B2B sales: Typically exempt if the buyer provides a resale or exemption certificate (missing or invalid certificates are a common audit trigger).

Do your diligence before due diligence

Sales and use tax issues don鈥檛 just surface in audits. They also appear in diligence.

Buyers and investors frequently uncover unpaid liabilities, and this can lead to escrow holds or valuation adjustments. By contrast, clean compliance records demonstrate operational maturity and readiness to scale. Penalties, back taxes and interest are painful enough, but once a state initiates an audit, it鈥檚 often too late to access Voluntary Disclosure Agreements. Proactive compliance is the only safe route.

So, sales and use tax may feel like a back-office issue. But for high-growth companies, it鈥檚 much more than that. It鈥檚 strategic. Founders and finance teams can stay ahead by engaging with a tax expert. In addition, consider:

• Mapping nexus exposure across states and updating this quarterly;
• Reviewing product and service taxability regularly;
• Tracking and validating exemption certificates; and
• Automating compliance through reliable software tools.

A thoughtful sales and use tax strategy preserves your runway, builds investor trust and prevents costly distractions down the road.

is ‘s indirect tax and compliance director. She has 25 years of industry and tax consulting experience. Since joining Burkland, she has significantly developed and expanded this practice area. Her substantial tax expertise spans sales/use/gross receipts, excise, and property tax, gained through various roles in public and private industry, and consulting 鈥� progressing from tax accountant to director. Her knowledge of tax law across diverse industries has positively influenced the key financial performance of the businesses she has served.

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For white-collar workers, AI looks like a pink slip. But for the millions of blue-collar individuals who struggle to land a job, not because they lack ability, but because of the lack of an effective hiring infrastructure, AI is emerging as the green light they鈥檝e been looking for.

The inability to connect the demand for blue-collar labor, given the substantial supply of skilled talent, is not only an embarrassing systemic failure, but it鈥檚 also a huge blow to the American economy. A of just one part of the blue-collar workforce 鈥斅爉anufacturing 鈥� by and projects that about 2.1 million U.S. manufacturing jobs will be unfilled by 2030, with the gap potentially costing $1 trillion in that year alone. We (if any at all) across industries that face similar shortages of skilled talent.

That is, unless something changes.

Blue-collar hiring infrastructure is broken

It starts and ends with hiring. The applicant-tracking and recruitment-management systems that most companies rely on are designed to uplift top candidates who meet the norms in white-collar industries, leading to a critical divide. As research demonstrates, these tools that they are trained to recognize.

This design is especially problematic given the realities of America鈥檚 blue-collar workforce. Foreign-born workers are overrepresented in these roles: the construction , for example, employs the largest percentage of immigrants of any industry.

U.S. r茅sum茅 conventions, from formatting to phrasing, are unfamiliar to many immigrants, and automated r茅sum茅 systems often down-rank these applications based on immaterial factors rather than work experience or relevant skills.

Credentials, critical across numerous blue-collar jobs, are another obstacle: Licenses and certificates earned abroad often map poorly to domestic job codes, even when the underlying skills are equivalent. For example, a 20-year veteran electrician certified in Nicaragua starts at the same place as a novice in the U.S.

For those who don鈥檛 speak or write English fluently, if at all, these issues are compounded. A key reason: automated systems are trained to reject applications that contain typos, incomplete phrases or grammatical errors. With all these issues combined, it鈥檚 easy to imagine why so many qualified candidates don鈥檛 bother to apply for jobs at all.

How AI is clearing the gutters of hiring

AI succeeds where these legacy hiring infrastructure systems have failed: nuance. Machine learning platforms can circumvent the obstacles that prevent immigrants from landing jobs at scale. Natural language processing allows the systems to interpret nontraditional r茅sum茅s, conduct interviews in multiple languages, and verify credentials automatically.

A welder without a formal r茅sum茅 can be matched to an employer based on verified training records earned in another country. A warehouse worker with limited English can be assessed by their abilities, not their syntax on a resume. This reality would have massive, positive implications for blue-collar employment numbers and the American economy. Better still, it鈥檚 possible.

Further, when these candidates are hired, the business case doesn鈥檛 stop. Employers that have brought refugees into shop-floor roles report meaningfully higher retention in manufacturing, logistics and blue-collar industries, which traditionally experience high turnover.

Put simply, hiring systems that prioritize skills, credentials and language inclusivity don鈥檛 just expand candidate reach, they drive lasting productivity and growth.

The competitive edge

For businesses, the payoff of implementing AI to improve or overhaul blue-collar hiring practices goes beyond altruism. It鈥檚 good business. AI-driven hiring platforms can shrink vacancy times, lower onboarding costs and expand labor pools 鈥� advantages that matter for companies individually, and for the American economy at large.

The challenge for executives and policymakers isn鈥檛 to slow AI down, but to deploy it wisely. Used correctly, these tools can rebuild the connective tissue of the labor market, helping millions of workers find the jobs that need them most.

The workers are out there. The jobs are waiting. The system is broken 鈥� but not beyond repair.

AI won鈥檛 take every job. Not even close. And for many, the technology will actually do the opposite: unlock one.

is the CEO of , an AI-powered hiring platform solving blue-collar America鈥檚 $1 trillion workforce crisis. He was previously the CMO of , a top-five globally ranked coding boot camp that has trained thousands of software engineers.

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