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VLSI? or AI what in AI?
In a world increasingly powered by microchips, Very Large-Scale Integration (VLSI) is the bedrock of modern electronics. From smartphones and laptops to electric vehicles and AI-driven data centers, VLSI design enables the technology we rely on daily.
Many countries in ASIA, like India, offer specialized training that costs a lot. Is the investment worth it? What is the ROI (Return on Investment) of VLSI training in today’s competitive market?
Having done a bachelor's in information technology (IT) and specialized skill training in the US but I lack enough experience in IT, and this project-based teaching appeals to me. Despite all this training, I am jobless. I lean toward these on-the-job cohorts from LinkedIn, which costs a lot, and wonder if it's worth it? All offer placement guarantees, mock interviews, and show income projections too good to be true. The same goes for data analytics or cybersecurity. I am having difficulty understanding what to do and how best to approach it. Should I be more focused on AI or be specialized in other than AI things.
any guidance is sincerely appreciated
4 answers
Updated
Wyatt’s Answer
Hello Bansuri,
I have over 25 years of experience in the technology sector, having built my career at a high end technology consulting firm. I began as an engineer and steadily advanced into executive leadership, ultimately overseeing several technology departments and driving strategic initiatives across the organization.
In my opinion, you’re at a critical decision point, but there are also many considerations in terms of options you have at your disposal. With your background in IT and training in the US, you’re already positioned to pivot into fast-growing fields, but it’s important to choose wisely. Very Large Scale Integration is a highly specialized area focused on hardware and semiconductor design, typically requiring a solid foundation in electronics or electrical engineering. While demand exists, especially in Asia and among major chipmakers like Intel and Qualcomm and others, breaking into VLSI from a software or IT background can be difficult and time-consuming. Again my opinion, it may not provide the ROI you’re looking for unless you’re truly passionate about hardware and willing to commit to a niche path which can be a double edge sword early in your career path. On the other hand, AI and related fields like data analytics and cybersecurity are more aligned with your IT foundation and offer broader entry points and job opportunities across industries and offers perhaps a much broader opportunity.
That said, the flood of training programs, even those backed by LinkedIn or with “placement guarantees” can be misleading (I know firsthand early in my career). Many overpromise and underdeliver. Income projections in marketing materials often reflect best case scenarios or cherry picked success stories. Realistically, companies hiring in AI or data fields are looking for demonstrable project experience, critical thinking, and problem solving skills, not just certificates (although certifications are very important in addition to a foundational college degree). If you’re considering expensive training programs, make sure they include personalized mentoring, a strong project portfolio, and real alumni job outcomes, not just flashy job titles on their landing pages. At the company I worked for, I provided ongoing training, certifications, boot camps, conferences, etc. to our technical employees regardless of there experience often investing heavily in younger less experienced employees to build their technical skills for advancement. Remember, these certifications stay with the person, so this is a huge advantage to build your career on your employers dime. You might benefit more from affordable platforms like Coursera, DataCamp, or even free high quality resources like Fast.ai or Google’s Career Certificates. Focus on building a public project portfolio (e.g., GitHub, Kaggle) and developing job relevant skills in a tangible, visible way, something you can showcase to employers right away.
Ultimately, you’re not alone in feeling stuck despite your education and effort. This happens to many who are transitioning or re-entering tech fields without work experience. What you need is clarity and structure. Pick one path, AI/ML, data analytics, or cybersecurity, based on what genuinely interests you, and commit to it fully for the next 3–6 months short term and remember this sort of tech space comes with a commitment to ongoing education for most likely the rest of your career. This is a good thing, as you will always stay relevant in the job market. Set a clear timeline, a learning plan with real projects, and begin actively applying to roles while networking on LinkedIn or with alumni from your training programs. Finding alumni in similar fields is often a great way to connect with someone who is willing to assist with their Ala Mater. Avoid course hoarding and distractions. Instead, focus on demonstrating value through action. Build something, share it, and talk about it. You’re not far from employability, you just need to align your efforts more strategically with the current market and your strengths.
I wish you all the best, it’s truly inspiring to see the next generation stepping up to carry the torch forward as the previous era of technology professionals begins to phase out of the workplace.
Wyatt
I have over 25 years of experience in the technology sector, having built my career at a high end technology consulting firm. I began as an engineer and steadily advanced into executive leadership, ultimately overseeing several technology departments and driving strategic initiatives across the organization.
In my opinion, you’re at a critical decision point, but there are also many considerations in terms of options you have at your disposal. With your background in IT and training in the US, you’re already positioned to pivot into fast-growing fields, but it’s important to choose wisely. Very Large Scale Integration is a highly specialized area focused on hardware and semiconductor design, typically requiring a solid foundation in electronics or electrical engineering. While demand exists, especially in Asia and among major chipmakers like Intel and Qualcomm and others, breaking into VLSI from a software or IT background can be difficult and time-consuming. Again my opinion, it may not provide the ROI you’re looking for unless you’re truly passionate about hardware and willing to commit to a niche path which can be a double edge sword early in your career path. On the other hand, AI and related fields like data analytics and cybersecurity are more aligned with your IT foundation and offer broader entry points and job opportunities across industries and offers perhaps a much broader opportunity.
That said, the flood of training programs, even those backed by LinkedIn or with “placement guarantees” can be misleading (I know firsthand early in my career). Many overpromise and underdeliver. Income projections in marketing materials often reflect best case scenarios or cherry picked success stories. Realistically, companies hiring in AI or data fields are looking for demonstrable project experience, critical thinking, and problem solving skills, not just certificates (although certifications are very important in addition to a foundational college degree). If you’re considering expensive training programs, make sure they include personalized mentoring, a strong project portfolio, and real alumni job outcomes, not just flashy job titles on their landing pages. At the company I worked for, I provided ongoing training, certifications, boot camps, conferences, etc. to our technical employees regardless of there experience often investing heavily in younger less experienced employees to build their technical skills for advancement. Remember, these certifications stay with the person, so this is a huge advantage to build your career on your employers dime. You might benefit more from affordable platforms like Coursera, DataCamp, or even free high quality resources like Fast.ai or Google’s Career Certificates. Focus on building a public project portfolio (e.g., GitHub, Kaggle) and developing job relevant skills in a tangible, visible way, something you can showcase to employers right away.
Ultimately, you’re not alone in feeling stuck despite your education and effort. This happens to many who are transitioning or re-entering tech fields without work experience. What you need is clarity and structure. Pick one path, AI/ML, data analytics, or cybersecurity, based on what genuinely interests you, and commit to it fully for the next 3–6 months short term and remember this sort of tech space comes with a commitment to ongoing education for most likely the rest of your career. This is a good thing, as you will always stay relevant in the job market. Set a clear timeline, a learning plan with real projects, and begin actively applying to roles while networking on LinkedIn or with alumni from your training programs. Finding alumni in similar fields is often a great way to connect with someone who is willing to assist with their Ala Mater. Avoid course hoarding and distractions. Instead, focus on demonstrating value through action. Build something, share it, and talk about it. You’re not far from employability, you just need to align your efforts more strategically with the current market and your strengths.
I wish you all the best, it’s truly inspiring to see the next generation stepping up to carry the torch forward as the previous era of technology professionals begins to phase out of the workplace.
Wyatt
Thank you very much. I sincerely appreciate your advice. Mr. Wyatt, I must have touched a nerve. Wow, such a detailed answer. This is Zillion $$ advice. I love it.
Bansuri
the future programmer isn't just a coder; they are increasingly a conductor, a prompt engineer, and a critical evaluator of AI-generated solutions.
Bansuri
People learn most things through the environment they are in, the experiences they have, and the people they engage with
Bansuri
For decades, success in tech was defined by hard skills: coding languages, system architecture, algorithmic thinking. Its AI age now
Bansuri
You’re welcome, Bansuri! Before I stepped away from the workforce a couple of years ago, mainstream companies were already deeply engaged with AI. While AI has been a key part of high-tech business strategies for about a decade, its roots in business applications stretch back much further, well before the current hype. Much like technologies such as virtualization, cloud computing, and machine learning our company was working in these areas far before they were the newest craze. At our company, a cutting-edge technology consulting firm, AI had been a major focus for over ten years. It’s funny how it often takes the media to spark public awareness about technologies that have actually been around and quietly transforming industries for a long time.
Wyatt .
You said
Realistically, companies hiring in AI or data fields are looking for demonstrable project experience, critical thinking, and problem solving skills, not just certificates (although certifications are very important in addition to a foundational college degree).
I have IT degree and looking just for all that you mention here. Training thru IBM SkillBuild, Coursera, DataQuest SimpliLearn Purdue U Online seems like not enough. Volunteering yes but practical demonstrable project experience is key. How where to get it?
Bansuri
Have you looked into capstone focused bootcamps or apprenticeship programs? It sounds like you’ve already covered most of the formal education needed at the foundational level. The challenge, as you now know, is that experience often weighs heavily in the tech job market. To bridge that gap, start applying for internships, apprenticeships, or entry level positions with a data focus such as data analyst, junior data scientist, or ML ops support or even junior roles in IT departments, particularly within industries you’re already familiar with through your IT education background. These can be strong stepping stones into the data and AI field.
Wyatt .
Keep in mind that entry level roles are just the first step on your journey, they’re meant to position you for where you want to be in a few years. In my career in tech and engineering, I’ve never seen anyone land a specialized IT role without first gaining experience through an internship or an entry level position. Your chosen field is highly specialized, and that is a wonderful thing, much like medicine, law, or accounting. Just as doctors, attorneys, and CPAs start with internships, junior roles, or support positions, so too must professionals in your area. It can be frustrating at times, but you’re on the right track. Stay patient the opportunity to unlock your full potential is ahead.
Wyatt .
James Constantine Frangos
SOFTWARE ENGINEER SINCE 1972; NUTRITIONIST SINCE 1976.
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Answers
Updated
James Constantine’s Answer
Hi Bansuri!
VLSI is key in electronics. Nanometer transistors the size of macromolecules. More transistors in a device, than people on earth! We should focus on integrating people too. VLSI will be utilized in seeking full control of what consumers purchase, lifestyles, and functionality. Can you see where this is headed? AI is growing fast (with our help). We should aim to lead well. AI will create new jobs, but it might also take some away. I feel something is off; is this just business as usual?
Specialize and diversify! That sounds like a contradictory approach, but it good to concentrate on your strengths, specialize in them whilst not missing other opportunities that eventuate. Specialize in a coding language like Python because you can teach yourself. SEE https://codefinity.com/get-started/spa/v8_new_brl ALSO https://www.coursera.org/learn/ai-python-for-beginners/paidmedia ALSO https://www.pluralsight.com/courses/python-best-practices-code-quality Generalize in something like accounting, bookkeeping, consulting, volunteering, or doing remote searches for people.
VLSI is key in electronics. Nanometer transistors the size of macromolecules. More transistors in a device, than people on earth! We should focus on integrating people too. VLSI will be utilized in seeking full control of what consumers purchase, lifestyles, and functionality. Can you see where this is headed? AI is growing fast (with our help). We should aim to lead well. AI will create new jobs, but it might also take some away. I feel something is off; is this just business as usual?
Specialize and diversify! That sounds like a contradictory approach, but it good to concentrate on your strengths, specialize in them whilst not missing other opportunities that eventuate. Specialize in a coding language like Python because you can teach yourself. SEE https://codefinity.com/get-started/spa/v8_new_brl ALSO https://www.coursera.org/learn/ai-python-for-beginners/paidmedia ALSO https://www.pluralsight.com/courses/python-best-practices-code-quality Generalize in something like accounting, bookkeeping, consulting, volunteering, or doing remote searches for people.
I appreciate your support, James Constantine
Bansuri
AI is newAge; VLSI is full control.Strange world we live in
Bansuri
My FB Intro:- "A time traveler a long way from home. Has a strange tendency to stand up for People not Persecute."
James Constantine Frangos
Keats. “I am certain of nothing but the holiness of the heart’s affection and the truth of the imagination.”
Bansuri
Updated
Krishnan’s Answer
I can share a relevant example from my network: a relative pursued an embedded systems specialization immediately after completing his bachelor's degree in electronics engineering. This targeted coursework proved instrumental in securing his first industry position, demonstrating the value of aligning postgraduate studies with career objectives.
Regarding VLSI coursework, I would recommend careful consideration of this path. As other contributors have noted, VLSI represents a highly specialized field that typically requires a strong foundation in related undergraduate coursework. Without this prerequisite knowledge base, you may face significant challenges in both comprehension and practical application.
Instead, I would strongly recommend exploring cybersecurity as a career trajectory, particularly if it aligns with your interests. While entry-level positions in this field are predominantly operational in nature, the cybersecurity landscape offers substantial growth opportunities through continuous professional development. Building a robust portfolio of industry certifications will be essential for career advancement and positioning yourself for more strategic roles.
Additionally, the integration of artificial intelligence into cybersecurity practices is accelerating rapidly. Maintaining current knowledge of AI applications and methodologies will provide you with a competitive advantage and ensure your skillset remains relevant in an evolving market. This dual expertise in cybersecurity and AI positions you well for emerging roles at the intersection of these disciplines.
Regarding VLSI coursework, I would recommend careful consideration of this path. As other contributors have noted, VLSI represents a highly specialized field that typically requires a strong foundation in related undergraduate coursework. Without this prerequisite knowledge base, you may face significant challenges in both comprehension and practical application.
Instead, I would strongly recommend exploring cybersecurity as a career trajectory, particularly if it aligns with your interests. While entry-level positions in this field are predominantly operational in nature, the cybersecurity landscape offers substantial growth opportunities through continuous professional development. Building a robust portfolio of industry certifications will be essential for career advancement and positioning yourself for more strategic roles.
Additionally, the integration of artificial intelligence into cybersecurity practices is accelerating rapidly. Maintaining current knowledge of AI applications and methodologies will provide you with a competitive advantage and ensure your skillset remains relevant in an evolving market. This dual expertise in cybersecurity and AI positions you well for emerging roles at the intersection of these disciplines.
True. Real, career-changing skills Structured paths in Python, SQL, AI, machine learning, and more Hands-on projects that build your portfolio and confidence at___for a fee. is it worth it
Certifications that prove your progress
Bansuri
Updated
Winnie’s Answer
Thanks for sharing this—it's a deeply relatable and smart question. You're not alone in feeling overwhelmed by the training options and promises in tech right now, especially in fields like VLSI, AI, data analytics, and cybersecurity. Let's break it down so you can make a solid decision based on your background, current market demand, and realistic ROI.
---
🔹 VLSI vs AI (and Other Tech Fields): Which Makes Sense for You?
✅ VLSI (Very Large-Scale Integration)
Best for: People who love hardware, electronics, semiconductors, chip design, and low-level architecture.
Pros: High demand in semiconductor companies (Intel, Qualcomm, NVIDIA, etc.), especially post-global chip shortages.
Cons:
Heavily hardware-focused—if you’re more inclined toward software, this may feel too specialized.
Harder to break in without a master’s or prior industry experience.
ROI can be slow, especially if training is expensive and jobs aren’t guaranteed.
✅ AI / Data Science / Machine Learning
Best for: People with a programming background, interest in math/stats, and problem-solving.
Pros:
Extremely versatile: AI skills can be used in healthcare, finance, marketing, and more.
Higher starting salaries and greater number of job postings.
Cons:
Very saturated—lots of candidates, so hands-on projects, GitHub portfolios, or Kaggle competitions are key.
Requires constant upskilling as the field evolves fast.
✅ Cybersecurity
Best for: Those interested in security, networks, forensics, ethical hacking.
Pros:
Growing demand globally, especially in government, finance, and defense sectors.
Often less saturated than AI.
Cons:
Requires certifications (like CompTIA, CEH, CISSP) and a security mindset.
Entry roles can be more operational before advancing to higher levels.
---
🧠 With Your Background in IT and Project-Based Learning…
You might thrive best in AI or data analytics, given that:
You already have a technical foundation.
You enjoy project-based, practical learning.
These fields are more flexible and open to career-switchers than VLSI.
VLSI, while prestigious and technical, tends to be niche and less accessible without prior electronics background or graduate study.
---
🔍 Be Careful with "Placement Guarantee" Bootcamps
Many bootcamps offer:
"Job placement or your money back"
Income projections like $100K/year post-certification
Red flags:
Hidden terms in the placement guarantee
Poor employer networks
Unrealistic job claims
Generic content without one-on-one coaching
Green flags:
Real alumni testimonials
Industry mentors or hiring partnerships
Projects that are evaluated and added to your portfolio
Strong LinkedIn presence of graduates
---
🎯 Recommendation: A Strategic Plan
1. Choose a direction:
If you like coding + data = go AI/ML or data analytics
If you prefer protecting systems and security = go cybersecurity
If you're really into microchips and electronics = go VLSI, but be ready for a steeper climb
2. Look for affordable, credible programs:
Try Coursera (Google, IBM, or Stanford AI courses)
Consider nonprofit orgs like freeCodeCamp
Build a portfolio of real projects
3. Start freelancing, internships, or volunteering to build experience:
Contribute to GitHub open-source
Do short projects for NGOs or small businesses
4. Network actively on LinkedIn:
Share your learning journey
Connect with alumni from programs you're considering
Attend virtual meetups or career expos
---
💬 Final Thought
Don’t let the pressure of being "jobless" push you into expensive programs unless they prove value clearly. You’re already doing the right thing by asking tough questions.
If you want, I can help you evaluate specific programs or write a strategy plan tailored to your background and goals.
---
🔹 VLSI vs AI (and Other Tech Fields): Which Makes Sense for You?
✅ VLSI (Very Large-Scale Integration)
Best for: People who love hardware, electronics, semiconductors, chip design, and low-level architecture.
Pros: High demand in semiconductor companies (Intel, Qualcomm, NVIDIA, etc.), especially post-global chip shortages.
Cons:
Heavily hardware-focused—if you’re more inclined toward software, this may feel too specialized.
Harder to break in without a master’s or prior industry experience.
ROI can be slow, especially if training is expensive and jobs aren’t guaranteed.
✅ AI / Data Science / Machine Learning
Best for: People with a programming background, interest in math/stats, and problem-solving.
Pros:
Extremely versatile: AI skills can be used in healthcare, finance, marketing, and more.
Higher starting salaries and greater number of job postings.
Cons:
Very saturated—lots of candidates, so hands-on projects, GitHub portfolios, or Kaggle competitions are key.
Requires constant upskilling as the field evolves fast.
✅ Cybersecurity
Best for: Those interested in security, networks, forensics, ethical hacking.
Pros:
Growing demand globally, especially in government, finance, and defense sectors.
Often less saturated than AI.
Cons:
Requires certifications (like CompTIA, CEH, CISSP) and a security mindset.
Entry roles can be more operational before advancing to higher levels.
---
🧠 With Your Background in IT and Project-Based Learning…
You might thrive best in AI or data analytics, given that:
You already have a technical foundation.
You enjoy project-based, practical learning.
These fields are more flexible and open to career-switchers than VLSI.
VLSI, while prestigious and technical, tends to be niche and less accessible without prior electronics background or graduate study.
---
🔍 Be Careful with "Placement Guarantee" Bootcamps
Many bootcamps offer:
"Job placement or your money back"
Income projections like $100K/year post-certification
Red flags:
Hidden terms in the placement guarantee
Poor employer networks
Unrealistic job claims
Generic content without one-on-one coaching
Green flags:
Real alumni testimonials
Industry mentors or hiring partnerships
Projects that are evaluated and added to your portfolio
Strong LinkedIn presence of graduates
---
🎯 Recommendation: A Strategic Plan
1. Choose a direction:
If you like coding + data = go AI/ML or data analytics
If you prefer protecting systems and security = go cybersecurity
If you're really into microchips and electronics = go VLSI, but be ready for a steeper climb
2. Look for affordable, credible programs:
Try Coursera (Google, IBM, or Stanford AI courses)
Consider nonprofit orgs like freeCodeCamp
Build a portfolio of real projects
3. Start freelancing, internships, or volunteering to build experience:
Contribute to GitHub open-source
Do short projects for NGOs or small businesses
4. Network actively on LinkedIn:
Share your learning journey
Connect with alumni from programs you're considering
Attend virtual meetups or career expos
---
💬 Final Thought
Don’t let the pressure of being "jobless" push you into expensive programs unless they prove value clearly. You’re already doing the right thing by asking tough questions.
If you want, I can help you evaluate specific programs or write a strategy plan tailored to your background and goals.
Thanks-No Liking for Cyber-Very saturated—lots of candidates. Love Project but not Math/stats. Prompting/coding/Programming my thing
Bansuri
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