Why Royston G King Says Urgent Work Always Eats the Patient Work

Every business contains two kinds of work. One is urgent and produces visible results inside weeks. The other is slow, compounds over years, and never announces itself. Royston G King argues that any organisation holding both will sacrifice the second without ever deciding to. Here he explains why, and what he does about it.

Royston G King has spent close to a decade advising founders, and he says the pattern is close to universal. It appears in firms of every size, in every sector, and it rarely results from a bad decision. It results from an absence of decisions.

King began working in digital marketing while studying business administration at the University of Southern California and founded his own firm in 2018. Named to the Forbes 30 Under 30 Monaco list, he has advised more than 1,000 clients across over 100 industries in markets including North America, Europe, Asia Pacific and Australia.

“Urgent work wins every argument about where people go this week,” said Royston G King. “Not because anyone thinks it is more important. Because it is due Thursday and the other thing is due eventually. Six months later you look up and nobody has touched the long-horizon work at all, and no meeting ever happened where that was agreed.”

The two categories differ in more than timing. Urgent work produces attributable results, so the people doing it can demonstrate their contribution. Patient work produces outcomes that arrive later and are difficult to trace to any specific effort. When a firm is deciding where to invest, the measurable side wins on the strength of its measurability rather than its value.

“That is the part founders miss,” said Royston G King. “It is not that they undervalue the slow work in principle. Ask anyone and they will tell you it matters. The problem is structural. You have put two things that cannot be compared into one queue, and the queue resolves it every time in favour of whatever can be counted.”

King’s response has been to separate them structurally rather than to resolve the argument each week. Work with fundamentally different time horizons, in his view, should not share a resource pool, because sharing one settles the question in advance.

“You have put two things that cannot be compared into one queue, and the queue resolves it every time.” Royston G King

He has applied the same reasoning to his own ventures, keeping work with different rhythms operationally distinct even where the underlying capability overlaps. The arrangement costs more in overhead, which he acknowledges directly.

“Two of anything means two sets of cost and my attention split rather than concentrated,” said Royston G King. “I would not pay that for two things that were basically the same work with different names. It is only worth it when the timelines genuinely cannot coexist.”

The pattern extends beyond resource allocation. King has argued that the same dynamic explains why firms consistently underinvest in the things that determine their position five years out, including documentation, training, and the systems that let work be done consistently rather than heroically.

“Nobody schedules the thing that prevents a problem,” said Royston G King. “You only get credit for handling the problem. So the preventive work sits there being obviously sensible and never getting done, and then a year later everyone is very busy handling something that did not have to happen.”

The remedy he suggests is unglamorous, which he says is characteristic of the whole category. Patient work needs its own protected time, its own owner, and a review cadence that matches its horizon rather than the calendar. A firm reviewing long-horizon investments monthly will conclude they are not working, since almost nothing visible happens in a month.

“You cannot judge a five-year investment on a thirty-day report,” said Royston G King. “But that is the report that exists, so that is what people use, and then they cancel the thing three months in and conclude it was a bad idea.”

King has said the founders who handle this well tend to be the ones who made the separation early, before either side of the business was under real pressure. Doing it during a difficult quarter is considerably harder, because that is precisely the moment when the urgent side has the strongest claim on every available hour and the argument for patience sounds like an argument for ignoring a fire.

Bay Area Tech Workforce Falls Behind New York, Still Ranks No. 1

The Bay Area tech workforce fell behind New York in total size in 2025, according to CBRE’s 2026 technology talent analysis. Even so, the Bay Area kept the top overall market ranking. The report shows how AI hiring, talent concentration, labor costs, and changing workplace patterns are reshaping the region’s technology economy.

Key Takeaways

  • New York Metro had 394,300 technology talent workers in 2025, compared with 375,730 in the San Francisco Bay Area.
  • The Bay Area lost 23,900 technology talent workers between 2022 and 2025, while New York added 30,640.
  • CBRE continued to rank the San Francisco Bay Area first overall among the technology talent markets in its analysis.
  • AI-related roles represented 57% of Bay Area technology job postings by June 2026.
  • Remote technology job postings in the Bay Area fell to 7% in April 2026, down from 24% in mid-2022.

New York Moves Ahead in Tech Workforce Size

New York Metro surpassed the San Francisco Bay Area in total technology talent workforce size in 2025, according to CBRE’s Scoring Tech Talent analysis.

New York recorded 394,300 technology talent workers, compared with 375,730 in the Bay Area. The figures mark the first time New York has had the larger technology talent workforce in CBRE’s comparison.

The change reflects sharply different workforce trends between the two markets. New York added 30,640 technology talent workers between 2022 and 2025, while the Bay Area lost 23,900 during the same period.

For the Bay Area, that represented a 6% decline from its 2022 workforce level. New York’s technology talent workforce increased by more than 8%.

CBRE’s definition of technology talent covers more than 20 occupations and is not limited to employees working directly for technology companies. Software developers, technology managers, engineers, and other specialists working in industries such as healthcare or financial services can also be counted.

That broader definition makes the workforce comparison different from simply counting employees at Silicon Valley technology firms.

Recent Bay Area technology layoffs also illustrate the changing employment picture. Cisco, for example, disclosed reductions affecting positions in San Jose, Milpitas, and San Francisco as the company shifted resources toward areas including AI, security, networking, and infrastructure.

Bay Area Tech Workforce Shifts Toward AI Roles

The decline in the Bay Area tech workforce has occurred alongside a significant change in the types of jobs employers are seeking.

CBRE reported that AI-related positions represented 57% of available technology talent job postings in the Bay Area by June 2026. At the mid-2022 peak for overall technology job postings, AI-related positions accounted for 20%.

Non-AI technology postings moved in the opposite direction. According to the report, those postings had fallen 73% from the mid-2022 peak, while AI-related postings were more than one-third higher than at that earlier point.

The national labor market shows a similar, although less concentrated, shift. AI-related roles accounted for 31% of available U.S. technology talent positions by June 2026, compared with 11% at the mid-2022 peak.

The figures indicate that workforce size and hiring demand are moving differently. The Bay Area had fewer total technology talent workers in 2025, but a much larger share of its available technology positions was connected to AI by 2026.

U.S. technology talent employment overall continued to grow. CBRE reported a 1.8% increase, equal to 108,760 jobs, in 2025. That was higher than the 1.1% growth recorded in 2024 but remained well below the 7.3% rate recorded in 2022.

CBRE Still Ranks the Bay Area First Overall

Bay Area Tech Workforce Falls Behind New York, Still Ranks No. 1

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Despite losing its lead in workforce size, the San Francisco Bay Area remained first in CBRE’s overall technology talent ranking.

The 2026 report kept the same six markets in its top six positions as the previous year. They were the San Francisco Bay Area, Seattle, Toronto, New York Metro, Austin, and Washington, D.C.

The ranking measures more than total employment. CBRE uses 13 metrics to evaluate the depth, vitality, and attractiveness of technology talent markets for employers and workers.

Technology talent concentration carries substantial weight in the assessment. Labor costs and other market characteristics are also considered.

The Bay Area continues to have one of the highest concentrations of technology talent relative to its overall workforce. According to CBRE, technology talent represents more than 10% of total employment in the region.

More than half of the Bay Area’s technology talent workforce also works directly within the technology industry, placing the region among markets with particularly high industry concentration.

That concentration comes at a cost. CBRE estimated that a typical 500-person technology company occupying 60,000 square feet of office space would face approximately $91 million in annual labor and real estate costs in the Bay Area, the highest figure among the markets in that comparison.

At the same time, local hiring and office activity varies significantly by company. One example is an AI recruiting firm expansion in San Francisco’s SoMa district, where Juicebox increased its office footprint after raising $80 million and outlined plans to grow several teams.

The contrast helps explain why the workforce-size ranking and CBRE’s overall scorecard produce different leaders. New York has more technology talent workers, while the Bay Area scores higher when a wider set of market characteristics is considered.

Tech Talent Extends Beyond Traditional Technology Companies

Technology employment is increasingly spread across industries rather than concentrated solely within technology companies.

CBRE reported that more than 38% of technology talent across the United States and Canada works within the technology industry. That means the majority works elsewhere.

Financial services, insurance, real estate, professional services, transportation, warehousing, and wholesale are among the industries employing technology specialists.

Since 2022, the finance, insurance, and real estate sector added the largest number of U.S. technology jobs among the industries cited in the report, with 90,530 positions. Professional services and transportation, warehousing, and wholesale each added roughly 66,000 technology jobs during the same period.

Growth also varied considerably by occupation.

U.S. employment of data scientists increased 12.4%, or 29,000 jobs, in 2025. Computer and information systems managers increased 3.8%, or 24,600 jobs. Technology and engineering occupations added another 15,030 positions.

These figures reinforce the distinction between the technology industry and the broader technology talent workforce. A region can gain or lose technology workers even when employment trends at traditional technology companies tell a different story.

AI Talent Remains Concentrated in the Bay Area

Artificial intelligence is one of the clearest areas in which the Bay Area continues to stand apart in CBRE’s analysis.

The number of AI-skilled technology workers across the United States and Canada increased 45% year over year to 751,000 by mid-2026, according to the report.

The San Francisco Bay Area, New York Metro, Seattle, and Washington, D.C., together accounted for 37% of U.S. AI-specialty talent.

CBRE also reported that the Bay Area has attracted 80% of U.S. AI venture funding since 2020 and contains one-sixth of the country’s AI-specialty talent, based on data from PitchBook and LinkedIn Talent Insights.

AI-related companies have also influenced San Francisco’s commercial real estate market. According to the report, they accounted for 30% of office-market activity in the city since 2023.

Those figures help explain why the region can retain a high overall technology talent ranking despite a decline in total workforce size. The composition and concentration of technology employment remain important parts of CBRE’s assessment.

Workplace Patterns Continue to Change

Bay Area Tech Workforce Falls Behind New York, Still Ranks No. 1

Photo Credit: Unsplash.com

The shift toward AI has coincided with another change in the Bay Area labor market: fewer technology positions are being advertised as remote.

Remote postings represented 7% of Bay Area technology talent job openings in April 2026, down from 24% in mid-2022, according to CBRE.

The Bay Area figure was also below the 18% remote share for U.S. technology talent postings overall.

CBRE connected the decline with the increased use of hybrid arrangements and hiring by AI companies, which the report said largely favor full-time, in-person work.

The change adds another layer to the region’s workforce transition. The Bay Area is not only seeing different types of technology jobs. Employers are also changing where and how those roles are performed.

The Ranking and Headcount Tell Different Stories

New York’s move ahead of the Bay Area in total technology talent workforce size represents a notable change in the geography of U.S. technology employment.

The broader data, however, shows that workforce size is only one measure of a technology market.

The Bay Area tech workforce is smaller than it was in 2022, while AI-focused job postings have become a much larger share of current demand. The region also retains a high concentration of technology workers, significant AI-specialty talent, and CBRE’s highest overall market ranking.

New York now leads in total technology talent workers. The San Francisco Bay Area continues to lead CBRE’s broader scorecard. Together, those findings point to a technology labor market in which headcount, specialization, hiring demand, and market concentration are increasingly telling different parts of the story.

Frequently Asked Questions

Did New York surpass the Bay Area in tech workers?

Yes. CBRE reported that New York Metro had 394,300 technology talent workers in 2025, compared with 375,730 in the San Francisco Bay Area. The comparison placed New York ahead in total workforce size.

How large is the Bay Area tech workforce?

CBRE’s 2025 employment data puts the Bay Area tech workforce at 375,730 technology talent workers. The report also provides separate 2026 figures on job postings, AI-related roles, and workplace trends.

Does the Bay Area still rank first for technology talent?

Yes. The San Francisco Bay Area remained first overall in CBRE’s technology talent scorecard. The ranking considers 13 metrics rather than relying only on total workforce size.

How much did the Bay Area tech workforce decline?

The Bay Area lost 23,900 technology talent workers between 2022 and 2025. CBRE described the change as a 6% decline from the region’s 2022 level.

How important is AI to Bay Area technology hiring?

AI-related positions accounted for 57% of available Bay Area technology talent job postings by June 2026, according to CBRE. That compares with 20% when overall technology job postings last peaked in mid-2022.

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Beyond the Algorithm and Josh Kovacevic on How AI Is Reshaping Music and Streaming

For more than a decade, the race to personalize music has largely focused on one question.

What should you listen to next?

Streaming platforms have become remarkably effective at answering it.

Algorithms can study listening histories, identify patterns, anticipate preferences and recommend songs or artists that a listener may never have discovered independently. What once required browsing record stores, listening to radio stations or exchanging recommendations with friends can now happen automatically.

Josh Kovacevic believes that achievement may represent only the first chapter of personalized music.

The founder and CEO of AiSound, Inc., the company behind the upcoming consumer platform AuClair, sees artificial intelligence opening the door to a different kind of personalization, one that moves beyond simply predicting what someone wants to hear and begins considering the individual who is actually listening.

His thesis starts with a basic observation:

People hear differently. So why should everyone receive essentially the same audio experience?

That question has helped shape years of technology development, patented intellectual property, and Kovacevic’s broader vision for where music and streaming could be heading next.

Streaming Transformed Discovery. What Comes Next?

The rise of streaming changed the economics and accessibility of music.

Listeners gained nearly instantaneous access to enormous catalogs. As those catalogs expanded, a new challenge emerged around helping people navigate them.

Recommendation algorithms became the answer.

Platforms learned to interpret listening behavior and turn massive libraries into increasingly personalized feeds, playlists, and suggestions.

That created an important first generation of personalization.

But Kovacevic believes there is a distinction between personalizing content selection and personalizing the experience of that content.

Today’s systems can become remarkably sophisticated at predicting whether someone might enjoy a particular song. The audio itself, however, is still generally delivered through a standardized framework.

For Kovacevic, that gap represents an opportunity.

The next era of music technology, he believes, could involve systems becoming better at understanding the individual listener, not simply their history of clicks, skips and streams.

Every Listener Is Different

The premise behind Kovacevic’s work is rooted in individual differences.

Listening preferences vary dramatically. More fundamentally, hearing itself can vary between people and even between an individual’s ears. Many people also experience some degree of hearing difference.

Yet digital audio is still largely built around the assumption that the same underlying experience can serve everyone.

Kovacevic has spent years challenging that assumption.

AiSound’s approach is built around adapting the listening experience to the individual, including how a person hears, what they prefer, and the environment in which they are listening.

His interest ultimately resulted in intellectual property centered on personalized audio. He is the inventor behind patented technology, including U.S. Patent No. 10,838,686, Artificial Intelligence to Enhance a Listening Experience.

That work became part of the foundation for AiSound, the company Kovacevic founded around personalized audio and individualized technology.

AiSound has since moved from an original concept and intellectual property through years of technology and product development toward commercialization.

The broader philosophy is simple enough.

People are not one-size-fits-all, so their technology increasingly shouldn’t be either.

From Recommendation to Individualization

The distinction Kovacevic draws between recommendation and individualization could become increasingly important as artificial intelligence develops.

Recommendation systems primarily seek to understand preference.

What music do you like?

Which artist are you likely to listen to?

What playlist fits your behavior?

Individualization asks a different set of questions about the person experiencing the technology.

Kovacevic believes AI could help move technology further in that direction.

The idea is not to abandon recommendation. Instead, it is to imagine what becomes possible when personalization evolves beyond deciding which piece of content appears next.

In music, that could represent a broader transition from platforms primarily understanding a listener’s taste toward technology that better understands the individual and adapts experiences accordingly.

That evolution also reflects Kovacevic’s larger view of artificial intelligence.

While much of the AI conversation centers on automation and generation, he believes some of the technology’s most meaningful applications may come from its ability to make existing experiences more personal, accessible and useful.

Music is one place where that potential becomes particularly tangible.

Building AiSound Around the Listener

Kovacevic did not arrive at personalized audio through a conventional music-industry career.

His professional background spans medical devices and healthcare, consumer technology, product development, international manufacturing, entrepreneurship and business development.

Those experiences exposed him to industries in which understanding the individual can matter significantly.

Eventually, the same perspective influenced how he thought about consumer technology.

The closer technology gets to people, Kovacevic concluded, the less logical it becomes to assume that everyone should interact with or experience it identically.

AiSound was built around exploring that premise through audio.

Doing so required combining several complicated disciplines: artificial intelligence, software, audio technology, intellectual property, consumer product development and music.

Kovacevic has secured private backing for the company’s development and assembled an international organization spanning engineering, product, music, business and operations.

AiSound has also spent years building relationships throughout the global music industry while moving its technology toward commercialization.

But the company’s ambitions are not centered solely on demonstrating what AI can technically accomplish.

For Kovacevic, the more important question is whether that technology can create a better experience for people.

Where Accessibility Fits Into the Future of Audio

Individualized audio also raises broader questions about accessibility.

If people experience sound differently, increasingly adaptive technology could potentially create experiences that are more personally relevant to listeners with different hearing abilities.

AiSound’s technology is not positioned as diagnosing, treating or curing hearing loss or any other medical condition.

Instead, Kovacevic’s focus is on personalization and accessibility, the possibility that technology can become better at adapting experiences around individuals while preserving what makes music valuable in the first place.

Music is emotional, creative and deeply human.

Kovacevic does not view AI’s role as removing those qualities.

His broader view is almost the opposite. Technology should help people experience more, connect more and receive greater value from the content and products around them.

That represents a noticeably different narrative from the idea that artificial intelligence is primarily about replacing human involvement.

In Kovacevic’s vision, intelligence is valuable because it can make technology more responsive to humanity.

AuClair Brings the Vision Toward Consumers

The next stage of that vision is beginning to emerge through AuClair.

AuClair is AiSound’s upcoming consumer platform, designed to bring the company’s broader philosophy of personalization into music.

Additional functionality and commercial details will be introduced as AuClair approaches launch.

Its significance, however, is already clear within the larger AiSound story.

AuClair represents an effort to translate years of intellectual property, development and experimentation into a consumer-facing platform.

It also arrives as listeners are becoming increasingly accustomed to personalized digital experiences.

Consumers already expect their streaming homepages, recommendations and playlists to reflect their preferences. Kovacevic believes those expectations may continue expanding as AI becomes more sophisticated.

Eventually, personalization may mean considerably more than an algorithm knowing which song someone is likely to enjoy.

It may mean technology becoming better at understanding the person on the other side of the device.

A Broader Opportunity Across Audio

While AuClair represents an important consumer direction, AiSound’s intellectual property has potential applications across the broader audio ecosystem.

That makes Kovacevic’s underlying thesis larger than any single music platform.

Streaming may simply provide one of the clearest examples of a wider technological transition.

The internet connected people to nearly unlimited information.

Smartphones made computing personal and portable.

Algorithms learned to predict behavior.

Artificial intelligence may now provide technology with increasingly sophisticated ways of adapting around individuals.

If that progression continues, Kovacevic believes personalization could eventually become less of a differentiating feature and more of a baseline expectation.

Consumers may begin asking not simply whether technology is powerful, but whether that power is being used to create an experience that actually understands them.

The Next Era of Listening

Streaming’s first revolution was access.

Its second was discovery and recommendation.

Kovacevic believes the next could be individualization.

The shift will not happen overnight. Building technology without an established blueprint has already required years of development, iteration and persistence for AiSound.

But Kovacevic’s conviction comes from seeing personalization as part of a much larger movement in technology.

Artificial intelligence is giving developers new ways to analyze, understand, and respond to differences between users. The companies that successfully translate those capabilities into genuinely useful experiences could help redefine what consumers expect from technology.

For music, that creates an intriguing possibility.

The algorithm already knows what song you may want to hear next.

The next frontier may be building technology that better understands you.

And for Kovacevic, AiSound and the emerging AuClair platform, that is where the next era of personalized audio begins.