AI & Agentic Coding: Deployment Impact Map

How fast and how many job functions can AI actually access, accelerate, or replace? 

To answer that, I have scored every role across five dimensions drawing on my experience across four very different industry sectors and what I have observed during my career stints. I have then mapped the results onto four axes to visualize how deploying AI can bring in gains in productivity, innovation or accelerate future cash inflows to an earlier period (NPV pull-in).

The framework reveals roles that AI can enable rapid uptake in productivity versus those requiring longer ramp times but offering transformational innovation potential.

AI & Agentic coding — Deployment Impact Map
Bubble size = NPV pull-in score · hover for detail
Sector
© Rohan Hubli
Industry-benchmarked Assessments refined by years of hands-on sector experience.& agentic AI deployment research · 2025–2026

Key Insights:

  • Roles are mapped on productivity gain vs. innovation potential, with bubble size showing NPV pull-in (cash flow acceleration)
  • High automation: QA engineers, Software Engineers, Data Analysts (codified knowledge, measurable outputs)
  • Limited automation: Directors, System Architects, Product Managers (tacit/institutional knowledge dependent)

⏱ Time to Meaningful Productivity: How quickly can an AI-assisted practitioner reach productive output? Roles with short ramp times — where tasks are well-defined and outputs are measurable — score higher. A QA engineer running AI-generated test suites is productive in days; a chip architect validating a micro-arch decision takes quarters.

💡Time to True Innovation: Beyond productivity lies a harder question: can AI expand the creative frontier of a role? We scored roles on how quickly AI can meaningfully shift what is possible — not just faster, but genuinely new. AI/ML diagnostic developers and micro-architects score highest here; project managers and QA engineers score lowest.

📋Codified Knowledge Rank | Manuals · Rules · SOPs · Standards

Knowledge that has been written down, structured, and made explicit. It lives in documentation, specifications, regulations, and repeatable processes. AI was essentially built for this. Roles that operate primarily from documented rules (regulatory submissions, test plans, RTL standards) are highly automatable. High Codified Knowledge Rank = High AI leverage.

Example roles:

  • QA Engineer writing test plans from specs ·
  • Regulatory Affairs drafting 510(k) submissions ·
  • Verification Engineer generating UVM testbenches from design documents

AI impact: Very High. Agentic tools can read, reason over, and generate codified knowledge at scale. Productivity gains of 40–80% are achievable and measurable quickly.

🧠Tacit Knowledge Rank | Intuition · Sensory judgment · Experience

Knowledge that lives in the body and mind of the practitioner — built through years of doing. It cannot be fully written down — is the hardest for AI to replicate. An analog circuit designer “feels” a layout. A clinical engineer reads a patient population intuitively.

Example roles:

  • Analog/Mixed-signal Engineer tuning a PLL
  • Senior System Architect analyzing performance requirements based on intended use case
  • Experienced BD leader reading a room in a government briefing

AI impact: Moderate. AI can accelerate adjacent tasks and surface patterns but cannot replicate the judgment itself. It augments the expert rather than replacing the expertise.

🏛 Institutional Knowledge Rank | Local context · Politics · Relationships

Knowledge that exists in the history, culture, and relationships of a specific organization or ecosystem. Who to call, what really happened in that program, which stakeholder actually holds the veto. AI has no access to any of this.

Example roles:

  • Medical Affairs Advisor navigating PMA with FDA
  • Director of Engineering navigating organizational politics
  • Defense markets, attending a government agency meeting for discerning next generation program requirements

AI impact: Low. The core value of these roles is irreplaceable human capital. AI can handle peripheral tasks but cannot substitute for trust, access, or organizational context.

A follow-on post will delve into Economics of Photonic Startup in the age of AI, a real-world scenario of how it played out.

Breaking the Innovation Speed Barrier with AI: The Golden Age of Innovation

Synopsis: As AI transitions from assisting with small tasks to autonomously managing full engineering work blocks, the innovator’s role is shifting from “doer” to “owner.” Drawing on a hands-on experiment building three Android apps in three months, this post explores why real-world deployment remains AI’s critical bottleneck, why human sagacity now commands a premium, and eight structural shifts redefining innovation, labor, and institutional power in an AI-driven economy.

The business world is filled with innovations that have disrupted industries, displaced incumbents and diminished gatekeepers alike. Yet behind the glossy roster of successful innovators lies a graveyard of promising startups that never crossed the chasm from early adoption to mainstream success. These failures typically stem from execution risk, market misalignment, strategic missteps, or shifting macroeconomic conditions. For many entrepreneurs, securing capital, talent, networks, and ecosystem support remains an insurmountable hurdle right from the start.

“We are entering a new era of competition defined by dynamic experimentation and real-time agility, shifting the current landscape in profound ways.”

AI and The Golden Age of Innovation

With the exponential growth in large language model (LLM) capabilities, it’s become clear that we are transitioning out of an era where AI merely assists with “snippets” of work (seconds or minutes) and into one where it can autonomously manage full work blocks (hours or even days).

(Adapted from IEEE Spectrum: Large Language Models Are Improving Exponentially)

As AI begins to handle hour-long engineering tasks, the human role is shifting from “doing the work” to “defining, owning, and shepherding what the AI produces.” The real advantage now accrues to those who can pair AI’s execution power with distinctly human capabilities—curiosity, observation, reflection, foresight and a holistic view—to uncover unmet needs and turn them into breakthrough innovations.

The AI Skeptic in me: For years, I approached AI with healthy skepticism but remained curious. An early undergraduate project involving neural networks and a SCARA robotic control system left me underwhelmed by the disconnect between academic hype and practical utility. That experience taught me to judge AI not by its promises, but by its execution—which is what drove me to run my own real-world test.

AI: The Doer | Human: The Owner

To test this hypothesis, I set out to develop a series of Android smartphone applications. Leveraging modern AI coding assistants alongside my existing development background, I identified an unmet need, defined the product vision, and scoped minimum viable product (MVP) features—then handed the heavy lifting to AI.

The results were striking; where developing a single production-ready Android app would traditionally take three months, I successfully built three increasingly complex applications in that same timeframe. This was largely accomplished through focused weekend sprints and iterative feature rollouts. My involvement shifted dramatically: roughly 20% of my time was spent refining code nuances and UI polish, while 80% went to testing. In fact, AI’s development speed consistently outpaced my capacity to validate the feature set.

AI Execution Speed: The Real-World Deployment Bottleneck

For my Shopping List Manager app, testing geo-fence notifications and optimizing algorithms to minimize battery drain became the primary bottleneck. I simply couldn’t keep pace with the rapid bug fixes and iterative tweaks AI kept deploying. The execution velocity is unprecedented in terms of productivity gains.

AI’s Current Blind Spot: Real-World Interaction

Similarly, debugging server connectivity and monitoring API calls for the HQPlayer Smart Configuration Manager required constant human oversight. Ensuring the MyInventory Tracking app functioned correctly across devices—especially when dealing with missing permissions or disabled system settings—demanded real-world intervention. These experiences highlight a critical truth: while AI excels at generating code, it still struggles with the unpredictable, context-heavy realities of live deployment.

Rules and Implications for an AI-Driven Economy

As this shift accelerates, several patterns are emerging across technical, human, and socioeconomic dimensions:

Real-World & Operational Realities

  1. Real-world deployment remains a human endeavor. Physical devices, network variability, and user environments will continue to cap purely AI-driven productivity gains.
  2. Security, interoperability, and consistency will emerge as critical bottlenecks. As AI generates code at scale, ensuring it integrates safely and predictably across fragmented ecosystems will require rigorous human oversight.

The Human Advantage

  1. Creative and cognitive traits will command a premium. Imagination, inquisitiveness, observation, reflection, synthesis, and foresight are becoming the new competitive moats. AI handles execution; humans handle direction.
  2. Domain knowledge is transferable; intrinsic wisdom is not. While AI can rapidly learn technical frameworks, it will struggle to replicate perception, common sense, empathy, intuition, and contextual awareness. These qualities remain deeply human.

Socioeconomic & Institutional Shifts

  1. Social cohesion may require deliberate protection. As personalized AI assistants become ubiquitous, we must actively guard against social isolation, reduced civic participation, and diminished creative expression.
  2. Labor market shifts will pressure policy frameworks. Widespread automation could make safety-net models like universal basic income (UBI) or wage subsidies increasingly relevant discussions for governments worldwide.
  3. Demographically challenged nations stand to gain the most. Countries with aging populations and shrinking workforces will likely reap the greatest productivity offsets from AI integration.
  4. Traditional gatekeepers will see their influence diminish. Domain-specific AI tools will democratize access and erode the strategic leverage historically held by legacy institutions in finance, logistics, legal, transportation, healthcare, entertainment, commerce, and education.

The Road Ahead

The bottleneck is no longer how fast we can build—it’s how wisely we can deploy, test, and align new creations with human needs. AI has handed us the keys to unprecedented execution speed, but it hasn’t replaced the need for vision, judgment, and real-world accountability.

We are entering a golden age of innovation not because AI can do everything, but because it frees us to focus on what only humans can: ask the right questions, navigate ambiguity, and shepherd ideas from concept to impact.

The question for founders, builders, and leaders is no longer “What can we make?” but “What should we make, and how do we ensure it thrives in the real world?”

Co-Packaged Intra-Link Photonic Transceiver Market – Technology Drivers and Application Segments

High Performance Compute Roadmap: 2025-2030

Advanced Packaging- Enabling Next Generation Silicon Chips

Key Takeaways:

  1. Increasing Design and Manufacturing Complexity associated with silicon chip development at < 5nm nodes and skyrocketing Development Costs will accelerate the use of Advanced Packaging across all market segments.
  2. The HPC/Server, Networking and High-End Smartphone market will be the “Lead Adopters” for Advanced Packaging Solutions.
  3. Co-Packaged Optics will see a major uptick to address Compute and I/O bottlenecks in Distributed Deep Learning and Datacenter market.
  4. By 2027, the Consumer, Automotive, Defense, Aerospace, Industrial and Medical market segments will also increasingly adopt Advanced Packaging driven by new innovations, standardizations and price erosion as the technology matures.
  5. Discrete Components will see a price erosion as “Motherboard-On-A-Chip” becomes a reality.
  6. In-Memory Compute and Photonics will emerges as the next frontier of innovation; as will novel ways to build monolithic multi-layer silicon chips to address limits of lithography.

Categories

Software Defined Vehicle-A Strategic Roadmap

References:

  • https://www.statista.com/outlook/mmo/passenger-cars/luxury-cars/worldwide#unit-sales
  • https://www.oica.net/category/sales-statistics/
  • https://www.blumeglobal.com/learning/automotive-supply-chain/
  • https://medium.com/next-level-german-engineering/porsche-future-of-code-526eb3de3bbe
  • https://www.osvehicle.com/how-many-sensors-are-in-your-car/
  • https://www.juniperresearch.com/blog/december-2021/the-rising-demand-for-automotive-sensors
  • https://www.counterpointresearch.com/promising-yet-challenging-market-self-driving-socs/
  • Revealing the Complexity of Automotive Software, Volvo Automotive Group (2020)

Datacenter Optical Transceiver Market

IoT Asset Management Solution: Trucking & Fleet Management

A high level architectural view of an IoT Asset Management Solution built from ground up to address the needs of different market segments.

The end use for Trucking and Fleet management tries to address the following key requirements:

  1. Low Cost Solution.
  2. Real-Time tracking even when faced with intermittent connectivity.
  3. Reliability to handle the most demanding terrain and environment.
  4. Scalable and Adaptable to accommodate various use cases.
  5. Security to prevent tampering and unauthorized access.

Asset Tracking & Management: An IoT Strategic Imperative

One of the most compelling use cases in IoT is in the area of Asset Tracking and Management. Asset classes can range from living, nonliving, transitory, stationary, remote to the accessible. An Asset Tracking system designed for one asset class can rarely be redeployed for another asset class as is. Each of the use cases and application scenarios are different and unique.

A further challenge to the emergence of a single dominant platform for managing assets is the heterogenous nature of assets that firms typically employ, even within the same industry. Therein lies the challenge of developing an Asset Tracking system; necessitating a multifaceted approach across various disciplines.

Wastage in Businesses – Inefficiency or Cost of Doing Business?

Every year businesses across all sectors of the economy lose billions of dollars on account of the following:

  1. Excess Inventory on hand.
  2. Low Asset Utilization and/or Asset Loss.
  3. Non-identifiable, Non-trackable and Perishable supplies.
  4. Labor costs associated with idling and/or unnecessary hauling of equipment.
  5. Line stoppages and business interruptions due to missing supplies and/or equipment breakdown.
  6. Cost of expediated transportation.

The above challenges need a systemic approach to tackle the inefficiencies, but you can’t fix what you don’t know.

Asset Tracking System: A Strategic Imperative

Activist investors are pressuring mismanaged firms to undertake a strategic review of how their businesses are run. During the 2017 proxy season, activists launched 327 public campaigns against U.S. companies, with $121 Billion1 under their management. Firms needs to proactively identify areas of weakness in their sphere of activities and call for a course correction.

Here are a few examples where the use of Asset tracking can unleash hidden value, eliminate waste and increase overall efficiency:

  1. In Los Angeles and Long Beach, California home to the busiest container ports in the USA, average truck turn time is around 82 minutes.2
  2. According to a 2017 study by National Retail Federation U.S. businesses lose around $50 billion annually to retail shrinkage.3
  3. Transportation delays in-transit and on customer premises will cost US chemical manufacturers an additional $22 billion in working capital on account of additional inventory held.4
  4. Annually hospitals lose 20% of their equipment. Historically asset utilization in US Hospitals has stayed around 40 percent, which means valuable assets such as IV pumps sit idle 60 percent of the time.5
  5. In the USA, an average city water utility loses 30 percent of the water supplied through leaks or un-billed usage.6

Asset Tracking System: A Competitive Advantage

The Internet has played an important role in the creation of new products-ideas, their diffusion and in levelling the playing field across firms and industries. Gone are the days where quality management systems such as TQM and Six Sigma enabled firms to leap frog competition.

The basis for competition in the hyperconverged world relies on achieving better quality with greater agility, easier provisioning and lower administrative costs. It’s imperative for firms to adopt a system wide view of their activities from procurement, design, manufacturing, operations, delivery, installation to use.

A firm that can leverage enterprise knowledge, integrate best practices and leverage asset tracking data can acquire a competitive advantage over its rival. To get there, firms need to invest on a platform that can leverage multiple data points and in-house knowledge to unlock hidden value.

Asset Tracking: Passive, Active or Intelligent?

At the basic level passive asset tracking involves nothing more than an electronic label and reader (e.g. RFID, NFC). One level higher is Active Tacking which entails connectivity, LBS (Location Based Services) and some form of a sensor coupled to a power source.

An intelligent asset tracking solution adds an extra layer of complexity with On-Board Monitoring, of one or more parameters of interest. Applications that require Real-Time resolution can now handle extreme events and undertake preventative actions.

Asset Tracking and Management – The Six Critical Elements

A compelling Asset Tracking solution requires the delicate act of balancing six critical elements – Sensors, Location Based Services (LBS), Connectivity, Power Consumption, On-Board Monitoring and Analytics.

  1. Sensors: The one analogy that I can think of when it comes to sensors is blood. Like blood, sensors serve three main functions: convey, protect and regulate the asset under observation. Sensors comes in all shapes, forms and functions the choice depends on what one intends to monitor, control and prevent.
  2. Location Based Services (LBS): For assets confined within a certain geographic radius (e.g. hospitals, warehouses, factory floor) one can assign fixed location identifiers or use triangulation (aided by beacons) to pinpoint location. If the asset under consideration involves a moving target (e.g. trucks, drones, mining equipment, shipping containers) that requires real-time monitoring one can select GPS or A-GPS (lower battery drain).
  3. Connectivity: The choice of connectivity often boils down to the tracker location (local vs. remote) and mobility (stationary vs. transitory) constraints. Additional requirements stem from network reach and coverage – PAN, LAN, MAN or WAN. The choice for a reliable connection range from NFC, Bluetooth Low Energy (BLE), ZigBee, ZigBee-IP, IEEE 802.11ah, WLAN to LPWAN (SigFox, LoRa, RPMA, Symphony Link, Weightless, NB-IoT, LTE-M). To offset some of the limitations arising out of cost, security and low power consumption gateway devices are often deployed for last-mile connectivity.
  4. Power Consumption: One of the key design metric in deploying trackers is whether to be battery or grid powered. For remote or unreachable applications, the choice is often forced. Additional constraints that dictate power usage include -Always ON, Alive When Spoken To and Periodic Awake.
  5. On–Board Monitoring: Applications that demand real-time monitoring have an additional constraint: take preventative or corrective actions before it’s too late. In battery powered devices there is a critical requirement to eliminate redundant data transfers or aggregate sensor readings. Both these scenarios call for an On-Board monitoring system.
  6. Analytics: What good is any data if you can’t act on it. The real value in any asset tracking system is knowing how to develop real-world solutions based on the insights gathered. The applications are numerous but just to list a few – Overall Equipment Efficiency, Defect Monitoring & Classification, Predictive & Preventative Maintenance, Trend Analysis and the holy grail using Machine Learning and AI to uncover hidden value.

A follow-on post will look at how various technologies can be leveraged and integrated to build a solution from ground up, specifically for the trucking industry.

References:

  1. The 2017 Proxy Season, Published by J.P. Morgan’s M&A Team, July 2017
  2. Average Monthly Truck Turn Time, Harbor Trucking Association, 2015-2017
  3. National Retail Security Survey 2017, NRF
  4. Transporting growth: Delivering a Chemical Manufacturing Renaissance, American Chemistry Council, March 2017
  5. Industry Survey: Transformative Technology Adoption and Attitudes—Location Technologies, ABI Research, 2Q 2017
  6. Smart Water Network, Navigant Research, 2016
  7. Prince, Jeffrey and Simon, Daniel H., Has the Internet Accelerated the Diffusion of New Products? (April 1, 2009).

Acknowledgements:

In fond memory of Rev Fr. Agnelo Pinto and Rev Fr. Pat D’Lima whose guidance during my adolescent years at St Paul’s High was instrumental in shaping who I am today.