How to Recognize When Technology Has Matured and Isn't Just Hype
The Moment Technology Becomes Boring
My British lilac cat Mochi has no interest in technology until it becomes invisible. She ignores my new gadgets entirely but has strong opinions about the heating system – technology so mature that it just works. That transition from exciting novelty to invisible infrastructure marks true technological maturity. The question is: how do you spot that transition before waiting years?
Every promising technology follows a pattern. Initial excitement. Inflated expectations. Disappointing results. Gradual improvement. Quiet maturity. The challenge for anyone making adoption decisions is identifying where a technology sits on this curve without waiting for hindsight to clarify.
I’ve watched dozens of technology waves over two decades. Some became foundations of modern life. Others became cautionary tales in business school case studies. The technologies that succeeded and the ones that failed often looked identical during their hype phases. The difference only became clear later – unless you knew what signals to watch.
This article provides practical frameworks for distinguishing mature technology from hype. Not academic theory, but observable indicators that predict which technologies will become reliable infrastructure and which will remain perpetual promises.
The stakes are real. Adopting immature technology wastes resources and creates technical debt. Waiting too long cedes competitive advantage. Getting the timing right requires understanding maturation signals that most coverage ignores in favor of excitement or skepticism.
Let me show you what to look for.
The Boring Company Test
The most reliable maturity signal is adoption by boring companies. Not tech giants seeking headlines. Not startups requiring differentiation. Boring companies that need technology to work without drama.
Insurance companies. Regional banks. Municipal governments. Manufacturing firms. Companies that exist in competitive but not revolutionary markets. Companies where IT serves operations rather than defining strategy. Companies that would never make a technology decision for marketing value.
When these organizations adopt a technology, they’ve done extensive due diligence focused purely on reliability and cost-effectiveness. They have no incentive to be early. They have strong incentives to avoid failure. Their adoption signals that a technology has crossed from experimental to dependable.
Cloud computing matured when regional credit unions started migrating. Blockchain remained immature as long as only crypto enthusiasts and innovation labs touched it. Video conferencing matured when local government agencies switched from phone bridges. The boring company test reliably predicted which technologies would become infrastructure.
I track technology maturity through industry publications that boring companies read. Insurance Technology Review. Government Technology Magazine. Manufacturing Business Technology. Coverage in these publications signals maturity more reliably than coverage in TechCrunch or Wired. When boring company publications run how-to guides rather than think pieces, the technology has matured.
Mochi demonstrates similar judgment. She ignores new cat toys until they’ve proven reliable. The automatic feeder took six months of consistent operation before she trusted it. Boring cat behavior mirrors boring company evaluation: skepticism until proven.
The Second Generation Product Test
Mature technologies support second-generation products from multiple vendors. First-generation products pioneer. Second-generation products refine. The existence of refined products from multiple sources signals market validation.
Consider smartphones. First-generation devices were rough. Limited apps. Poor battery life. Unreliable touch screens. Second-generation devices from multiple manufacturers proved the concept had staying power. The iPhone 3G and subsequent Android devices demonstrated that smartphones weren’t a passing novelty.
Electric vehicles showed similar patterns. First-generation EVs like early Tesla Roadsters were pioneering but limited. Second-generation mass-market EVs from multiple manufacturers – Model 3, ID.4, Mach-E – signaled true market maturity.
The key is multiple vendors. A single company releasing second-generation products might indicate company commitment rather than technology maturity. Multiple companies independently investing in second-generation products indicates market-wide validation.
I apply this test by watching product announcements from conservative manufacturers. When Toyota, not Tesla, announces significant EV investment, EVs have matured. When Microsoft, not a startup, releases significant AI developer tools, AI development has matured. The second player from the conservative tier matters more than the fifth player from the innovative tier.
Virtual reality provides a counter-example. Despite multiple generations of products, we still haven’t seen the second-generation mainstream product from conservative manufacturers. Sony’s PSVR efforts remain limited. No boring companies use VR operationally. The technology remains immature despite decades of development.
The Talent Migration Test
Technology maturity shows in labor markets. Where skilled people choose to work indicates where reliability has replaced risk.
During hype phases, technology attracts risk-tolerant talent: young engineers, entrepreneurs, people with high risk tolerance and career flexibility. These early adopters signal excitement but not stability.
As technology matures, talent migration shifts. Experienced engineers from established companies start taking roles. People with mortgages and families join. The talent willing to stake stable careers on the technology signals reduced risk perception by those with most to lose.
I watch LinkedIn job postings and company leadership announcements for these signals. When a 20-year veteran from IBM takes a VP role at a company built on new technology, that technology has matured. When parental leave policies at AI companies start matching traditional tech companies, AI employment has matured.
The counter-signal is equally telling. Technologies that still only attract young, risk-tolerant talent after years of development haven’t proven themselves. If experienced professionals still see career risk in joining, they know something the hype doesn’t acknowledge.
Mochi applies similar evaluation to new environments. She explores cautiously, watching before committing. A new piece of furniture takes days of observation before she’ll nap on it. Mature furniture gets immediate adoption. The feline talent migration test tracks her willingness to invest her most precious resource: sleep.
The Failure Story Test
Mature technologies have well-documented failures. This seems counterintuitive – shouldn’t mature technology avoid failures? – but the documentation of failures signals a crucial shift.
Immature technologies have few documented failures because nobody uses them seriously enough to fail meaningfully. Or failures get explained away as early-stage growing pains. Or the technology’s proponents suppress failure narratives to protect hype.
Mature technologies have enough serious deployments that some inevitably fail. More importantly, the technology’s community has matured enough to discuss failures openly. Postmortems get published. Lessons get shared. The conversation shifts from “this technology is amazing” to “here’s how this technology fails and how to avoid it.”
Cloud computing matured when major outage postmortems became standard content. AWS publishes detailed postmortems after incidents. The cloud community discusses failures openly. This transparency signals maturity – the technology is established enough that honest failure discussion helps rather than threatens it.
I look for failure content in technology evaluation. How-to articles about avoiding common problems. Case studies of unsuccessful implementations. Stack Overflow questions about recovery from failures. These signals indicate that enough people have used the technology seriously enough to fail and learn.
Technologies where all content is promotional – success stories, capability announcements, potential applications – remain immature. The absence of failure content suggests insufficient real-world deployment or a community still protecting its hype.
The Standard Tooling Test
Mature technologies have standardized tooling. Not just tools that work, but tools that everyone expects. Default choices that don’t require justification.
Consider web development. jQuery became the standard JavaScript library not because it was technically superior but because it became the expected default. React’s dominance signals framework maturity. When you can say “we use React” without explanation, the technology ecosystem has matured.
Cloud deployment shows similar patterns. Kubernetes became the expected container orchestration tool. Terraform became the expected infrastructure-as-code tool. The existence of expected defaults indicates ecosystem maturity beyond the core technology.
I evaluate technologies by asking: what’s the obvious choice? If there’s no obvious choice – if choosing requires extensive evaluation – the ecosystem remains immature. If there’s an obvious choice that practitioners would use without extensive justification, maturity has arrived.
The danger signal is continuous churn in tooling. Technologies where the standard choice changes annually remain immature. The churn indicates that no solution has proven sufficiently stable to become the expected default.
Machine learning tooling demonstrates the maturity test well. PyTorch and TensorFlow have become expected defaults for different use cases. MLflow and similar tools have become expected for model management. The existence of these standard choices – tools you’d use without extensive evaluation – signals ML infrastructure maturity.
The Integration Test
Mature technologies integrate with existing systems without heroics. Immature technologies require special handling, custom adapters, and constant maintenance.
This test reflects an often-overlooked aspect of technology adoption: technologies don’t exist in isolation. They must work with existing systems. The effort required for integration indicates maturity more reliably than the technology’s standalone capabilities.
Cloud services became mature when standard integration patterns emerged. APIs stabilized. SDKs covered major languages. Documentation included integration guides. Connecting cloud services to existing systems became predictable rather than adventurous.
I assess integration maturity through documentation quality. Does official documentation include integration scenarios? Do third-party connectors exist? Can someone integrate the technology without heroic engineering effort? Affirmative answers indicate maturity.
The integration test also reveals hidden immaturity. A technology might seem mature based on its core capabilities while remaining practically immature due to integration difficulty. The technology works in demos but fails in production environments with existing systems.
Mochi’s integration with household systems provides an amusing parallel. She integrated smoothly with the existing heating schedule. She has never integrated with the robot vacuum – she attacks it on sight. The vacuum remains immature technology from her perspective, regardless of its capabilities in isolation.
graph TD
A[New Technology Appears] --> B{Boring Companies Adopting?}
B -->|No| C[Still in Hype Phase]
B -->|Yes| D{Second-Gen Products from Multiple Vendors?}
D -->|No| C
D -->|Yes| E{Experienced Talent Migrating?}
E -->|No| C
E -->|Yes| F{Documented Failure Stories Exist?}
F -->|No| C
F -->|Yes| G{Standard Tooling Established?}
G -->|No| C
G -->|Yes| H{Easy Integration with Existing Systems?}
H -->|No| C
H -->|Yes| I[Technology Has Matured]
C --> J[Wait and Reassess]How We Evaluated
Our technology maturity assessment framework developed through systematic analysis of past technology transitions.
Step 1: Historical Pattern Analysis We examined 30 technology transitions from 1995-2025, documenting the timeline from initial hype through maturity or abandonment. Technologies included: cloud computing, smartphones, social media, blockchain, VR/AR, IoT, and various software paradigms.
Step 2: Signal Identification We identified candidate maturity signals and tested their predictive power against historical data. Signals that accurately predicted maturity timing across multiple technologies were retained.
Step 3: Counter-Example Testing We specifically examined technologies that appeared mature by some signals but failed, and technologies that succeeded despite appearing immature. This refined signal reliability assessments.
Step 4: Current Application We applied the framework to current technologies to generate predictions testable in coming years. This forces ongoing framework refinement as predictions succeed or fail.
Step 5: Practitioner Validation We interviewed 25 technology decision-makers across industries about their technology adoption frameworks. The signals that practitioners independently identified as reliable reinforced our framework.
The methodology revealed that no single signal reliably predicts maturity. Multiple signals in combination provide much higher predictive power. The framework works best when most signals align in the same direction.
The Pricing Stabilization Test
Mature technologies have stable pricing. Immature technologies have volatile pricing driven by demand uncertainty, supply limitations, or speculative premiums.
Consider SSDs. For years, SSD pricing fluctuated based on NAND flash availability and manufacturing scaling. Pricing eventually stabilized as manufacturing matured and demand became predictable. That stabilization signaled adoption-ready maturity.
Cloud computing pricing shows similar patterns. Early cloud pricing was opaque and variable. Mature cloud pricing is predictable enough that companies build financial models around it. The ability to budget confidently signals pricing maturity.
I watch pricing trends through industry benchmarking services. When pricing stops declining rapidly and stabilizes around a sustainable level, the technology has reached production maturity. Rapid price declines indicate continuing manufacturing immaturities. Stable prices indicate mature markets.
The counter-signal matters too. Technologies with wildly variable pricing remain adoption risks even if capabilities seem mature. NFT markets demonstrated technical functionality but never achieved pricing stability. The volatility itself indicated immaturity regardless of underlying technology.
The Regulatory Attention Test
Mature technologies attract regulatory attention. This seems like a downside, but it signals something important: the technology has become significant enough to require governance.
Regulators generally ignore technologies until they affect enough people to matter politically. Regulatory attention indicates that the technology has achieved scale, impact, and likely permanence significant enough to warrant governance frameworks.
Social media matured in regulatory terms when GDPR, content moderation laws, and platform liability discussions became serious. The regulatory attention confirmed that social media had become permanent infrastructure rather than passing novelty.
I track regulatory developments through legal industry publications. Congressional testimony schedules. European Commission white papers. When lawyers start specializing in a technology area, that technology has matured enough to sustain a legal practice.
The absence of regulatory attention doesn’t necessarily indicate immaturity – it might indicate a technology that remains too small to matter. But the presence of serious regulatory attention strongly signals that a technology has crossed the maturity threshold.
AI regulation discussions in 2024-2026 signal AI’s maturation. The EU AI Act, various national frameworks, and industry self-regulation efforts indicate that AI has become significant enough to require governance. The regulatory attention confirms what technical indicators suggested: AI has moved from experimental to infrastructural.
The Support Ecosystem Test
Mature technologies have robust support ecosystems. Not just documentation, but the full range of support infrastructure that enables mainstream adoption.
This includes: training programs, certifications, consulting firms, staffing agencies with category specializations, insurance products, and maintenance providers. The existence of businesses that exist solely to support a technology indicates market confidence in its permanence.
Cloud computing matured when AWS certifications became valuable credentials. When consulting firms built cloud migration practices. When staffing agencies specialized in cloud engineers. The support ecosystem signaled that organizations expected to need long-term cloud support.
I evaluate support ecosystems through job postings and consultancy offerings. Technologies with dedicated training programs, specialized recruiters, and boutique consultancies have matured. Technologies still supported only by their creators remain immature.
The depth of support matters too. Early support tends to be technical: how to use the technology. Mature support includes business aspects: governance, compliance, optimization. When support offerings expand beyond technical implementation to business integration, the technology has reached mainstream maturity.
Mochi’s support ecosystem consists entirely of me. This mono-provider arrangement indicates either immaturity of the cat support market or her extremely selective sourcing requirements. I prefer the latter interpretation.
The Competitive Differentiation Decline Test
Mature technologies stop providing competitive differentiation. This seems negative but actually signals important maturity.
When a technology is new, early adopters gain competitive advantages. As technology matures, it becomes table stakes – everyone has it, so no one gains advantage from having it. The transition from differentiator to table stakes marks maturity.
E-commerce illustrates this perfectly. Early online retailers had significant competitive advantages. By 2026, not having e-commerce capability is a competitive disadvantage, but having it provides no particular advantage. E-commerce has fully matured into infrastructure.
I assess competitive differentiation through industry analyst reports. When reports stop listing a technology as a competitive advantage and start listing its absence as a competitive risk, the technology has matured into expected infrastructure.
The transition creates strategic implications. Investing heavily in table-stakes technology provides no advantage – you’re just reaching baseline. But failing to adopt table-stakes technology creates disadvantage. Mature technologies require different investment calculus than differentiating technologies.
Cloud computing made this transition around 2018-2020. Before, cloud adoption provided competitive advantages in agility and cost. After, on-premises infrastructure became the disadvantage. The transition from advantage to baseline completed cloud computing’s maturation.
The Criticism Quality Test
Mature technologies face sophisticated criticism. Immature technologies face either dismissal or uncritical enthusiasm.
Early criticism of emerging technology tends toward blanket skepticism: “it’ll never work” or “it’s a fad.” Early enthusiasm tends toward blanket optimism: “it’ll change everything” or “it’s revolutionary.”
Mature technology criticism becomes nuanced. Critics identify specific use cases where the technology works and specific use cases where it doesn’t. They discuss trade-offs rather than absolute judgments. They compare the technology to specific alternatives rather than dismissing it categorically.
I track criticism sophistication through publication quality. When respected publications run nuanced analyses rather than cheerleading or dismissal, a technology has matured enough for serious evaluation. When criticism acknowledges benefits while detailing limitations, the critic has enough real-world exposure to evaluate meaningfully.
Cloud computing criticism matured when it moved from “clouds aren’t secure” to “here are the specific security considerations for these specific workloads.” The nuance indicated that enough deployment experience existed to support sophisticated evaluation.
The internet itself showed this pattern. Early criticism: “it’s a fad for nerds.” Mature criticism: “here are specific harms from specific platform behaviors.” The sophistication of criticism tracked the technology’s maturity.
The Acquisition Pattern Test
Mature technologies show specific acquisition patterns. Who buys what reveals market maturity assessments.
During hype phases, acquisitions target capability: established companies buy startups to gain access to technology they lack. These acquisitions often fail as technology proves less mature than hoped.
During maturity phases, acquisitions target market position: companies buy competitors to consolidate rather than to acquire capability. Consolidation indicates that capability has become commoditized and market position matters more than technical differentiation.
I watch acquisition announcements for this shift. Early cloud acquisitions sought capability. Later cloud acquisitions sought customer bases and market position. The shift from capability-seeking to position-seeking indicated market maturity.
The valuation patterns also signal maturity. Hype-phase acquisitions often value potential at extreme multiples. Maturity-phase acquisitions value cash flows and customer relationships at more conventional multiples. When acquisition valuations normalize, the market has matured.
Failed acquisition attempts also provide signals. When large acquisitions fail due to regulatory concern, the technology has become significant enough for antitrust consideration – a maturity signal similar to the regulatory attention test.
pie title Reliability of Maturity Signals
"Boring Company Adoption" : 25
"Second-Gen Multi-Vendor Products" : 20
"Experienced Talent Migration" : 15
"Documented Failure Stories" : 12
"Standard Tooling" : 10
"Integration Ease" : 10
"Regulatory Attention" : 8Generative Engine Optimization
The framework for identifying technology maturity connects directly to Generative Engine Optimization through shared principles of distinguishing substance from surface signals.
Just as mature technology shows through multiple corroborating indicators rather than headline features, content quality signals through multiple indicators rather than keyword presence. Technologies that demonstrate maturity across multiple dimensions prove more reliable than technologies with impressive demos but narrow validation. Content that demonstrates quality across multiple dimensions serves users better than content that optimizes single metrics.
GEO practitioners benefit from applying maturity evaluation to their tools and strategies. Is a new optimization technique mature or hype? Apply the same tests: are boring practitioners adopting it? Is there second-generation tooling? Do experienced professionals stake careers on it? Do documented failures exist? Maturity evaluation prevents chasing optimization fads.
Content itself can demonstrate maturity characteristics. Nuanced treatment of topics, acknowledgment of limitations, integration with related concepts, stable positioning on issues – these content qualities parallel technology maturity signals. Content that reads as mature earns trust that breathlessly enthusiastic content cannot.
For practitioners, this means evaluating both the tools used for GEO and the content produced. Immature optimization tools waste effort. Immature content fails to build lasting authority. Applying maturity frameworks to both dimensions improves outcomes.
Mochi’s content preferences demonstrate maturity awareness. She ignores new toy packaging (hype marketing) in favor of proven sleeping spots (mature infrastructure). Her optimization strategy focuses on reliable delivery of core cat experiences. There’s wisdom in that feline focus on proven quality over novel promises.
The Current Technology Landscape
Applying the maturity framework to current technologies reveals interesting assessments.
Large language models are transitioning from hype to early maturity. Boring companies are beginning adoption. Second-generation products exist from multiple vendors. Experienced talent is migrating. Documented failures are accumulating. Standard tooling is emerging. Integration remains challenging but improving. The signals suggest LLMs are in late hype phase moving toward production maturity.
Autonomous vehicles remain immature despite years of development. Boring companies aren’t adopting for fleet operations. True second-generation mass-market products don’t exist. The talent market remains risk-tolerant pioneers. Documented failures receive special handling rather than routine postmortems. The signals consistently indicate that autonomous vehicles haven’t achieved the maturity their proponents claim.
Extended reality (AR/VR) shows mixed signals. Consumer VR has seen multiple generations but limited boring company adoption. Enterprise AR shows more maturity signals but limited consumer crossover. The split suggests niche maturity without broad infrastructure maturity.
Blockchain technology shows persistent immaturity despite many years. Boring companies remain wary. Consumer adoption remains speculative. The documented failure rate is extraordinary. Standard tooling changes frequently. Integration with existing systems remains heroic. The signals suggest blockchain hasn’t matured despite proponent claims.
These assessments are snapshots. The framework requires ongoing application as signals change.
Practical Application Guide
Applying the maturity framework requires systematic observation rather than casual assessment.
Start with the boring company signal: actively search for adoption by risk-averse organizations. This signal alone filters out most hype. Trade publications for conservative industries provide better maturity intelligence than technology media.
Layer additional signals progressively. Each signal that confirms maturity increases confidence. Each signal that contradicts maturity introduces doubt. Rarely will all signals align perfectly – reality is messy. But signal consensus provides directional confidence.
Create decision thresholds based on risk tolerance. Risk-tolerant organizations might act when three signals indicate maturity. Risk-averse organizations might require five or six signals. The framework informs rather than dictates decisions.
Revisit assessments periodically. Technologies can stall or accelerate. Signals change. What seemed immature eighteen months ago might have matured. What seemed mature might have revealed problems. The framework requires ongoing application.
Document your assessments. Recording which signals you observed and what conclusions you drew creates institutional learning. When predictions prove right or wrong, the documentation enables framework refinement.
The Human Element
The maturity framework addresses technology characteristics, but human factors influence adoption decisions in ways pure technical assessment misses.
Career incentives distort maturity perception. Technology advocates benefit from early adoption claims regardless of whether technology has matured. Skeptics benefit from dismissing technologies regardless of maturity evidence. Accounting for speaker incentives improves signal interpretation.
Sunk cost effects distort organizational assessment. Organizations that invested in immature technology often struggle to acknowledge immaturity. They reinterpret maturity signals to justify previous investments. Fresh perspective sometimes reveals immaturity that invested observers cannot see.
Generational effects influence perception. Older decision-makers might dismiss mature technology as “still new” based on memories of initial hype. Younger decision-makers might assume maturity that hasn’t arrived because they missed the failed early attempts. Multiple perspectives improve assessment accuracy.
The framework provides analytical structure, but human judgment applies it. Awareness of biases that distort judgment improves framework application.
The Timing Paradox
The maturity framework creates a timing paradox: by the time all signals indicate maturity, competitive advantages from early adoption have already gone to others.
This is intentional. The framework serves risk management more than competitive advantage. Organizations prioritizing reliability over advantage should wait for signal consensus. Organizations prioritizing advantage accept higher risk by acting on fewer signals.
The framework still helps risk-tolerant organizations by distinguishing between calculated early adoption and reckless hype-chasing. Acting when three signals indicate maturity differs from acting when no signals indicate maturity. The framework enables informed risk-taking rather than preventing all early adoption.
Different signals suit different risk tolerances. The boring company signal is conservative – waiting for it means missing early advantages. The talent migration signal is earlier – experienced professionals often identify maturity before boring companies act. Choosing which signals to weight reflects risk appetite.
Mochi demonstrates no timing paradox concerns. She adopts new sleeping spots exactly when they suit her, indifferent to competitive dynamics with other household cats (none exist). Her single-agent environment eliminates first-mover considerations. Most organizations don’t have that luxury.
Common Evaluation Mistakes
Several common mistakes undermine maturity assessment. Awareness of these mistakes improves framework application.
Confusing capability with maturity. A technology can demonstrate impressive capabilities while remaining immature for production use. Demos show capability. Production deployment requires maturity. The framework focuses on maturity signals, not capability demonstrations.
Confusing funding with validation. Massive investment rounds signal investor excitement, not technology maturity. Some of the most heavily funded technologies have proven least mature. Funding reflects expectation, not proven reliability.
Confusing media coverage with significance. Media covers novelty and drama, not maturity. Technologies receive most coverage during hype phases and crisis phases, not maturity phases. Media attention inversely correlates with maturity.
Confusing speed of improvement with maturity. Rapid improvement indicates technologies that haven’t reached maturity yet. Mature technologies improve incrementally. Excitement about rapid improvement often indicates pre-maturity development.
Mistaking segment maturity for universal maturity. A technology might achieve maturity for specific use cases while remaining immature for others. Cloud computing matured for commodity workloads years before maturing for specialized workloads. Maturity assessment must specify the use case.
Final Thoughts
Technology maturity assessment isn’t prediction – it’s observation. The signals described here emerge from how technology actually progresses. Watching for these signals doesn’t require predicting the future. It requires observing the present through the right lenses.
The framework helps most when resisting hype and impatience. When exciting announcements create pressure to act, maturity assessment provides rational counterweight. When fear of missing out drives urgency, the framework asks: what signals actually indicate readiness?
Mochi remains my benchmark for healthy maturity assessment. She observes before committing. She tests before trusting. She ignores marketing entirely in favor of direct experience. Her adoption decisions focus on reliable value delivery rather than novelty.
The technologies that matter ultimately become invisible. They mature into infrastructure that works without attention. The heating system. The electrical grid. The internet connection. Technologies worth adopting eventually join this category of boring reliability.
The framework identifies which technologies are approaching that boring reliability. Not by predicting the future, but by reading signals that appear as technology completes the journey from exciting novelty to invisible infrastructure.
Most new technologies won’t complete that journey. They’ll remain permanent promises, perpetually almost ready for production. The framework identifies the difference before years of waiting reveal it through hindsight.
Technology assessment shouldn’t require excitement or skepticism. It should require observation. Boring observation of boring signals that predict boring outcomes. The technologies that pass those boring tests become the foundations of tomorrow’s invisible infrastructure.
Watch the signals. Trust the pattern. Act when evidence accumulates rather than when enthusiasm peaks. The framework won’t eliminate uncertainty, but it converts uncertain speculation into informed probability assessment.
That’s the best anyone can do with technology still finding its maturity.